https://blog.civicdatadesignlab.mit.eduhttps://blog.civicdatadesignlab.mit.edu/icons/icon-512x512.pnghttps://blog.civicdatadesignlab.mit.eduGatsbyJSMon, 31 Aug 2020 03:43:41 GMThttps://blog.civicdatadesignlab.mit.edu/who-cares-about-covid-19https://blog.civicdatadesignlab.mit.edu/who-cares-about-covid-19Sun, 30 Aug 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "WHO CARES about COVID-19?", "author": "Brian Williams", "date": "2020-08-30T00:00:00.000Z", "tags": ["Covid", "Data", "Health", "Race", "Visualization"], "hero": "images/pic-for-2nd-article-cares.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, mdx("strong", { parentName: "p" }, "Is Section 18115 really doing anything?")), mdx("p", null, "On June 4th, the U.S. Department of Health and Human Services (HHS) released new guidance requiring laboratories to include relevant demographic data, such as age and race, on every COVID-19 test. As specified in the Coronavirus Aid, Relief, and Economic Security (CARES) ActSection 18115,these changes went into effect on August 1st. But did anythingreallyhappen?"), mdx("p", null, "As a follow up to the", mdx("a", _extends({ parentName: "p" }, { "href": "https://blog.civicdatadesignlab.mit.edu/data-from-reported-covid-19-tests-are-telling-an-incomplete-story:-here's-what-you-need-to-know", "target": "_blank", "rel": "noreferrer" }), "previous article"), "on missing reported race data in COVID-19 tests nationwide, we wanted to see the significant effects (if any) of the new federal guidance passed in the CARES Act Section 18115. Previously, we found large gaps in the aggregate data across the United States. From this, we asked if anything could be done at individual laboratories or medical testing centers to compensate for the gap. Now, we seek to investigate the impact of the new federal regulation on reporting race data."), mdx("p", null, "Data Comparisons, Before and After 18115"), mdx("p", null, "Section 18115 may actually be impacting the level of reported race data nationwide\u2026 But is it enough?"), mdx("p", null, "From July 26th to August 26th, positive COVID-19 cases increased by over 1.5 million but only about 1 million had associated race data."), mdx("p", null, "In the United States, the percent of cases with associated race data has varied over time:"), mdx("ol", null, mdx("li", { parentName: "ol" }, "cumulative as of August 26th: 55.65%"), mdx("li", { parentName: "ol" }, "only between July 26th and August 26th: 66.10%"), mdx("li", { parentName: "ol" }, "cumulative as of July 26th: 51.75%")), mdx("p", null, "[[", "insert bar graph]", "](", mdx("a", _extends({ parentName: "p" }, { "href": "https://chart-studio.plotly.com/~brianwilliams2022/17.embed", "target": "_blank", "rel": "noreferrer" }), "https://chart-studio.plotly.com/~brianwilliams2022/17.embed"), ")"), mdx("p", null, "This visualization shows the difference of percents of cases where race data is known, per state. The number represented in each bar is the percent difference in cases with associated race between two different periods: fromJuly 26th to August 26thand from the beginning of data collection up until July 26th."), mdx("p", null, "For example, let\u2019s say that a given state\u2019s total cases rose from July to August by x, and of those cases, there is a subsection where race data is unknown. Let\u2019s say this subsection of unknown race data increases by y. I calculated 1 - (y/x), and compared that same calculation instead with the cumulative period from toward the beginning of the pandemic (late March) to July 26th."), mdx("p", null, "Some of these values are negative because the total number of Other and Unknown cases actuallyincreasesfrom July to August at a greater percentage compared to the beginning of the pandemic. This is pretty alarming."), mdx("p", null, "Please note that North Dakota (ND) seems to have a very large value: 87%. This is due to the fact that North Dakota only very recently started reporting race data foranyof their cases."), mdx("p", null, "But why?"), mdx("p", null, "This seems unusual but I think it can be explained by retroactive revisions to the data (either by individual laboratories or a state\u2019s Department of Public Health) which would move cases from these categories to their accurate racial category."), mdx("p", null, "For example, if a scientist was able to confirm a case or a set of cases belonged to certain demographic after the total number of cases had already been reported, you would simply move cases over to their respective categories. Maybe in certain situations, backlogs of case data prevent labs from sorting cases by demographic data before state deadlines but as time goes along, they are able to update their reported case data. This doesn\u2019t seem to happen in many states and the testing efficiency problem could easily be a bigger and more widespread problem than I am expressing/speculating here."), mdx("p", null, "Here\u2019s a regional breakdown of the same data."), mdx("p", null, "[[", "insert color map]", "](", mdx("a", _extends({ parentName: "p" }, { "href": "http://plotly.com/~brianwilliams2022/35.embed", "target": "_blank", "rel": "noreferrer" }), "http://plotly.com/~brianwilliams2022/35.embed"), ")"), mdx("p", null, "Discussion: Almost too little, too late"), mdx("p", null, "So far, the national average of cases with race data increased by about 15% directly following the period Section 18115 went into effect, compared to reported race averages during the rest of the pandemic."), mdx("p", null, "Is this difference big enough? And how much of it can be directly attributed to Section 18115? To be quite honest\u2026 I\u2019m not sure."), mdx("p", null, "Though somewhat significant, the impact is almost too little too late. Just imagine what we\u2019re not seeing, and the things we\u2019ve ", "*", "already", "*", " missed. Before August, there were more than 2 million Covid-19 tests that were unidentifiable by race. This type of federal guidance should have been in place since February and at the latest, the beginning of March."), mdx("p", null, "It doesn\u2019t seem like we will have access to complete, accurate, and thorough data sets into the foreseeable future for many reasons starting at the laboratory level stretching all the way to the federal level. I just hope policymakers, publicly obligated to support our communities, are doing their best trying to walk in the dark during this pandemic."), mdx("p", null, "Sources"), mdx("ol", null, mdx("li", { parentName: "ol" }, mdx("a", _extends({ parentName: "li" }, { "href": "https://www.hhs.gov/sites/default/files/covid-19-laboratory-data-reporting-guidance.pdf", "target": "_blank", "rel": "noreferrer" }), "https://www.hhs.gov/sites/default/files/covid-19-laboratory-data-reporting-guidance.pdf")), mdx("li", { parentName: "ol" }, mdx("a", _extends({ parentName: "li" }, { "href": "https://www.cdc.gov/coronavirus/2019-ncov/lab/reporting-lab-data.html#what-to-include", "target": "_blank", "rel": "noreferrer" }), "https://www.cdc.gov/coronavirus/2019-ncov/lab/reporting-lab-data.html#what-to-include")))); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/h2a:-seeing-sketchiness-in-datahttps://blog.civicdatadesignlab.mit.edu/h2a:-seeing-sketchiness-in-dataSat, 22 Aug 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "H2A: Seeing Sketchiness in Data", "author": "Evan Denmark", "date": "2020-08-22T00:00:00.000Z", "excerpt": "In part two, we examine how the missing data associated with H2A hints at potentially nefarious behavior. ", "tags": ["Economy", "Data"], "hero": "images/totalinstances.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "The H2A Visa program has become ", mdx("a", _extends({ parentName: "p" }, { "href": "https://blog.civicdatadesignlab.mit.edu/america's-essential-yet-unknown-program:-h2a", "target": "_blank", "rel": "noreferrer" }), "increasingly essential"), " to the American food supply chain. Since 2012, the number of participating farms, visa applications, worker hours, and total expense of the program have risen significantly. However, it hasn\u2019t all been good. "), mdx("p", null, "\u201CWith the explosion in usage of the H2A program \u2026 we\u2019ve seen the abuse of the program grow,\u201D said Caitlin Ryland, the Supervising Attorney at the Legal Aid of North Carolina, a nonprofit law firm dedicated to equal access to justice for low income workers."), mdx("p", null, "Farmers employing foreign workers through the H2A program must abide by strict ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.dol.gov/agencies/whd/agriculture", "target": "_blank", "rel": "noreferrer" }), "labor laws"), " to ensure that their workers are treated fairly and paid appropriately. However, ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.buzzfeednews.com/article/kenbensinger/the-pushovers", "target": "_blank", "rel": "noreferrer" }), "recent reports"), " have shown the potential for worker abuse in the H2A program. Inspired by these multiple reports, we decided to investigate the data surrounding the violations in the H2A program."), mdx("p", null, "According to the US Department of Labor (DOL) Wage and Hour Division\u2019s public data on enforcement, the number of federal H2A violations has dramatically increased in the past two decades. H2A violations may include improper housing requirements, poor working conditions, or improper payment of workers. The data displayed in the graph below shows the number of H2A violations since 2000. In this data, a list of farms that have violated H2A policy, the number of violations per investigation, and the duration of the violations are all provided."), mdx("p", null, mdx("img", _extends({ parentName: "p" }, { "src": "https://lh6.googleusercontent.com/0ARiQGoswpnFFuxDilxkwkZzERn3aVo5136q60ljyl3PmjE3K2w6-Ur-3e2akwff0eHQmp7eQ80LNApi5MgIM4oDAHQ_I-o7ouPNSWJ-TYK-XG9A9jWJCbTM0nAvQgztjkdrep4y", "alt": null })), mdx("em", { parentName: "p" }, "Data provided by the US Department of Labor Wage and Hour Division\u2019s WHISARD data, updated in July 2020.")), mdx("p", null, "This increase in violations comes from an increased reporting of violations in addition to a lack of government oversight of the program."), mdx("p", null, "\u201CMost of ", "[", "DOL\u2019s] inspections are complaint driven,\u201D says Ryland, which means that many farms may be committing H2A violations that are not represented in the data."), mdx("p", null, "Despite the growth of the program, the multiple agencies that enforce the H2A laws are significantly lacking. According to Ryland, the North Carolina Department of Labor has only \u201Cfive or six inspectors for about two thousand labor camp sites.\u201D"), mdx("p", null, "To make matters even more complicated, Rylands believes that during COVID, they\u2019re not \u201Cdoing any onsite investigations.\u201D"), mdx("p", null, "Notably, the data displayed shows a significant decrease in violations in recent years. One possible reason is that it can take time to investigate and confirm a set of violations. Ryland said that workers \u201Ctypically don\u2019t learn about the results of their complaint for about a two year period,\u201D which renders the visualization incomplete."), mdx("p", null, "The lack of government oversight also points to serious questions in the H2A application procedure. In addition to the violation enforcement data, the Department of Labor Employment and Training Administration also publishes the H2A visa applications of each farm. By combining these two datasets, there are some jarring artifacts."), mdx("p", null, "For example, Lewis M. Bailey IV Farms, a Mississippi potato grower, had a total of 180 accepted visa applications in early 2019 to work the fields until November. However, by the end of June, the DOL confirmed 178 violations on the Bailey Farm, resulting in over $325,000 in worker backwages (i.e. over 1 month of pay for 178 workers)."), mdx("p", null, "Despite a violation of this magnitude, the DOL approved 230 H2A applications to the Bailey Farm for the August to November harvest on July 9 - only three weeks after the end of the major violation."), mdx("p", null, "Although the data cannot tell us the full story, it does point to potentially glaring missteps by the Department of Labor. The fact that a farm like Bailey Farm can be approved even after a large violation questions forces us to not only question the DOL\u2019s application process but also to wonder how farms respond to violations. That is, after a violation, do farms respect the authority of the DOL?"), mdx("p", null, "Of all of the farms who applied for H2A applications in 2019, 2.25% had previously violated H2A policy. With this in mind, we wondered if farms who had previously violated H2A policy tended to violate again. As shown in the below graph, we examined the recidivism - the tendency to reoffend - of farms who have broken H2A protocols at least three times in the last two decades."), mdx("p", null, mdx("img", _extends({ parentName: "p" }, { "src": "https://lh4.googleusercontent.com/bqpd3z76Dk0tV2ZlocL9GYSUW_LcmuUu5b0g7mL5B2dQcbZkAst-QU2rqzPlGlwoKgwYXFdedXcZ2Fz2MDI3m8FYQ_JzSNTxCx23rMlM37b12y8dHCIHs-tjusoDfylKm3FZcp2l", "alt": null }))), mdx("p", null, "From this data, those multi-violating offenders tended to not offend nearly as bad as before."), mdx("p", null, "In addition to potentially sketchy farms, some law firms (who are contracted by farms to apply for H2A visas) stick out as outliers when examining their rates of violation. For example, over 35% of the nearly 4,000 visas applied for by Farmer, Farmer, & Brown PLLC - a firm based out of Georgia - were contracted by farms who had previously violated H2A policy. Why is is that some law firms are associated with this sketchy behavior? "), mdx("p", null, "As with many data projects, the data is only a single source and in order to understand the full story, deeper investigations must be done. However, these initial investigations show that data can hint at a larger behavior, including a lack of government oversight, a lack of violation reporting, and potentially nefarious behavior by both farms and law firms."), mdx("p", null, "What is evident, however, is the amount of missing data within this realm, and because of the magnitude of it, it can be difficult to grasp the scale of the worker abuse problem by the data alone.")); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/covid-19-open-spaces:-numbers-and-reality-in-nychttps://blog.civicdatadesignlab.mit.edu/covid-19-open-spaces:-numbers-and-reality-in-nycFri, 14 Aug 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "COVID-19 Open Spaces: Numbers and Reality in NYC", "author": "Laura Kim", "date": "2020-08-14T00:00:00.000Z", "tags": ["Covid", "Mapping"], "hero": "images/maps2.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "Access to open space is an urban luxury. Cities\u2019 makeup prioritizes buildings rather than vast, green space in order to ensure density. But during a worldwide pandemic with shelter-in-place mandates and business closures, access to open space is a necessity for public health."), mdx("p", null, "By early April, 45 states in the U.S. had ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.nbcnews.com/health/health-news/here-are-stay-home-orders-across-country-n1168736", "target": "_blank", "rel": "noreferrer" }), "stay-at-home orders"), " of varying degrees. Streets emptied, public transportation ridership decreased, and overall traffic disappeared revealing smog-less skies. The activity-void landscape from afar gave people, especially urbanists, the opportunity to rethink urban life and envision ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.nytimes.com/2020/07/09/opinion/sunday/ban-cars-manhattan-cities.html", "target": "_blank", "rel": "noreferrer" }), "car-less, walkable cities"), ". Streets became the center of attention and a canvas for new, urgent ideas. But on the ground, there were people in desperate need of space \u2014 to walk socially distant from strangers, to exercise, and to be. And in June, Black Lives Matter movements and protests against racism and police violence filling up the empty streets brought a crucial point into the conversation: equity."), mdx("p", null, "Inequitable distribution of parks and public space is not news. \u201CThe average park size is 6.4 acres in poor neighborhoods, compared with 14 acres in wealthy neighborhoods, according to an analysis by the ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.nytimes.com/2020/07/15/nyregion/nyc-parks-access-governors-island.html", "target": "_blank", "rel": "noreferrer" }), "Trust for Public Land."), "\u201D Using quantitative and qualitative data, this post shows how open space distribution in NYC fared during the pandemic."), mdx("p", null, mdx("strong", { parentName: "p" }, "How much space is open in NYC?")), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "2480px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "141.45161290322582%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/2cafccf172bf6e75e82d9fec9d8de529/8e64d/map.webp 2480w"], "sizes": "(max-width: 2480px) 100vw, 2480px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/2cafccf172bf6e75e82d9fec9d8de529/8c5c5/map.png 2480w"], "sizes": "(max-width: 2480px) 100vw, 2480px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/2cafccf172bf6e75e82d9fec9d8de529/8c5c5/map.png", "alt": "Map of all open spaces in NYC", "title": "Map of all open spaces in NYC before the pandemic", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "New York City, on a pre-pandemic day, would have approximately 33,630 acres of open space, which is around 16% of total city land. Streets are considered as open spaces as well, adding 8,137 miles. What counts as open space in this map:"), mdx("ul", null, mdx("li", { parentName: "ul" }, "Parks operated by Department of Parks and Recreation"), mdx("li", { parentName: "ul" }, "Recreational Areas not designated as NYC Parks"), mdx("li", { parentName: "ul" }, "Department of Transportation Pedestrian Plazas"), mdx("li", { parentName: "ul" }, "Cemeteries"), mdx("li", { parentName: "ul" }, "Streets")), mdx("p", null, "I use figure ground maps, which are used to show the relationship between the built environment and open space, to illustrate which spaces were open or closed and how openness evolved during the course of the past few months."), mdx("h4", { "id": "how-much-was-closed-in-april" }, "How much was closed in April?"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "2480px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "141.45161290322582%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/2e04eedfc196513775cc45ae59a2d86f/8e64d/maps2.webp 2480w"], "sizes": "(max-width: 2480px) 100vw, 2480px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/2e04eedfc196513775cc45ae59a2d86f/8c5c5/maps2.png 2480w"], "sizes": "(max-width: 2480px) 100vw, 2480px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/2e04eedfc196513775cc45ae59a2d86f/8c5c5/maps2.png", "alt": "Map with highlights of closed spaces in April", "title": "Map of closed spaces during April lockdown", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "Effective at 8pm on Sunday, March 22, all non-essential businesses in New York state were ordered to close along with parks with any recreational features such as playgrounds, basketball courts, and beaches. Cemeteries remained open, but closed to visitors. This meant around 30% of NYC\u2019s open spaces were not accessible\u2014this equates to around 12 Central Parks being closed. These closed spaces are marked red in the above map."), mdx("p", null, "When Covid-19 hospitalization rates were at its ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www1.nyc.gov/site/doh/covid/covid-19-data.page", "target": "_blank", "rel": "noreferrer" }), "peak"), " on 3/30 with 6,136 cases in NYC, limiting access to spaces where there are chances of people congregating in large numbers was the right move. What this closure strategy surfaced is the existing inequities in open space distribution in NYC."), mdx("p", null, "Even from a quick glance, you can see the unequal distribution of large open spaces, which would enable proper social distancing. Each borough has one or two large parks, such as the Bronx Park and Central Park, but this means the majority of NYC residents rely on small parks in their neighborhoods for walkable open space access. And during a lockdown, with decreased public transportation use, people need safe spaces that they can walk to more than ever."), mdx("p", null, "A ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.arcgis.com/apps/webappviewer/index.html?id=4d082c62efb44e56b105366fb92335b3&extent=-8287910.233%2C4941135.2385%2C-8185178.8669%2C4998463.0097%2C102100", "target": "_blank", "rel": "noreferrer" }), "map"), " from the Trust for Public Land shows park needs in NYC during the summer of 2020, determined by access to an open park within a ten-minute walk of home. Taking findings from this map, I zoomed into a neighborhood that has a high need for parks this summer to see what the change looked like. Elmhurst in Queens saw a 64% decrease in neighborhood open space in April. And with social distancing, the remaining open space were most likely used at a lower capacity."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "2332px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "76.45797598627787%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/e3764acb5faaff3af9ba942d12d89fb7/ad528/elm-copy.webp 2332w"], "sizes": "(max-width: 2332px) 100vw, 2332px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/e3764acb5faaff3af9ba942d12d89fb7/a90b0/elm-copy.png 2332w"], "sizes": "(max-width: 2332px) 100vw, 2332px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/e3764acb5faaff3af9ba942d12d89fb7/a90b0/elm-copy.png", "alt": "Elmhurst, Queens", "title": "Open and closed spaces in Elmhurst, Queens in April", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("h4", { "id": "what-about-privately-owned-public-spacespops" }, mdx("strong", { parentName: "h4" }, "What about Privately Owned Public Spaces(POPS)?")), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "2480px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "141.45161290322582%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": 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"https://www.nytimes.com/2017/04/19/nyregion/public-space-trump-tower.html", "target": "_blank", "rel": "noreferrer" }), "controversial"), ") addition to public space in NYC, with the first initiatives dating to the 1960s. Some are plazas in front of buildings like the Seagram building and some are parks with green space such as the Domino Park in Williamsburg. POPS are a way for the city to create more public space within private development. The map shows that the majority distribution is in Manhattan, in areas with already walkable access to Central Park or Bryant Park. It was also unclear what was open during the lockdown because unlike city parks, there was no direct way to find information about whether they were open."), mdx("p", null, mdx("strong", { parentName: "p" }, "\u201COpen\u201D Streets")), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "2480px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "141.45161290322582%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/101eeaa3a5726b026270b2ec5157b07b/8e64d/maps3.webp 2480w"], "sizes": "(max-width: 2480px) 100vw, 2480px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/101eeaa3a5726b026270b2ec5157b07b/8c5c5/maps3.png 2480w"], "sizes": "(max-width: 2480px) 100vw, 2480px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/101eeaa3a5726b026270b2ec5157b07b/8c5c5/maps3.png", "alt": "Open Streets in NYC", "title": "Open Street Locations in NYC", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "A popular pandemic response to inadequate space for social distancing and outdoor activities is Open Streets. Oakland, Milan, and many other cities have ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.archdaily.com/938202/people-to-reclaim-streets-in-milan-in-post-covid-19-vision-of-the-city", "target": "_blank", "rel": "noreferrer" }), "adopted similar strategies"), ". Open Streets in NYC are pedestrian-friendly streets that are blocked off by NYPD barricades during daytime hours to allow for social distancing while walking and to use as pseudo-park space. A total of 67 miles have been opened up (or closed depending on how you are using the street) to date, shown as white lines in the map. This effort is combined with Open Restaurants as the city is reopening and allowing outdoor dining using parking space and sidewalk space."), mdx("p", null, "What the data shows on the map allows for possibilities of car-less streets and brings memories of ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www1.nyc.gov/html/dot/summerstreets/html/home/home.shtml", "target": "_blank", "rel": "noreferrer" }), "Summer Streets"), ". (Summer Streets have been cancelled for 2020.) However, what the data misses is how open these designated streets are used in reality. First, there is critique about how the process", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.nytimes.com/2020/07/20/upshot/pandemic-city-planning-inequality.html", "target": "_blank", "rel": "noreferrer" }), " lacks public participation"), ". 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This picture shows an empty Berry Street in Williamsburg on July 3rd, Friday at 3pm. Without much tree coverage on a hot summer day, this long strip of Open Streets is not a desirable space to spend time in."), mdx("p", null, "And now with businesses using the street and increased vehicle traffic compared to April, people in need of open space are being pushed once again to share the street."), mdx("h4", { "id": "the-silver-lining" }, "The Silver Lining"), mdx("p", null, "Covid-19, of its many impacts, has taken away the joys of city living and accentuated the difficult experiences. But New Yorkers are resilient and creative. Stoop birthday parties, sidewalk ceramic studios, parklet dining color coded with the restaurant\u2019s sign, and streets as space to demand justice. Moreover, the increased attention on reevaluating public spaces offers hope towards a healthier, more equitable, safer \u2014 livable for all \u2014 city."), mdx("p", null, mdx("strong", { parentName: "p" }, mdx("iframe", { width: "100%", height: "520", frameBorder: "0", src: "https://mit.carto.com/u/laurapk/builder/e37c169c-c929-4de6-a8c5-a19f94af5319/embed", allowFullScreen: true, webkitallowfullscreen: true, mozallowfullscreen: true, oallowfullscreen: true, msallowfullscreen: true }))), mdx("h4", { "id": "data-sources" }, mdx("strong", { parentName: "h4" }, "Data Sources")), mdx("p", null, "Department of Information Technology & Telecommunications, Open Space (Parks), 2018."), mdx("p", null, "Department of Information Technology & Telecommunications, Open Space (Other), 2018."), mdx("p", null, "Department of Transportation, NYC DOT Pedestrian Plazas, 2020."), mdx("p", null, "Department of Transportation, Open Streets Locations, 2020. "), mdx("p", null, "Department of Parks and Recreation, Beaches, 2018."), mdx("p", null, "Department of City Planning, Privately Owned Public Space Database, 2018.")); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/statewide-eviction-protections-visualizedhttps://blog.civicdatadesignlab.mit.edu/statewide-eviction-protections-visualizedFri, 07 Aug 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "Statewide Eviction Protections, Visualized", "author": "Joyce Zhao", "date": "2020-08-07T00:00:00.000Z", "tags": ["Covid", "Data"], "hero": "images/evictionrates.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "The eviction moratorium under the Federal CARES Act came to an end recently on July 25, and many state-wide eviction protections are quickly following. Many are worried that renters in America could be facing an unprecedented eviction crisis, with some estimating up to ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.cnbc.com/2020/07/30/what-its-like-to-be-evicted-during-the-coivd-19-pandemic.html", "target": "_blank", "rel": "noreferrer" }), "40 million renters"), " at risk of eviction once protections end. Described using terms such as \u201Ctsunami wave\u201D or \u201Cavalanche\u201D, this predicted eviction crisis has been met with varying policies across the country."), mdx("p", null, "Evictions themselves are a public health issue. Like many of the pre-existing inequalities magnified by the COVID-19 pandemic, evictions tend to affect ", mdx("a", _extends({ parentName: "p" }, { "href": "https://thehill.com/blogs/congress-blog/politics/508897-a-wave-of-mass-evictions-is-inevitable-and-black-women-will-be", "target": "_blank", "rel": "noreferrer" }), "communities of color"), ", and often low-income neighborhoods, the most. Furthermore, those threatened with eviction often face ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.bu.edu/sph/2018/10/05/the-hidden-health-crisis-of-eviction/", "target": "_blank", "rel": "noreferrer" }), "adverse, long-term health outcomes"), ". Evicted tenants also face housing instability that can lead to overcrowded households and homelessness, all which put people at greater risk of COVID-19."), mdx("p", null, "The graph below ranks each state by eviction rate (the number of evictions per 100 rental homes in 2016) and colors each state based on the status of their COVID eviction protection policies. The word \u201Cprotections\u201D emphasizes the differences in policy strength between legislations. Some states have true eviction moratoriums with a grace period for rent payment, some only prevent new eviction filings, while others only have orders to prioritize essential court proceedings. I\u2019ve limited the scope of housing policies in this visualization to those that explicitly protect tenants from eviction and are executed state-wide. The pale green color indicates whether or not these policies have expired as of July 31, 2020. The last five states in the graph had no available eviction rates."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "954px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "119.916142557652%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/e5ee9357039050751fc709a1d735f2fd/c19f8/evictionrates.webp 954w"], "sizes": "(max-width: 954px) 100vw, 954px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/e5ee9357039050751fc709a1d735f2fd/f8dd2/evictionrates.png 954w"], "sizes": "(max-width: 954px) 100vw, 954px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/e5ee9357039050751fc709a1d735f2fd/f8dd2/evictionrates.png", "alt": "Eviction Rates and COVID Eviction Protections", "title": "Eviction Rates and COVID Eviction Protections", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "What is first noticeable about this graph is the sheer number of states with expired eviction protections. As of July 31, only 20 states had existing eviction protections. Of the states with no available eviction rate data, only New Jersey currently has eviction protections; North Dakota and Alaska protections have expired, while South Dakota and Arkansas did not have state-wide protections. The constant line represents the 2.34% eviction rate of the United States in 2016. Interestingly, the four states with no state-wide COVID eviction protections all have higher rates than the national average."), mdx("p", null, "How long do these protections last? This next visualization examines the timeline during which these eviction protections exist. The color of each date represents the number of states with eviction protections in place on that date."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1275px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "129.41176470588235%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/0998c3136274d37a8476b494f2a10539/7f2e2/eviction-moratoria-data-duration-heatmap-1-.webp 1275w"], "sizes": "(max-width: 1275px) 100vw, 1275px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/0998c3136274d37a8476b494f2a10539/e70d7/eviction-moratoria-data-duration-heatmap-1-.png 1275w"], "sizes": "(max-width: 1275px) 100vw, 1275px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/0998c3136274d37a8476b494f2a10539/e70d7/eviction-moratoria-data-duration-heatmap-1-.png", "alt": "eviction moratoria data duration heatmap 1 ", "title": "eviction moratoria data duration heatmap 1 ", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "On March 13, Massachusetts and North Carolina became the first states to enact eviction-related policies. A month later on April 13, all 44 states with eviction protections had enacted them. We can see states begin to pass eviction protections most rapidly during the third week of March, with many of the executive orders announcing states of emergencies signed by governors during this week including these housing protections. Gradually, we can see these protections begin to expire. When the CARES Act eviction moratorium expired on July 25, there were 23 states with ongoing protections. Four states (Delaware, Kentucky, New Jersey, and New Mexico) have protections that expire with their state\u2019s state of emergency orders, rather than at the end of a given duration."), mdx("p", null, "While it is difficult to judge the effectiveness of these policies on duration alone, observing the majority of these protections end throughout the summer could offer an explanation to the coming eviction wave that many predict. The average duration of an eviction protection policy among states is 94 days. At the end of June, around half of the states had remaining eviction protections, while the national unemployment rates remained around 11%."), mdx("p", null, "Some of the current policies include a grace period to pay back missed rent, but none offer rent forgiveness. Based on unemployment statistics and the Census Bureau\u2019s ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.census.gov/programs-surveys/household-pulse-survey.html", "target": "_blank", "rel": "noreferrer" }), "Household Pulse Survey"), ", researchers have created projections on what the ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.cnbc.com/2020/07/27/how-the-eviction-crisis-will-impact-each-state.html?__source=twitter%7Cmain", "target": "_blank", "rel": "noreferrer" }), "coming month of evictions"), " could look like for renters across the country, with some states predicted to be hit harder than others. It is worth noting that eviction protections also differ across counties and cities. This leaves tenants navigating a patchwork of protections with various strengths and expiration dates."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "630px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "73.49206349206348%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/716476c233d445cb22ca3653a2dcb2cd/d869c/106627776-1595444094848-20200721_nova__table_of_eviction_forecast_metrics.webp 630w"], "sizes": "(max-width: 630px) 100vw, 630px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/716476c233d445cb22ca3653a2dcb2cd/48f8e/106627776-1595444094848-20200721_nova__table_of_eviction_forecast_metrics.png 630w"], "sizes": "(max-width: 630px) 100vw, 630px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/716476c233d445cb22ca3653a2dcb2cd/48f8e/106627776-1595444094848-20200721_nova__table_of_eviction_forecast_metrics.png", "alt": "106627776 1595444094848 20200721 nova table of eviction forecast metrics", "title": "106627776 1595444094848 20200721 nova table of eviction forecast metrics", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "As these state-wide eviction moratoriums expire, some are questioning where the responsibility for protecting tenants lies. Many believe that it is up to Congress to enact stronger, ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.nytimes.com/2020/07/23/opinion/coronavirus-evictions-rent.html", "target": "_blank", "rel": "noreferrer" }), "nation-wide eviction moratoriums"), ", while reports of ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.latimes.com/homeless-housing/story/2020-06-18/despite-protections-landlords-attempting-to-evict-tenants-in-south-l-a-black-and-latino-neighborhoods-data-shows", "target": "_blank", "rel": "noreferrer" }), "illegal self-help evictions"), " are leading tenants and activist groups to defend themselves. Like many other COVID-19 related policies, such as mask mandates and economic reopening, housing protections across the country do not often align in timing, strength, or jurisdiction."), mdx("p", null, "In this case, we might not see the effects of these eviction protections for many months, as courts gradually reopen and summons proceed. For more information on how specific cities are witnessing evictions during the pandemic, check out the ", mdx("a", _extends({ parentName: "p" }, { "href": "https://evictionlab.org/eviction-tracking/", "target": "_blank", "rel": "noreferrer" }), "COVID-19 Eviction Tracking tool"), " created by Princeton\u2019s Eviction Lab.")); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/h2a:-america's-essential-yet-unknown-programhttps://blog.civicdatadesignlab.mit.edu/h2a:-america's-essential-yet-unknown-programFri, 31 Jul 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "H2A: America's Essential yet Unknown Program", "author": "Evan Denmark", "date": "2020-07-31T00:00:00.000Z", "excerpt": "The first of many posts that dig into America's H2A visa program, how our food supply chain depends on it, and how data can show its potential flaws, especially in the midst of a pandemic. ", "tags": ["Covid", "Visualization", "Economy"], "hero": "images/screen-shot-2020-07-28-at-10.16.52-am.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "The Mississippi sweet potato planting season recently came to a close. Mike Williamson, a farmer of Williamson Family Farms in Water Valley, Mississippi, only hopes that all he\u2019ll have the workforce to harvest those crops come October. "), mdx("p", null, "\u201CI\u2019m not one hundred percent sure that my amigos will be here in the fall. If not, I\u2019ll be out of business,\u201D Williamson said."), mdx("p", null, "The \u201Camigos\u201D he refers to are his farm workers from Mexico, specifically those who were issued H2A visas\u2014one of the only visas that has not been suspended per a ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.whitehouse.gov/presidential-actions/proclamation-suspending-entry-aliens-present-risk-u-s-labor-market-following-coronavirus-outbreak/", "target": "_blank", "rel": "noreferrer" }), "June 22 Presidential Proclamation"), " because it is \u201Cessential to the food supply chain.\u201D"), mdx("p", null, "For the last seven years, Williamson has hired H2A visa workers to plant and harvest his crop. This year, he requested 28 workers, but due to personal fears of COVID-19, only 25 made the trip. Fortunately for Williamson, all of his workers are in the 90% of visa holders who have previously held an H2A visa, which means that their visa processing time was streamlined."), mdx("p", null, "In a pre-pandemic time, H2A visa applicants - 92% of whom are from Mexico - would undergo a personal interview at a US Consulate to get their visa approved. This year, the Department of State gave Consulates the option to ", mdx("a", _extends({ parentName: "p" }, { "href": "https://travel.state.gov/content/travel/en/News/visas-news/important-announcement-on-h2-visas.html", "target": "_blank", "rel": "noreferrer" }), "cancel the interview"), " for both new and returning visa applicants on March 26."), mdx("p", null, "However, of the 10% of applicants who are new to H2A, up to \u201Cforty percent\u201D of applications could be put on hold, according to Todd Miller of Head Honchos LLC, the San Antonio-based \u201Cseasonal labor processing work permit specialist\u201D firm hired by Williamson to recruit workers and navigate the complex visa process."), mdx("p", null, "\u201CWhen the Consulate finally opens up, it\u2019s going to take 100 years to talk to ", "[", "applicants] and get them visas,\u201D Miller said with hyperbole."), mdx("p", null, "Because of the lack of interviews, Miller said that if there is anything imperfect about an application, it could be put on hold. However, as he noted, \u201Cthere is no hard rule that ", "[", "applications] have to be held back\u201D because \u201Cit depends on the Consulate staff.\u201D"), mdx("p", null, "A world with COVID-19 is also presenting financial obstacles for farmers. Allen Robison, an apple and cherry farmer in Chelan, Washington, said that he is taking daily temperatures of his employees and spending \u201Cthousands of dollars\u201D to buy masks and other personal protective equipment."), mdx("p", null, "Despite the logistical hurdles and financial expense caused by the pandemic, the US Department of State and farmers across America are making sure that most H2A visa applicants are able to make it to their final destination. Their efforts speak to the fact that the H2A program is increasingly essential to America\u2019s food supply chain."), mdx("p", null, "H2A visa holders carried 9% of the country\u2019s agricultural labor budget in 2019, a number that is up from 6% in 2016 and that has been steadily growing in the past decade."), mdx("p", null, "Created in 1952 (and modified in 1986), the H2A program allows US-based farmers and agricultural firms to hire foreign workers when \u201Cthere are not sufficient ", "[", "American] workers who are able, willing, qualified, and available, and that the employment of aliens will not adversely affect the wages and working conditions of workers similarly employed in the U.S.\u201D"), mdx("p", null, "Despite posting the positions in newspapers and on job boards in multiple states, Mike Williamson said that he has not received \u201Ca single American application\u201D in the last seven years."), mdx("p", null, "Without an American agriculture workforce, the demand for visa-issued foreign workers has increased but comes at no small expense for farmers. In addition to paying employees their hourly wage, farmers are required to pay for worker housing, which must abide by a strict code, transportation between the farm and the foreign Consulate, and other visa related fees."), mdx("p", null, "Despite the expense, the program has seen a steady rise in the past decade. One decade ago, the US Department of Labor would consistently issue fewer than 75,000 visas each year. Today, that number has nearly tripled with more than 205,000 visas issued in 2019."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "3992px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "70.08%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/f674fbed69da5660608b085f0fac93bb/a3bc4/screen-shot-2020-07-27-at-2.36.51-pm.webp 2500w", "/static/f674fbed69da5660608b085f0fac93bb/2ba8c/screen-shot-2020-07-27-at-2.36.51-pm.webp 3992w"], "sizes": "(max-width: 3992px) 100vw, 3992px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/f674fbed69da5660608b085f0fac93bb/412e4/screen-shot-2020-07-27-at-2.36.51-pm.png 2500w", "/static/f674fbed69da5660608b085f0fac93bb/0ff6a/screen-shot-2020-07-27-at-2.36.51-pm.png 3992w"], "sizes": "(max-width: 3992px) 100vw, 3992px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/f674fbed69da5660608b085f0fac93bb/0ff6a/screen-shot-2020-07-27-at-2.36.51-pm.png", "alt": "The H2A program has dramatically increased during both Obama and Trump's tenure in the White House. Source: U.S. Department of State, Bureau of Consular Affairs, “Nonimmigrant Visa Statistics\"Source: ", "title": "The H2A program has dramatically increased during both Obama and Trump's tenure in the White House. Source: U.S. Department of State, Bureau of Consular Affairs, “Nonimmigrant Visa Statistics\"Source: ", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "Since 2016 (the year when the US DOL began producing data with consistent formatting), the labor costs of the H2A program have increased by 66% due to a rise in the amount of labor being performed and significant increases in the ", mdx("a", _extends({ parentName: "p" }, { "href": "http://www.mobilefarmware.com/support/wams/aewr/", "target": "_blank", "rel": "noreferrer" }), "adverse wage"), " - a state-by-state minimum wage to ensure that guest worker wages will not depress the wages of domestic workers in similar occupations. In some states, like Washington, the adverse wage has increased by 24% since 2016."), mdx("p", null, "Additionally, while the number of farms participating in H2A increased by 28%, the total person-hours done by H2A workers increased by 43% and the number of certified jobs by 45%, indicating both that new farms began participating in the program and that returning farms required more farm labor."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "3031px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "64.88%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/d1ad5efd2efeae49433a0e27dbdd71c6/a3bc4/2016v2020.webp 2500w", "/static/d1ad5efd2efeae49433a0e27dbdd71c6/f2528/2016v2020.webp 3031w"], "sizes": "(max-width: 3031px) 100vw, 3031px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/d1ad5efd2efeae49433a0e27dbdd71c6/412e4/2016v2020.png 2500w", "/static/d1ad5efd2efeae49433a0e27dbdd71c6/6fab7/2016v2020.png 3031w"], "sizes": "(max-width: 3031px) 100vw, 3031px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/d1ad5efd2efeae49433a0e27dbdd71c6/6fab7/2016v2020.png", "alt": "Source: U.S. Department of Labor, Employment and Training Administration, “H2A Disclosure Data” for years 2016-2019", "title": "Source: U.S. Department of Labor, Employment and Training Administration, “H2A Disclosure Data” for years 2016-2019", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "The use and expense of the H2A program have been steadily increasing, yet most Americans are completely unaware of its existence. Our food is the foundation of our health as individuals and, by extension, our society. Without an understanding of the farm to table process, the American food supply chain and agricultural economy at large becomes less resilient to obstacles\u2026 like a pandemic."), mdx("p", null, "Thus, this post is the first of many CDDL posts which will explore the H2A visa program, its value in America\u2019s supply chain, and its ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.nbcnews.com/specials/h2a-visa-program-for-farmworkers-surging-under-trump-and-labor-violations/index.html?fbclid=IwAR30iL0RLxMBpUfQzh-b4d_Qeu-x_R6rERytYMGeEuG-J6OfR1pvFJ0_7HY", "target": "_blank", "rel": "noreferrer" }), "failures"), " through both data visualizations and extensive interviews to bring our readers and Americans at large a little bit closer to the food they eat."), mdx("h2", { "id": "h2a-visually" }, "H2A, Visually"), mdx("iframe", { src: "https://public.tableau.com/shared/8YXPTJFYZ?:display_count=y&:origin=viz_share_link:showVizHome=no&:embed=true", width: "100%", height: "600", allowFullScreen: true }), mdx("p", null, "This ", mdx("a", _extends({ parentName: "p" }, { "href": "https://public.tableau.com/views/TheCurrentStateofH2A/Story1?:language=en&:display_count=y&publish=yes&:origin=viz_share_link", "target": "_blank", "rel": "noreferrer" }), "visualization"), " is a Tableau storyboard that highlights some of the basics about the H2A program: where workers are employed by county, which states use the H2A program, and which crops are most affected by H2A."), mdx("p", null, "All of the data used in this graphic come from the US Department of Labor\u2019s Employment and Training Administration\u2019s published dataset on H2A visa applications for the years 2016-2019."), mdx("p", null, "The map, ", mdx("em", { parentName: "p" }, "Where do H2A visa workers go?"), ", shows where H2A jobs are being taken in each county in the US. If a county has H2A activity, the size of the circle represents the number of certified H2A jobs in 2019. It is worth noting that this does not show the number of workers going to each county but rather the number of jobs that were given to an H2A visa holder. Although the two may correlate, a single H2A visa holder may have multiple jobs (potentially on the same farm) at different points in the year. Mike Williamson - the Mississippi sweet potato farmer - hires the same 28 workers in May to plant the crop and in September to pick the harvest. For these 28 workers, these are two separate jobs. In the summer months between the jobs, the workers must leave the US."), mdx("p", null, "The second visualization, ", mdx("em", { parentName: "p" }, "Which states use H2A?"), ", shows the amount of H2A labor required in each state in 2019. Each visa application contains the job start and end date as well as the expected number of hours worked per week, so this was used to calculate the total number of person-hours required for each state. Interestingly, the top five states - North Carolina, Washington, Florida, California, and Georgia - make up 51% of the entire labor in the country."), mdx("p", null, "Lastly, in ", mdx("em", { parentName: "p" }, "Which crops are affected by H2A?"), ", the size of each bubble corresponds to the number of person-hours spent on a given crop in each state. By default, the visualization shows data for the entire country but the state can be adjusted in the dropdown. On each visa application, a farmer must specify the job function or primary crop for the job. Many farmers often put \u201CGeneral Farm Worker.\u201D However, the most popular crop for H2A visa holders is tobacco, a crop that supports a large industry but also requires intensive manual labor. It is interesting to see that other crops like corn - America\u2019s largest crop by total production - require much less H2A labor because harvesting is aided by machinery and, thus, requires fewer person-hours. Therefore, the number of person-hours dedicated to a particular crop is a function of the size of the industryandhow much individual attention is required for crop production."), mdx("p", null, "Sources"), mdx("ol", null, mdx("li", { parentName: "ol" }, "U.S. Department of Labor, Employment and Training Administration, \u201CH2A Disclosure Data\u201D for years 2016-2019"), mdx("li", { parentName: "ol" }, "U.S. Department of State, Bureau of Consular Affairs, \u201CNonimmigrant Visa Statistics\u201D"))); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/data-from-reported-covid-19-tests-are-telling-an-incomplete-story:-here's-what-you-need-to-knowhttps://blog.civicdatadesignlab.mit.edu/data-from-reported-covid-19-tests-are-telling-an-incomplete-story:-here's-what-you-need-to-knowFri, 31 Jul 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "Data from reported COVID-19 tests are telling an incomplete story: Here's what you need to know", "author": "Brian Williams", "date": "2020-07-31T00:00:00.000Z", "excerpt": "As of July 26th, there were 4.2 million positive COVID-19 tests in the United States, however, only about 2.2 million of those cases have race data associated. ...How did we get here?", "tags": ["Covid", "Data", "Health", "Race", "Visualization"], "hero": "images/newplot-1-.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, mdx("strong", { parentName: "p" }, "Missing Racial Data in COVID-19 Reporting")), mdx("p", null, "In the past six months, hospitals, clinics, and medical institutions across the United States have conducted millions of COVID-19 tests. The data from the tests has been made publicly available through multiple avenues for public use. ", mdx("a", _extends({ parentName: "p" }, { "href": "https://covidtracking.com/", "target": "_blank", "rel": "noreferrer" }), "The Covid Tracking Project"), ", a volunteer organization launched from ", mdx("em", { parentName: "p" }, "The Atlantic"), ", has collected and published metadata to accompany the testing data. One such piece of metadata: race."), mdx("p", null, "When analyzing the reported race data, I\u2019ve noticed that some states report race data much more consistently and thoroughly than others. This prompted me to dig into why this is, why a testing center does or does not report race, and how the state, county, municipal, and lab policies vary with regards to collecting and reporting race information."), mdx("p", null, "As a part of the CDDL\u2019s Missing Data Project, this investigation tries to tackle just that: the missing data that is inherent in reported health data. By trying to highlight this missing data, we may be able to better illuminate issues within this larger system."), mdx("h3", { "id": "at-first-glance" }, mdx("strong", { parentName: "h3" }, "At First glance.")), mdx("p", null, "Below is a graphic representing the racial breakdown of positive cases in each U.S. state and territory over time, as data is made available."), mdx("iframe", { width: "1200", height: "600", frameBorder: "0", scrolling: "no", style: { "border": "none" }, seamless: "seamless", src: "//plotly.com/~brianwilliams2022/13.embed?link=false" }), mdx("p", null, mdx("strong", { parentName: "p" }, "Notes:")), mdx("p", null, "It\u2019s an ", mdx("a", _extends({ parentName: "p" }, { "href": "http://www.plotly.com/~brianwilliams2022/13.embed?link=false", "target": "_blank", "rel": "noreferrer" }), "interactive visualization"), "! ", mdx("em", { parentName: "p" }, "Please click the link to view in proper dimensions."), " (1) Use the options in the top right to start/pause the animation, or even select states to compare percentages over time. You can highlight one racial category (like Asian or white) and see those individual trends in the states over time. This is a very useful feature!"), mdx("p", null, "I urge you to pay attention to the ", mdx("em", { parentName: "p" }, "Unknown"), " category as percentages move over time. ", mdx("strong", { parentName: "p" }, "Notice these states and territories in particular:"), " North Dakota (ND), New York (NY), Puerto Rico (PR), Texas (TX), Northern Marianas (MP), and Virgin Islands (VI). These regions do a particularly poor job in reporting race in their testing results."), mdx("p", null, "When investigating, I wondered \u201CWhat\u2019s the functional difference between the ", mdx("em", { parentName: "p" }, "Other"), " designation and the", mdx("em", { parentName: "p" }, "Unknown"), "designation?\u201D In the context of the other racial and ethnic categories: ", mdx("em", { parentName: "p" }, "American Indian or Alaska Native, Asian, Black or African American, Latinx, Native Hawaiian or Other Pacific Islander"), ", and ", mdx("em", { parentName: "p" }, "White"), ", the ", mdx("em", { parentName: "p" }, "Other"), " category doesn\u2019t give any description of value more than the ", mdx("em", { parentName: "p" }, "Unknown"), " category. And in some states, reported race data seems to switch categories - from ", mdx("em", { parentName: "p" }, "Unknown"), " to ", mdx("em", { parentName: "p" }, "Other"), " - after a certain date. Therefore, I decided ", mdx("em", { parentName: "p" }, "Other"), " will be treated as ", mdx("em", { parentName: "p" }, "Unknown"), " for the purposes of this project. Regardless, it speaks to how \u201Cmissing data\u201D is pervasive throughout the healthcare system."), mdx("h3", { "id": "the-bigger-picture" }, mdx("strong", { parentName: "h3" }, "The Bigger Picture")), mdx("p", null, "As of July 26th, there were ", mdx("strong", { parentName: "p" }, "4.2 million"), " positive COVID-19 tests in the United States, however, only about ", mdx("strong", { parentName: "p" }, "2.2 million"), " of those cases have race data associated. The ", mdx("em", { parentName: "p" }, "Other"), " category is purposefully ", mdx("strong", { parentName: "p" }, "not"), " considered to be a race designation in this calculation because of how it differs descriptively from ", mdx("em", { parentName: "p" }, "Multiracial"), ". (2)"), mdx("p", null, "In other words, for any given COVID test, you could flip a coin to determine whether the patient\u2019s race is known. With that level of (un)certainty, I ask: what aren\u2019t we seeing? What could all this missing racial data mean for real COVID testing results? And how is it impacting our communities?"), mdx("p", null, "From various ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.nytimes.com/interactive/2020/07/05/us/coronavirus-latinos-african-americans-cdc-data.html", "target": "_blank", "rel": "noreferrer" }), "sources"), ", we know that the pandemic impacts varying demographics differently, notably, disproportionately impacting Black and Latinx communities. To that end, how accurately can we create solutions or policy decisions to alleviate and support these communities with only 50% certainty of data?"), mdx("p", null, "In the below visualization, the known percentages of cases with reported race are plotted against each state\u2019s testing per capita. (3) The plot is animated to show how each state has progressed over time."), mdx("iframe", { width: "1200", height: "800", frameBorder: "0", scrolling: "no", style: { "border": "none" }, seamless: "seamless", src: "//plotly.com/~brianwilliams2022/11.embed?link=false" }), mdx("p", null, mdx("strong", { parentName: "p" }, "Notes:")), mdx("p", null, "Another ", mdx("a", _extends({ parentName: "p" }, { "href": "https://plotly.com/~brianwilliams2022/11.embed?link=false", "target": "_blank", "rel": "noreferrer" }), "interactive visualization"), "! ", mdx("em", { parentName: "p" }, "Please click the link to view in proper dimensions."), " Use the options in the top right to pan around the graph and select states to highlight their path over time. You can also select multiple states with Shift+Select for easy comparisons. Hover over each state bubble for more relevant data. You can press each category name to only view states of that category as well."), mdx("p", null, "The size of each state bubble represents its total positive tests. So a state with more cases will be represented as a larger bubble than a state with fewer cases in this visualization."), mdx("h3", { "id": "categories" }, mdx("strong", { parentName: "h3" }, "Categories")), mdx("p", null, mdx("em", { parentName: "p" }, "[", "Highly Impacted States, Increasing, Decreasing, and Constant]")), mdx("p", null, "States are categorized by whether this \u201Cknown percentage\u201D factor has increased, decreased, or remained relatively constant from the first time data is available for that state to the latest data available. In order for a state to be considered \u201Cincreasing,\u201D it would need to increase by 5 or more percentage points. Similarly, \u201Cdecreasing\u201D states are those that decreased by 5 or more percentage points. \u201CConstant\u201D states are those that lie in the middle."), mdx("p", null, "For example, if a state\u2019s \u201Cknown percentage\u201D is 69.7% on it\u2019s earliest date and on it\u2019s latest date it\u2019s 85.4%, the state will be placed in the \u201CIncreasing\u201D category which is labeled green in the visualization."), mdx("h3", { "id": "discussion" }, mdx("strong", { parentName: "h3" }, "Discussion")), mdx("p", null, "The motivation to use testing per capita as a metric rather than absolute state population was because we thought the rate of reporting race could be affected by the total number of tests, which correlates with a state\u2019s population. Rather, we were interested in comparing states with similar tests per capita as that could correlate better with similar testing practices."), mdx("p", null, mdx("em", { parentName: "p" }, "Category: Highly Impacted States")), mdx("p", null, "We expected states with large amounts of total positive tests (states like Arizona, California, Florida, Texas, and New York) to have very low amounts of race data reported compared to other states. We suspected that as people flooded into testing locations and the states\u2019 testing per capita increased, some aspects of the testing process would give way. As testing locations made operational decisions to keep the testing process optimized and safe for patients and medical staff, we thought patient identification would be overlooked from the strain of this increased testing density."), mdx("p", null, "Looking back at this, we weren\u2019t necessarily wrong! But considering how the state bubbles fluctuate over time and how scattered they are (suggesting little correlation between reported race data and testing density), we understand there are other variables contributing to discrepancies in reported race data other than just the impact of rising testing density."), mdx("p", null, "Nonetheless, here are some interesting numbers:"), mdx("p", null, "As of July 26th,"), mdx("ul", null, mdx("li", { parentName: "ul" }, "The percent of cases that have race data in those ", mdx("strong", { parentName: "li" }, "five"), " highly impacted states: 32.81%"), mdx("li", { parentName: "ul" }, "The percent of cases that have race data in every state and territory: 51.75%"), mdx("li", { parentName: "ul" }, "The percent of cases that have race data in the ", mdx("em", { parentName: "li" }, "non-vulnerable"), " states: 66.29%")), mdx("p", null, "In Arizona, California, Florida, Texas, and New York, for every 3 cases, only 1 case has race data reported, but in every other region in the United States combined, 2 out of 3 cases have race data reported. Yet, when you combine these two sets, the national average moves to 1 out of 2 cases having race data reported. This means that around ", mdx("strong", { parentName: "p" }, "half of all COVID-19 cases in the United States"), " are in these ", mdx("strong", { parentName: "p" }, "five, most impacted"), " states."), mdx("p", null, mdx("em", { parentName: "p" }, "Category: Increasing - Green")), mdx("p", null, mdx("strong", { parentName: "p" }, "Arizona (AZ)"), ", Connecticut (CT), Delaware (DE), Georgia (GA), Illinois (IL), Louisiana (LA), Massachusetts (MA), Maryland (MD), Maine (ME), Michigan (MI), Missouri (MO), Nebraska (NE), New Hampshire (NH), New Jersey (NJ), Nevada (NV), Pennsylvania (PA), Virginia (VA), Vermont (VT), and Washington (WA) fall into this category."), mdx("p", null, "Generally, green should represent positive trends as these states are reporting more race data over time."), mdx("p", null, mdx("em", { parentName: "p" }, "Category: Decreasing - Red")), mdx("p", null, "Alaska (AK), Alabama (AL), Arkansas (AR), Colorado (CO), District of Columbia (DC), ", mdx("strong", { parentName: "p" }, "Florida (FL)"), ", Hawaii (HI), Iowa (IA), Idaho (ID), Indiana (IN), Minnesota (MN), Mississippi (MS), Montana (MT), North Carolina (NC), Oklahoma (OK), Oregon (OR), Rhode Island (RI), South Carolina (SC), ", mdx("strong", { parentName: "p" }, "Texas (TX)"), ", Utah (UT), and Wyoming (WY) fall into this category."), mdx("p", null, "This is a red flag category and some concern should be shown toward data management and reporting around race from these areas as time goes on during the pandemic."), mdx("p", null, mdx("em", { parentName: "p" }, "Category: Constant - Neutral")), mdx("p", null, mdx("strong", { parentName: "p" }, "California (CA)"), ", Guam (GU), Kansas (KS), Kentucky (KY), Northern Mariana Islands (MP), North Dakota (ND), New Mexico (NM), ", mdx("strong", { parentName: "p" }, "New York (NY)"), ", Ohio (OH), Puerto Rico (PR), South Dakota (SD), Tennessee (TN), Virgin Islands (VI), Wisconsin (WI), and West Virginia (WV) fall into this category."), mdx("p", null, "Notably, North Dakota, New York, and Puerto Rico do not report ", mdx("strong", { parentName: "p" }, "any"), " race data for their reported cases. This is alarming, and we need better data from these regions."), mdx("p", null, "Here\u2019s a clear ", mdx("a", _extends({ parentName: "p" }, { "href": "http://www.plotly.com/~brianwilliams2022/15.embed?link=false", "target": "_blank", "rel": "noreferrer" }), "visualization"), " of what these categories look like on a map. ", mdx("em", { parentName: "p" }, "Please click the link to view in proper dimensions.")), mdx("iframe", { width: "1200", height: "800", frameBorder: "0", scrolling: "no", style: { "border": "none" }, seamless: "seamless", src: "//plotly.com/~brianwilliams2022/15.embed?link=false" }), mdx("h3", { "id": "interview-insights" }, mdx("strong", { parentName: "h3" }, "Interview Insights")), mdx("p", null, "To get a more qualitative explanation of what is happening in the data, I sought experts and health officials to help me understand why so much race data could be missing and where in the process - from patient arrival to lab collection to data reporting - the missing link could occur. Additionally, I wanted to understand what the process of race designation is and how it is reported accurately."), mdx("p", null, "First, I consulted with MIT Medical Associate Medical Director & Chief of Student Health Shawn Ferullo. He explained that policies and procedures could vary greatly not only from state to state but from institution to institution. For example, Massachusetts General Hospital may have a completely different reporting protocol than MIT Medical, associated with a higher education institution, which has a much more rigidly defined community. But at the end of the day, Ferullo says, \u201C", mdx("strong", { parentName: "p" }, "what is being reported is what the State mandates to be reported."), "\u201D Let\u2019s keep this in mind."), mdx("p", null, "From what I gathered, this is the testing methodology at MIT Medical, broken down into three key areas:"), mdx("ol", null, mdx("li", { parentName: "ol" }, "Clinical Testing: Someone is sick or has symptoms and needs to be monitored, typically done in the office"), mdx("li", { parentName: "ol" }, "Contact Tracing Testing: a separate waiting area for people who may have been potentially exposed to COVID-19 but may still be healthy"), mdx("li", { parentName: "ol" }, mdx("strong", { parentName: "li" }, "Asymptomatic Testing:"), " Outside testing booths for people who don\u2019t have symptoms but want testing, low-risk large volume testing is performed here, most similar to the concept of ", mdx("em", { parentName: "li" }, "drive-thru"), " testing (4)")), mdx("p", null, "I\u2019m most interested in how demographic data is being organized at these pop-up sites."), mdx("p", null, "As Ferullo explained, since MIT Medical uses an online health record system, with demographic data already on file, race data is automatically associated with a patient\u2019s test, positive or negative. And in practice, this system minimizes the risk of losses of qualitative data even in the presence of high testing stress."), mdx("p", null, "He leaves this closing remark:"), mdx("p", null, "\u201CAs a clinician, so much of the day to day work is so patient-focused, as you can imagine\u2026 The lab has whatever requirements it has to submit, so a lot of clinicians on the front lines may not even know what data is reported\u2026 because they\u2019re tasked with seeing the patient, collecting the test, and all of that. More of these bigger, drive up, population-based testing sites, I wonder how many are scrambling, quite honestly, if some states are just scrambling to get testing that they\u2019re not as thoughtful with how they are setting up their systems", "[", "for data collecting]. I hate to think about how many states just don\u2019t want to know or are intentionally not asking the question\u201D", "[", "about race]."), mdx("p", null, "Next, an excerpt from an interview with Sarita Shah, who is an epidemiologist at Emory University, studying racial disparities in areas with high rates of COVID-19 and volunteering with Fulton County\u2019s health department. Notably, Shah sees the data collection problem firsthand. After doing nasal swabs at a drive-up testing site, she later calls those who test positive to fill in personal information, including race. ", mdx("strong", { parentName: "p" }, "[", "self-identification]"), " However, even after multiple attempts, the team reaches only about half of these people. Shah says she\u2019d love to note a person\u2019s race when they\u2019re sitting in front of her at the test site, but so far, the forms provided by labs that process the samples don\u2019t have a place to note it. \u201CI wish it was something more complex than that,\u201D she says, \u201Cbut it\u2019s not.\u201D ", mdx("strong", { parentName: "p" }, "[", "the researcher also lacks an opportunity to identify patients and race data is lost]"), " ", "[", "3]"), mdx("p", null, "Her experience is not in isolation. Shortly after the interview with Ferullo, I followed up with MIT Medical Lab Director Jonathan Pelletier who confirmed that the template form used at MIT Medical to report testing results - sent directly from the Massachusetts Department of Public Health - has no column or option for reporting race designations. This is especially confusing because the previous data suggests Massachusetts is one of the leading states in which race data is being reported. It raises the question of how this is even possible? And where are they getting their data from if it\u2019s not required, as Massachusetts historically has done a good job with their race data reporting. But these questions and many more, I do not have the answers to and as I leave them unexplored, I assume other laboratories across the nation are facing similar data issues."), mdx("p", null, "But maybe there\u2019s hope; an amendment to the Coronavirus Aid, Relief, and Economic Security (CARES) Act passed back on June 4th will require laboratories to include relevant demographic data, such as age and race, on every test. ", "[", "5] However, this is scheduled to go into effect on August 1st, leaving many passionate researchers and relief organizations in the dark about this crucial piece of metadata. I guess we\u2019ll have to wait and see if this makes a difference in the data."), mdx("h3", { "id": "conclusions" }, mdx("strong", { parentName: "h3" }, "Conclusions")), mdx("p", null, "I believe the lack of normalized and uniform systems of reporting demographic data at the local level - individual laboratories, medical institutions, testing locations, communities, and counties - results in these large discrepancies that we see at the state and national level when researchers try to draw conclusions from data aggregates. This needs to change, and fast."), mdx("p", null, "\u201COne problem that epidemiologists, in particular, have seen with all of this new lab testing sites data (pharmacies, drive-throughs, non-traditional lab settings) is incomplete data,\u201D Scott Becker of the Association of Public Health Laboratories wrote in an email to NPR. And public health experts say what\u2019s been needed are detailed breakdowns on how the virus is affecting Black and other marginalized communities. These groups have been hit especially hard, suffering higher case numbers per capita, serious illness, hospitalizations, and death.\u201D ", "[", "2]"), mdx("p", null, "Lastly, accurate information on race is critical in making policy decisions, particularly for civil rights and federal programs like unemployment stimulus packages and eviction moratoriums. Race data is also used to promote equal employment opportunities and to assess racial disparities in health and environmental risks. ", "[", "1] And in this case, laboratory testing data, in conjunction with case reports and other demographic data, provide vital guidance for mitigation and control activities, as well as properly allocating resources for relief. ", "[", "5]"), mdx("p", null, "As the country begins to reopen its doors, access to clear and accurate data is essential to communities and leadership as they make data-driven decisions for a phased reopening. For individuals, access to clear representations of real-world data improves feelings of safety, security, and awareness, and even empowers them to take action to support themselves, their families, and their communities. I hope we are able to see real change soon."), mdx("p", null, mdx("strong", { parentName: "p" }, "Project Notes - Methodology")), mdx("p", null, "(1) On some days, a state\u2019s percentages may add up to more than 100%, we understand these to be inaccuracies in the original data set of which the visualization was made. This could be attributed to either compiling errors or reporting errors from the individual medical institutions", "[", "labs]."), mdx("p", null, "(2) For the purposes of this project, we considered the race designation ", mdx("em", { parentName: "p" }, "Other"), " to be functionally the same as ", mdx("em", { parentName: "p" }, "Unknown"), " and therefore we included all cases reported having a race of ", mdx("em", { parentName: "p" }, "Other"), " as unknown. We carry over this adjustment in all calculations made for unknown visualizations."), mdx("p", null, "This first suggestion came about after some manually screening of the CSV file used to generate the visualizations showed some interesting patterns in data management which in shorter words, looked like some cases originally designated as ", mdx("em", { parentName: "p" }, "Unknown"), " were swapped over to the ", mdx("em", { parentName: "p" }, "Other"), " category. As the running totals continued in the spreadsheet, it seemed suspicious and we think this is a better way to account for that uncertainty."), mdx("p", null, "For instance, on May 13th in Iowa, 2878 cases are reported as ", mdx("em", { parentName: "p" }, "Unknown"), " and 232 cases as ", mdx("em", { parentName: "p" }, "Other"), ", then on May 17th, 3144 cases are now reported as ", mdx("em", { parentName: "p" }, "Other"), " and 0 cases as ", mdx("em", { parentName: "p" }, "Unknown"), ", and from this moment forward, case numbers increase in the ", mdx("em", { parentName: "p" }, "Other"), " category but remain stagnant in the ", mdx("em", { parentName: "p" }, "Unknown"), " category until June 17th. This is only noticeable because of how large a change it is but would be virtually undetectable if an individual laboratory chose to reinterpret data in this way. This seems more like a shift in data management rather than real testing results, and it\u2019s these shifts that are most interesting to us as we try to interpret the gaps in the data."), mdx("p", null, "(3) What does testing per capita mean?"), mdx("p", null, "After much deliberation, we choose to map the \u201Cknown percentages\u201D - the percent of positive cases in which race data exists - to testing per capita instead of positive tests because of some confounding variables. Testing per capita represents the total amount of tests performed in that state by the date specified divided by the state\u2019s population. In short, we were looking for a good way to visualize magnitudes of uncertainty as states increase their number of tests. Do testing strategies change (per state, per district, per county?) as testing density increases, especially looking at race and ethnicity data?"), mdx("p", null, "Generally speaking, the rate of infection of a disease multiplied by the total number of tests, should result in the total number of positive tests. Therefore, inherent differences in the rate of infection in separate states may impact positive test results without increasing the total number of tests - a confounding variable that made using total positive tests undesirable."), mdx("p", null, "And by using raw positive tests as an axis label, we would be assuming that increases in testing density are the only cause of increases in positive tests, when this may not be valid; the rate of infection also matters. There are many social regulations and restrictions in states, such as mask mandates or general climate patterns, which can impact the rate of infection. Especially considering the timing of this legislation varies greatly from state to state. Therefore, we thought testing per capita would be a more normalized metric off which to make conclusions."), mdx("p", null, "(4) Drive-thru refers to making tests more available in communities in an easier way, which would increase the volume of testing. With drive-thru testing, it is easier to keep patients social distancing by staying in their vehicles and to preserve clinician PPE."), mdx("p", null, mdx("strong", { parentName: "p" }, "Sources")), mdx("p", null, mdx("em", { parentName: "p" }, "Non-Data")), mdx("p", null, "[", "1] ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.census.gov/topics/population/race/about.html", "target": "_blank", "rel": "noreferrer" }), "US Census - About Race")), mdx("p", null, "[", "2] ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.npr.org/sections/coronavirus-live-updates/2020/06/04/869815033/race-ethnicity-data-to-be-required-with-coronavirus-tests-in-u-s", "target": "_blank", "rel": "noreferrer" }), "Race, Ethnicity Data To Be Required With Coronavirus Tests In U.S.")), mdx("p", null, "[", "3] ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.sciencemag.org/news/2020/07/huge-hole-covid-19-testing-data-makes-it-harder-study-racial-disparities", "target": "_blank", "rel": "noreferrer" }), "\u2018Huge hole\u2019 in COVID-19 testing data makes it harder to study racial disparities")), mdx("p", null, "[", "4] ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.nytimes.com/interactive/2020/07/05/us/coronavirus-latinos-african-americans-cdc-data.html", "target": "_blank", "rel": "noreferrer" }), "The Fullest Look Yet at the Racial Inequity of Coronavirus")), mdx("p", null, "[", "5] ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.hhs.gov/sites/default/files/covid-19-laboratory-data-reporting-guidance.pdf", "target": "_blank", "rel": "noreferrer" }), "CARES Act Section 18115")), mdx("p", null, mdx("em", { parentName: "p" }, "Data")), mdx("p", null, "[", "6] ", mdx("a", _extends({ parentName: "p" }, { "href": "https://covidtracking.com/", "target": "_blank", "rel": "noreferrer" }), "The COVID Tracking Project | The COVID Tracking Project")), mdx("ul", null, mdx("li", { parentName: "ul" }, "For raw case data.")), mdx("p", null, mdx("em", { parentName: "p" }, "For region populations")), mdx("p", null, "[", "7] ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.census.gov/newsroom/press-kits/2019/national-state-estimates.html", "target": "_blank", "rel": "noreferrer" }), "2019 National and State Population Estimates")), mdx("p", null, "[", "8] ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.doi.gov/oia/islands", "target": "_blank", "rel": "noreferrer" }), "Islands We Serve | US Department of the Interior")), mdx("ul", null, mdx("li", { parentName: "ul" }, "American Samoa, Guam, Northern Mariana Islands, Virgin Islands."))); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/covid-19-state-reopening-policies-and-mask-mandateshttps://blog.civicdatadesignlab.mit.edu/covid-19-state-reopening-policies-and-mask-mandatesFri, 24 Jul 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "COVID-19 State Reopening Policies and Mask Mandates", "author": "Amy Fang", "date": "2020-07-24T00:00:00.000Z", "tags": ["Covid", "Data", "Visualization"], "hero": "images/statewide-reopening-and-mask-mandate-map-by-week.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "All 50 states have been affected uniquely and have reacted differently to COVID-19. Some have authorized statewide stay-at-home orders, and some have authorized statewide mask mandates, but even these policies vary in both scale and time from state to state. Notable news publications, including the ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html", "target": "_blank", "rel": "noreferrer" }), "New York Times"), " ", "[", "1],", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.cnn.com/2020/06/19/us/states-face-mask-coronavirus-trnd/index.html", "target": "_blank", "rel": "noreferrer" }), "CNN"), " ", "[", "2], and the", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.washingtonpost.com/graphics/2020/national/states-reopening-coronavirus-map/", "target": "_blank", "rel": "noreferrer" }), "Washington Post"), " ", "[", "3], have created interactive visualizations illustrating these variations in state reopening statuses, consistently updating in real-time to provide the latest information. To add additional insight to these nationwide conversations on reopening, these data visualizations narrate timelines: comparing and contrasting how ever-shifting national guidelines and state case counts have influenced statewide policies over time."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1700px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "96.41176470588235%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/7f98ca36214ebafa075c0a445928fcc2/74d86/statewide-reopening-and-mask-mandate-map-by-week.webp 1700w"], "sizes": "(max-width: 1700px) 100vw, 1700px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/7f98ca36214ebafa075c0a445928fcc2/7f8a1/statewide-reopening-and-mask-mandate-map-by-week.png 1700w"], "sizes": "(max-width: 1700px) 100vw, 1700px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/7f98ca36214ebafa075c0a445928fcc2/7f8a1/statewide-reopening-and-mask-mandate-map-by-week.png", "alt": "statewide reopening and mask mandate map by week", "title": "Statewide Reopening and Mask Mandate Map by Week, Source: NYT [1] and CNN [4]", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "COVID-19 has created an international public health epidemic and, partially due to its shocking novelty, has been met with a variety of uncoordinated responses on the international, national, state, and even county and municipal level. As our collective understanding of the virus increases over time, guidelines, particularly those for preventative measures, have been ever-evolving. For example, although the first case of COVID-19 in the United States was laboratory-confirmed on January 20, 2020 ", "[", "5], the Center for Disease Control (CDC), our leading national health authority, did not recommend that everyone wear face coverings around others until the beginning of April ", "[", "6, 7]. Until then, it was unconfirmed and unknown that the virus could also be spread by asymptomatic people. In fact, President Trump did not endorse wearing face masks until July 21."), mdx("p", null, "As prominently displayed in the above map, this inevitable lack of clarity around COVID-19 is also reflected in the constant shifting of state reopening policies. By the beginning of June, every state had initiated their reopening plans. By mid-July, ", mdx("strong", { parentName: "p" }, "20"), " states had paused or even reversed upon these original plans. Although individual counties and cities have imposed further restrictions on their residents, these data visualizations look exclusively at reopening policies and mask mandates at the state level."), mdx("p", null, "Although COVID-19 associated death counts can be a rough metric, indicative of where a state stands in successfully combating the virus, it is difficult to completely understand how and why each state makes such decisions. The following data visualizations compare COVID-19 associated death counts with state reopening and mask wearing policies. Despite an interesting graphic, we are incapable of commenting on the level of correlation or causation between these two variables because the spread and control of the virus is a multi-dimensional problem. I chose to look at death counts rather than case counts due to the additional impact and ambiguity of regional differences in testing rates, reporting procedures, and hospital resources. From the ", mdx("a", _extends({ parentName: "p" }, { "href": "https://data.cdc.gov/NCHS/Provisional-COVID-19-Death-Counts-by-Week-Ending-D/r8kw-7aab", "target": "_blank", "rel": "noreferrer" }), "dataset"), " ", "[", "9], blank spaces in the chart data visualization indicate that \u201COne or more data cells have counts between 1\u20139 and have been suppressed in accordance with NCHS confidentiality standards.\u201D"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1700px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "155.23529411764704%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": 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"alt": "statewide reopening and mask mandate chart by week ordered by decreasing total covid 19 associated death count", "title": "Statewide Reopening and Mask Mandate Chart by Week, Ordered by Decreasing Total COVID-19 Associated Death Count, Source: NYT [1] and CNN [4]", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1600px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "137.5%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": 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Count and Statewide Mask Mandate Chart by Week, Source: NYT [1], CNN [4], and CDC [9]", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1787px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "49.52434247341914%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": 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["/static/20b4e21bf2ced9d3884eac94d07445f9/c6cb5/covid-19-associated-death-count-up-until-statewide-mask-mandate-graph.webp 1787w"], "sizes": "(max-width: 1787px) 100vw, 1787px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/20b4e21bf2ced9d3884eac94d07445f9/b7458/covid-19-associated-death-count-up-until-statewide-mask-mandate-graph.png 1787w"], "sizes": "(max-width: 1787px) 100vw, 1787px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/20b4e21bf2ced9d3884eac94d07445f9/b7458/covid-19-associated-death-count-up-until-statewide-mask-mandate-graph.png", "alt": "covid 19 associated death count up until statewide mask mandate graph", "title": "COVID-19 Associated Death Count until Statewide Mask Mandate Graph, Categorized by Reopening Stage at Time of Mask Mandate, Source: NYT [1], CNN [4], and CDC [9]", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "Thus, from these data visualizations, I draw the following conclusions:"), mdx("ol", null, mdx("li", { parentName: "ol" }, "Statewide mask mandates do not necessarily align with the stages of a state\u2019s reopening policies. In fact, statewide mask mandates have been initiated when a state has been under a stay-at-home order, while reopening, after being reopened, while pausing reopening, and while reversing reopening."), mdx("li", { parentName: "ol" }, "As of June 12th, 24 states have statewide mask mandates. 16 of these states instated their mask mandate within two weeks of their peak (in the case of Texas, their second peak) COVID-19 associated death count."), mdx("li", { parentName: "ol" }, "The 12 states with the largest total COVID-19 associated death counts, excluding Florida, have statewide mask mandates, 7 of which were instated while the state was under a stay-at-home order. This aligns with the results of a ", mdx("a", _extends({ parentName: "li" }, { "href": "https://www.nytimes.com/interactive/2020/07/17/upshot/coronavirus-face-mask-map.html?action=click&module=Top%20Stories&pgtype=Homepage", "target": "_blank", "rel": "noreferrer" }), "New York Times nationwide survey"), " ", "[", "10], which qualitatively asked individuals about their mask-wearing preferences, and found that \u201CPlaces that have experienced a lot of coronavirus, with a few exceptions, show higher-than-usual rates of masking.\u201D")), mdx("p", null, "This same article concludes, \u201CIn parts of the country without a lot of cases, there are still wide variations in mask use that appear to be driven more by local custom and partisan identification than by specific disease risk. If coronavirus cases end up climbing in those areas, too, masks may start appearing on many more faces.\u201D"), mdx("p", null, "As with everything surrounding the collective status of the United States and COVID-19, looking not only at statewide mask mandates but also at statewide reopening policies, we\u2019ll have to see how everything plays out."), mdx("h2", { "id": "sources" }, "Sources"), mdx("p", null, "[", "1] Lee, Jasmine C., et al. \u201CSee How All 50 States Are Reopening (and Closing Again).\u201D The New York Times, The New York Times, 25 Apr. 2020,", mdx("a", _extends({ parentName: "p" }, { "href": "http://www.nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html", "target": "_blank", "rel": "noreferrer" }), "www.nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html"), "."), mdx("p", null, "[", "2] Elassar, Alaa. \u201CThis Is Where All 50 States Stand on Reopening.\u201D CNN, Cable News Network, 26 Apr. 2020,", mdx("a", _extends({ parentName: "p" }, { "href": "http://www.cnn.com/interactive/2020/us/states-reopen-coronavirus-trnd/", "target": "_blank", "rel": "noreferrer" }), "www.cnn.com/interactive/2020/us/states-reopen-coronavirus-trnd/"), "."), mdx("p", null, "[", "3] Gerhart, Ann. \u201CWhere States Reopened and Cases Spiked after the U.S. Shutdown.\u201D The Washington Post, WP Company, 20 July 2020,", mdx("a", _extends({ parentName: "p" }, { "href": "http://www.washingtonpost.com/graphics/2020/national/states-reopening-coronavirus-map/", "target": "_blank", "rel": "noreferrer" }), "www.washingtonpost.com/graphics/2020/national/states-reopening-coronavirus-map/"), "."), mdx("p", null, "[", "4] Kim, Allen, et al. \u201CThese Are the States Requiring People to Wear Masks When out in Public.\u201D CNN, Cable News Network, 22 July 2020,", mdx("a", _extends({ parentName: "p" }, { "href": "http://www.cnn.com/2020/06/19/us/states-face-mask-coronavirus-trnd/index.html", "target": "_blank", "rel": "noreferrer" }), "www.cnn.com/2020/06/19/us/states-face-mask-coronavirus-trnd/index.html"), "."), mdx("p", null, "[", "5] Stokes EK, Zambrano LD, Anderson KN, et al. Coronavirus Disease 2019 Case Surveillance \u2014 United States, January 22\u2013May 30, 2020. MMWR Morb Mortal Wkly Rep 2020;69:759\u2013765. DOI:", mdx("a", _extends({ parentName: "p" }, { "href": "http://dx.doi.org/10.15585/mmwr.mm6924e2", "target": "_blank", "rel": "noreferrer" }), "http://dx.doi.org/10.15585/mmwr.mm6924e2")), mdx("p", null, "[", "6] Goodnough, Abby, and Knvul Sheikh. \u201CC.D.C. Weighs Advising Everyone to Wear a Mask.\u201D The New York Times, The New York Times, 31 Mar. 2020,", mdx("a", _extends({ parentName: "p" }, { "href": "http://www.nytimes.com/2020/03/31/health/cdc-masks-coronavirus.html?action=click&module=RelatedLinks&pgtype=Article", "target": "_blank", "rel": "noreferrer" }), "www.nytimes.com/2020/03/31/health/cdc-masks-coronavirus.html?action=click&module=RelatedLinks&pgtype=Article"), "."), mdx("p", null, "[", "7] \u201CSurgeon General Explains Evolution of CDC Face Mask Guidance.\u201D MSNBC, NBCUniversal News Group, 3 Apr. 2020,", mdx("a", _extends({ parentName: "p" }, { "href": "http://www.msnbc.com/msnbc/watch/surgeon-general-explains-evolution-of-cdc-face-mask-guidance-81619013687", "target": "_blank", "rel": "noreferrer" }), "www.msnbc.com/msnbc/watch/surgeon-general-explains-evolution-of-cdc-face-mask-guidance-81619013687"), "."), mdx("p", null, "[", "8] Baker, Peter. \u201CTrump, in a Shift, Endorses Masks and Says Virus Will Get Worse.\u201D The New York Times, The New York Times, 22 July 2020,", mdx("a", _extends({ parentName: "p" }, { "href": "http://www.nytimes.com/2020/07/21/us/politics/trump-coronavirus-masks.html?action=click&module=RelatedLinks&pgtype=Article", "target": "_blank", "rel": "noreferrer" }), "www.nytimes.com/2020/07/21/us/politics/trump-coronavirus-masks.html?action=click&module=RelatedLinks&pgtype=Article"), "."), mdx("p", null, "[", "9] National Center for Health Statistics. \u201CProvisional COVID-19 Death Counts by Week Ending Date and State.\u201D Centers for Disease Control and Prevention, Centers for Disease Control and Prevention, 23 July 2020,", mdx("a", _extends({ parentName: "p" }, { "href": "https://data.cdc.gov/NCHS/Provisional-COVID-19-Death-Counts-by-Week-Ending-D/r8kw-7aab", "target": "_blank", "rel": "noreferrer" }), "data.cdc.gov/NCHS/Provisional-COVID-19-Death-Counts-by-Week-Ending-D/r8kw-7aab.")), mdx("p", null, "[", "10] Katz, Josh, et al. \u201CA Detailed Map of Who Is Wearing Masks in the U.S.\u201D The New York Times, The New York Times, 17 July 2020,", mdx("a", _extends({ parentName: "p" }, { "href": "http://www.nytimes.com/interactive/2020/07/17/upshot/coronavirus-face-mask-map.html?action=click&module=Top%2BStories&pgtype=Homepage", "target": "_blank", "rel": "noreferrer" }), "www.nytimes.com/interactive/2020/07/17/upshot/coronavirus-face-mask-map.html?action=click&module=Top%2BStories&pgtype=Homepage"), ".")); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/the-anger-behind-the-nairobi-protests-explained-in-4-chartshttps://blog.civicdatadesignlab.mit.edu/the-anger-behind-the-nairobi-protests-explained-in-4-chartsFri, 17 Jul 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "The anger behind the Nairobi protests, explained in 4 charts", "author": "Civic Data Design Lab", "date": "2020-07-17T00:00:00.000Z", "tags": ["Visualization"], "hero": "images/cddl_blogpost_0713-03.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "On the first of June, Vox released an article called \u201CThe anger behind the protests, explained in 4 charts\u201D. In the article, Sean Collins uses data visualizations to explain the landscape of police violence in the United States, a structural issue that has come to the surface most recently due to the murder of George Floyd on the 25th of May. The author states \u201Cprotests have continued night after night because they are not just about that single killing but what it represents: rampant police brutality that seems to have no consequences\u201D (Collins 2020). Reading this article got me thinking about the parallels in Nairobi, Kenya, where recent protests have mirrored those happening in the United States."), mdx("p", null, "This article explores the anger behind the protests in Nairobi, looking into some of the dynamics of police violence through four charts. You will notice that this story looks quite a bit different to the sleek digital visualizations that we create at the CDDL. I have always been amazed by data humanism, particularly the hand-drawn visualizations of Mona Chalabi and Giorgia Lupi, and my hope is that the hand drawn charts convey both the urgency of the situation and its deeply human impact."), mdx("p", null, "On the 8th of June in Mathare, one of Nairobi\u2019s informal settlements, over 200 protesters marched with signs reading \u201CStop Killer Cops\u201D and \u201COur Lives Matter\u201D, and images of mothers of victims of police violence kneeling in front of Mathare Social Justice Centre draw scary parallels to what is going on in the United States. While many people in the United States see the coronavirus and police violence protests as separate crises, in Nairobi the two crises and their responses have been deeply entwined."), mdx("p", null, mdx("strong", { parentName: "p" }, "Where Coronavirus and Police Violence Collide")), mdx("p", null, "The Kenyan government was quick to respond to the Coronavirus outbreak, implementing a lockdown and strict curfew starting from 27th March 2020. This curfew has been enforced intensely and with violence in many forms. At police checkpoints strategically placed on many roads key to people\u2019s daily commutes between the center city and its outskirts, workers were met with violence:"), mdx("p", null, mdx("em", { parentName: "p" }, "Thousands joined long queues at bus stations that extended beyond the 7pm cut-off time as matatus \u2013 observing new guidelines \u2013 were ferrying at about 50 per cent capacity\u2026.As they braved the delays and queues, police officers on the other end were arming themselves with clubs and whips \u2013 generally preparing themselves for violence (in Daily Nation 2020).")), mdx("p", null, "Tear gas was employed to dispel crowds and protesters both in Nairobi and Mombasa. In addition, state actors carrying out forced evictions in Kariobangi settlement have left 5,000 citizens newly homeless and at higher risk for contracting COVID-19 (Bhalla 2020). Then, we began to hear how citizens accused of breaking curfew were beaten and killed. Over 15 people were murdered by police since the start of the curfew, including 13-year-old boy Yassin Hussein Moyo, who was shot by a police bullet while standing on his apartment balcony in late March. As the Nation reports, \u201Cthe indiscriminate violence on men, women and children was shocking in its brutality and complete disregard for liberties\u201D (Daily Nation 2020). #NoJusticeNoPeace and #ICan\u2019tBreathe were themes that arose in Nairobi too, and activist groups including the Mathare Social Justice Centre have led protests to bring these issues to a national and global view."), mdx("p", null, mdx("strong", { parentName: "p" }, "The Graphics")), mdx("p", null, "One"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1731px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "100.0577700751011%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/d664b7fac43f95163d919596f00b8b75/f7d4b/cddl_blogpost_0713-01.webp 1731w"], "sizes": "(max-width: 1731px) 100vw, 1731px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/d664b7fac43f95163d919596f00b8b75/e19c0/cddl_blogpost_0713-01.png 1731w"], "sizes": "(max-width: 1731px) 100vw, 1731px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/d664b7fac43f95163d919596f00b8b75/e19c0/cddl_blogpost_0713-01.png", "alt": "cddl blogpost 0713 01", "title": "cddl blogpost 0713 01", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "\u201CThey have killed us more than corona,\u201D a protester from the 8th June in Mathare, said simply. This straightforward quote inspired the first graphic. As of the end of May, the statistics showed that 101 people had been killed by police and 71 people killed by COVID-19. As we move past the initial curfew, the COVID-19 cases have risen rapidly to overtake this number, however police murder persists."), mdx("p", null, "Two"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1730px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "100.11560693641619%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/70393c9f50924da229aa08517d8d0b3f/8315d/cddl_blogpost_0713-03.webp 1730w"], "sizes": "(max-width: 1730px) 100vw, 1730px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/70393c9f50924da229aa08517d8d0b3f/205df/cddl_blogpost_0713-03.png 1730w"], "sizes": "(max-width: 1730px) 100vw, 1730px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/70393c9f50924da229aa08517d8d0b3f/205df/cddl_blogpost_0713-03.png", "alt": "cddl blogpost 0713 03", "title": "cddl blogpost 0713 03", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "The initial curfew went from 7pm to 5am, with some reports of police enforcement of the curfew even before the clock hit seven. The example I have taken is the work schedule of a matatu driver or housekeeper who, like many workers across the city, leaves before dawn and works until late because they must commute multiple hours a day in Nairobi traffic. This leaves more than three hours where workers traveling to and from their workplaces were vulnerable to severe violence and at times murder at the hands of the police. As of the 7th of June, the curfew has since been extended 21 days but relaxed to 9pm to 4am, allowing more time for free movement."), mdx("p", null, "Three"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1730px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "100.11560693641619%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/606acd2e4d378fc583bafaf77a31306c/8315d/cddl_blogpost_0713-02.webp 1730w"], "sizes": "(max-width: 1730px) 100vw, 1730px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/606acd2e4d378fc583bafaf77a31306c/205df/cddl_blogpost_0713-02.png 1730w"], "sizes": "(max-width: 1730px) 100vw, 1730px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/606acd2e4d378fc583bafaf77a31306c/205df/cddl_blogpost_0713-02.png", "alt": "cddl blogpost 0713 02", "title": "cddl blogpost 0713 02", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "According to GAN Business Anti-Corruption portal, three out of four Kenyans consider most or all police officers as corrupt (GCB 2015). This graphic points to larger issues with police conduct in Nairobi: these include ubiquitous bribery, extortion especially of Somalis, and brutality toward often innocent citizens. Corrupt police officers are rarely arrested or prosecuted for corruption crimes (HRR 2016)."), mdx("p", null, "Four"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1731px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "100.0577700751011%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/55eb58517e5ad22ec4290860df3bbfed/f7d4b/cddl_blogpost_0713-04.webp 1731w"], "sizes": "(max-width: 1731px) 100vw, 1731px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/55eb58517e5ad22ec4290860df3bbfed/e19c0/cddl_blogpost_0713-04.png 1731w"], "sizes": "(max-width: 1731px) 100vw, 1731px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/55eb58517e5ad22ec4290860df3bbfed/e19c0/cddl_blogpost_0713-04.png", "alt": "cddl blogpost 0713 04", "title": "cddl blogpost 0713 04", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "Police conduct goes further than the reach of their hand. This last visualization is a result of my inner urban planner. Something that we often forget about is how the use of teargas can have a magnified effect on high-density residential areas including informal communities like Kibera settlement in Nairobi. If teargas is released in Kibera, its 300m radius means that the teargas could reach more than 300 buildings, compromising the health of countless residents of the areas surrounding the location where the teargas was released."), mdx("p", null, mdx("strong", { parentName: "p" }, "What This Means")), mdx("p", null, "It is my hope that the graphics, along with the articles quoted, speak for themselves. However, in closing, it is important to emphasize that the situation reveals to us unlikely parallels in police brutality between the United States and Kenya. It shows us the global nature of this issue, and how police were created to support existing power structures rooted in colonialism and white supremacy. This situation is also a call for urgent innovation in Coronavirus prevention. Informal settlements, often highly dense, are doubly vulnerable- not only to the disease but also to the loss of income resulting from curfews. In the United States and in Kenya, we need to start thinking of prevention measures that protect both people\u2019s livelihoods and their lives."), mdx("p", null, "You can read more about police brutality in Kenya at Mathare Social Justice Centre, Kenyan newspaper The Daily Nation, Citizen TV, and through Human Rights Watch."), mdx("p", null, mdx("strong", { parentName: "p" }, "References")), mdx("ul", null, mdx("li", { parentName: "ul" }, "Bhalla, Nita. \u201CForced Evictions Leave 5,000 Kenyan Slum Dwellers at Risk of Coronavirus.\u201DReuters, Thomson Reuters, 6 May 2020,", mdx("a", _extends({ parentName: "li" }, { "href": "http://www.reuters.com/article/us-health-coronavirus-kenya-homelessness/forced-evictions-leave-5000-kenyan-slum-dwellers-at-risk-of-coronavirus-idUSKBN22I1VC", "target": "_blank", "rel": "noreferrer" }), "www.reuters.com/article/us-health-coronavirus-kenya-homelessness/forced-evictions-leave-5000-kenyan-slum-dwellers-at-risk-of-coronavirus-idUSKBN22I1VC"), "."), mdx("li", { parentName: "ul" }, "Collins, Sean. \u201CThe Anger behind the Protests, Explained in 4 Charts.\u201DVox, Vox, 31 May 2020,", mdx("a", _extends({ parentName: "li" }, { "href": "http://www.vox.com/2020/5/31/21276004/anger-police-killing-george-floyd-protests", "target": "_blank", "rel": "noreferrer" }), "www.vox.com/2020/5/31/21276004/anger-police-killing-george-floyd-protests"), "."), mdx("li", { parentName: "ul" }, "Gan Integrity. \u201CKenya Corruption Report.\u201DGAN Integrity, 11 June 2020,", mdx("a", _extends({ parentName: "li" }, { "href": "http://www.ganintegrity.com/portal/country-profiles/kenya/", "target": "_blank", "rel": "noreferrer" }), "www.ganintegrity.com/portal/country-profiles/kenya/"), "."), mdx("li", { parentName: "ul" }, "Kahura, Dauti. \u201CSaba Saba At 30: The Gains We Have Lost.\u201DThe Elephant, 7 July 2020,", mdx("a", _extends({ parentName: "li" }, { "href": "http://www.theelephant.info/features/2020/07/07/saba-saba-at-30-the-gains-we-have-lost/", "target": "_blank", "rel": "noreferrer" }), "www.theelephant.info/features/2020/07/07/saba-saba-at-30-the-gains-we-have-lost/"), "."), mdx("li", { parentName: "ul" }, "Kenya Ministry of Health. 2020,Covid-19 Outbreak in Kenya - Situation Report No. 77,", mdx("a", _extends({ parentName: "li" }, { "href": "http://www.health.go.ke/wp-content/uploads/2020/06/Kenya-COVID-19-SITREP-077-02-Jun-2020.pdf", "target": "_blank", "rel": "noreferrer" }), "www.health.go.ke/wp-content/uploads/2020/06/Kenya-COVID-19-SITREP-077-02-Jun-2020.pdf"), "."), mdx("li", { parentName: "ul" }, "Santiago, Maria M. Mur. \u201CLiving with Tear Gas, the Other Face of Chile\u2019s Protests.\u201DWww.efe.com, 17 Nov. 2019,", mdx("a", _extends({ parentName: "li" }, { "href": "http://www.efe.com/efe/english/portada/living-with-tear-gas-the-other-face-of-chile-s-protests/50000260-4113001", "target": "_blank", "rel": "noreferrer" }), "www.efe.com/efe/english/portada/living-with-tear-gas-the-other-face-of-chile-s-protests/50000260-4113001"), "."), mdx("li", { parentName: "ul" }, "Sperber, Amanda. \u201C\u2018They Have Killed Us More than Corona\u2019: Kenyans Protest against Police Brutality.\u201DThe Guardian, Guardian News and Media, 9 June 2020,", mdx("a", _extends({ parentName: "li" }, { "href": "http://www.theguardian.com/global-development/2020/jun/09/they-have-killed-us-more-than-corona-kenyans-protest-against-police-brutality", "target": "_blank", "rel": "noreferrer" }), "www.theguardian.com/global-development/2020/jun/09/they-have-killed-us-more-than-corona-kenyans-protest-against-police-brutality"), "."), mdx("li", { parentName: "ul" }, "The Nation Team Kenyans Brace Brutality as Police Enforce Curfew.\u201DDaily Nation, 30 Mar. 2020,", mdx("a", _extends({ parentName: "li" }, { "href": "http://www.nation.co.ke/kenya/news/kenyans-brace-brutality-as-police-enforce-curfew-282826", "target": "_blank", "rel": "noreferrer" }), "www.nation.co.ke/kenya/news/kenyans-brace-brutality-as-police-enforce-curfew-282826"), "."), mdx("li", { parentName: "ul" }, "United Nations High Commissioner for Refugees. \u201CKenya: Information on the July 1990 Riots.\u201DRefworld,", mdx("a", _extends({ parentName: "li" }, { "href": "http://www.refworld.org/docid/3ae6aac02c.html", "target": "_blank", "rel": "noreferrer" }), "www.refworld.org/docid/3ae6aac02c.html"), "."), mdx("li", { parentName: "ul" }, "Wambui, Mary. \u201CPolice Disrupt Saba Saba Protests in the City.\u201DDaily Nation, 7 July 2020,", mdx("a", _extends({ parentName: "li" }, { "href": "http://www.nation.co.ke/kenya/news/politics/police-disrupt-saba-saba-protests-in-the-city-1446314", "target": "_blank", "rel": "noreferrer" }), "www.nation.co.ke/kenya/news/politics/police-disrupt-saba-saba-protests-in-the-city-1446314"), ".")), mdx("p", null, mdx("strong", { parentName: "p" }, "Update from the 7th of July 2020")), mdx("p", null, "On the seventh of July, another protest took place in Nairobi\u2019s Central Business District. Kenyans took to the street in the Saba Saba protests against human rights violations in the country. Saba Saba (seven seven in Kiswahili) refers to the 1990 pro-democracy demonstrations held throughout Kenya. At the time, over 6,000 people demonstrated against the detention of two cabinet ministers without trial, which reflected a larger blockage of political voice for citizens. The demonstrations called for the restoration of the multiparty system after leaders had called for it in May 1990. Then-President Daniel Arap Moi deployed the General Service Unit (GSU, the Kenyan paramilitary police) troops and used violence against protesters, killing over 20 people and leaving 1,000 in jail after the four days of confrontations (The Elephant 2020)."), mdx("p", null, "This year\u2019s protests celebrated the 30th anniversary of Saba Saba, and they came in the wake of recent cases of police violence. Protesters were met with heavy security presence including teargas and arrests. Protesters carried the message against police violence through chants and placards with messages like \u201CStop Promoting Police Deviance\u201D, \u201CStop Killer Cops\u201D and \u201CCorruption is as Bad as Colonialism\u201D (The Nation 2020). Over 20 demonstrators were arrested, and the Social Justice Working Group and its community group members continue to organize for sponsoring bail funds and working against police violence in Nairobi.")); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/housing-and-covid-19-in-san-franciscohttps://blog.civicdatadesignlab.mit.edu/housing-and-covid-19-in-san-franciscoTue, 14 Jul 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "Housing and COVID-19 in San Francisco", "author": "Joyce Zhao", "date": "2020-07-14T00:00:00.000Z", "tags": ["Covid"], "hero": "images/2019-eviction-notices-and-covid-rates.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "On March 13, 2020, Mayor London Breed issued an executive order temporarily halting most evictions in San Francisco. This moratorium, excluding evictions due to health and safety, is set to expire on July 31. As a result of legislation passed by the Board of Supervisors on June 26, landlords are permanently banned from evicting tenants because of missed rent due to the pandemic. Following the Mayor\u2019s eviction moratorium in March, only 73 eviction notices have been filed through the San Francisco Rent Board, a noticeable decrease compared to the 378 notices filed over the same time period from 2019."), mdx("p", null, "While those official eviction notices have seen a noticeable decrease in numbers, many housing justice groups are predicting", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.nytimes.com/2020/05/27/us/coronavirus-evictions-renters.html", "target": "_blank", "rel": "noreferrer" })), " ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.nytimes.com/2020/05/27/us/coronavirus-evictions-renters.html", "target": "_blank", "rel": "noreferrer" }), "a looming wave of evictions"), " following the expiration of moratoriums across the nation. In many cities, local moratoria and rent freezes have already expired and eviction courts have begun to reopen."), mdx("p", null, "Neighborhoods in San Francisco experiencing historically higher rates of eviction show correlation with some of the same neighborhoods experiencing the highest rates of COVID-19."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1000px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "80%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/df5410c1f4ecabe0ce60abc489f6f04f/e00f7/2019-eviction-notices-and-covid-rates.webp 1000w"], "sizes": "(max-width: 1000px) 100vw, 1000px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/df5410c1f4ecabe0ce60abc489f6f04f/332ff/2019-eviction-notices-and-covid-rates.png 1000w"], "sizes": "(max-width: 1000px) 100vw, 1000px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/df5410c1f4ecabe0ce60abc489f6f04f/332ff/2019-eviction-notices-and-covid-rates.png", "alt": "2019 Eviction Notices and COVID Rates by Neighborhood, San Francisco", "title": "2019 Eviction Notices and COVID Rates by Neighborhood, San Francisco", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "In particular, 10% of all eviction notices filed in 2019 took place in the Mission (highlighted in pink below), a neighborhood with historically higher rates of eviction. The Mission also currently has one of the highest COVID rates in San Francisco."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1046px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "65.2963671128107%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/b2e848baa908fbd1998bb49a60b1e6da/6ded6/eviction-count-by-nhood.webp 1046w"], "sizes": "(max-width: 1046px) 100vw, 1046px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/b2e848baa908fbd1998bb49a60b1e6da/49d2b/eviction-count-by-nhood.png 1046w"], "sizes": "(max-width: 1046px) 100vw, 1046px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/b2e848baa908fbd1998bb49a60b1e6da/49d2b/eviction-count-by-nhood.png", "alt": "2019 Eviction Notice by Neighborhood, San Francisco", "title": "2019 Eviction Notice by Neighborhood, San Francisco", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "Perhaps this statistic is unsurprising: lower-income and economically vulnerable communities have been independently found to have higher risks towards both COVID and eviction."), mdx("p", null, "However, eviction rates are only the tip of the iceberg in a city\u2019s housing landscape. Displacement of cost-burdened residents, landlords incentivized to flip affordable housing, and neighborhoods undergoing rapid gentrification\u2014 already difficult phenomena to quantify\u2014are further left out of COVID-19 related housing data. In San Francisco, the Hispanic and Latinx community alone makes up 50% of all COVID-19 cases, despite making up only 15% of the population. Furthermore, this community is most densely concentrated within the Mission."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "887px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "77.00112739571591%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAPCAIAAABr+ngCAAAACXBIWXMAAA7DAAAOwwHHb6hkAAACIElEQVQoz4VT2W7aUBD1h/U3+gv9h771pY9tX1ukKn2IGqmpolYliCaBRgmYxTGEzTZerje8gY0XiGwDJp3gBpwIqSPbmjs6Z+ackS8WBIHnea7rzmaz5DHW6/X9/wIwWBzHjuOYpmkYRvqFXnvRq9UqihfZCgbvYrGAyfP53LZtELJcLnftYcD9PxVhGFlje50k6RFgWCogeSxBPOQb2VCGaSAtbQduxhMnjKIUA3Us6yFNfNiC77t+IKmaqGhIUnkkgbp0ODQCZBRFvu9jz4xBS9uZWuOJblpDpPKywSGFFSRr4mRh0OIJGfRMXVczLBYpSNGkkQ4Jw8uXBHXLSCAB0FuBYRjCarH1JuBsTaZ9VmLQiBZUTlRZpPaHYnsgFK47XRZy5EzdrDswgm3Pnh+AQqAJ4NWwIBEVvdzov3ydOyrWRFWf2M4zjzuyYXun1UG+Sl21WEbUYD6SR0B+8erd2895pJqCrO0lP2RecFfAaXhKBNPsCQNOhoU1utyb3K8PXy++lch8pTu7i7LKswuLf9comFzEB/WeiHcQxSvQ4rzBfC/fHhSIg9MmJxv7yaskaVESaK62WbzD17qoTNAfT66OL4jLG+ZLgTg8I49LpG45W/6Tn4SRjA6nkpR43WIrbS73E39/9IcX1Uqb/vQDL9bpUpPWxxnyahPpTRqZdo+VQC3Z5yskc1ion5RvhryMt4fnDbrHa5v/eHfh/gIN7zsY3CDdYQAAAABJRU5ErkJggg==')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/27b38eeb8345f8ac90da3b071997c23d/56e69/latinx-population.webp 887w"], "sizes": "(max-width: 887px) 100vw, 887px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/27b38eeb8345f8ac90da3b071997c23d/41d70/latinx-population.png 887w"], "sizes": "(max-width: 887px) 100vw, 887px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/27b38eeb8345f8ac90da3b071997c23d/41d70/latinx-population.png", "alt": "Latinx Population, San Francisco", "title": "Latinx Population, San Francisco", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "Drawing correlations between eviction, racial demographics, and COVID rates across San Francisco raises important questions surrounding current and predicted patterns of displacement. Current eviction rates cannot fully represent who may be unable to pay rent, who is being illegally evicted, or who may be subject to eviction after the moratorium expires. There are many factors to the structural racism and pre-existing conditions that the city ascribes to COVID risk so we can\u2019t immediately map a single factor to COVID cases. Understanding the inability of eviction data to fully tell the story of San Franciscans facing housing injustice during this pandemic is only the first step in unpacking these correlations.")); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/coronavirus-occupation-race-and-coronavirushttps://blog.civicdatadesignlab.mit.edu/coronavirus-occupation-race-and-coronavirusMon, 13 Jul 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "Coronavirus, Occupation, Race, & Coronavirus", "author": "Griffin Kantz", "date": "2020-07-13T00:00:00.000Z", "excerpt": "The connection between COVID-19 and the health of the economy is obvious. Yet, the direct cause & effect relationship between the two is intricate to measure, because it is neither one-dimensional nor unidirectional.", "tags": ["Covid", "Mapping"], "hero": "images/casespercapita_20200703.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "The connection between COVID-19 and the health of the economy is obvious. Yet, the direct cause & effect relationship between the two is intricate to measure, because it is neither one-dimensional nor unidirectional."), mdx("p", null, "Here are two striking animated maps illustrating this asymmetrical relationship. First is a map representing daily new confirmed coronavirus cases per capita in the U.S. by county from March 1, 2020 to July 6, 2020 (a one-week trailing average to account for regular fluctuations)."), mdx("p", null, mdx("img", _extends({ parentName: "p" }, { "src": "/5dbbd25f7a30868f69aac87ffe63f161/casespercapita.gif", "alt": "Animated map of new coronavirus cases per capita in the U.S. (seven-day rolling average)." }))), mdx("p", null, mdx("em", { parentName: "p" }, "Map 1. Daily new confirmed coronavirus cases per capita (one-week trailing average), United States, 3/1/2020-7/6/2020. Produced with QGIS and Photoshop.")), mdx("p", null, "Here it was important to represent cases in the vertical dimension as well as choropleth fill to highlight counties both large and small."), mdx("p", null, "This second animated map displays the unemployment rate by county from Feb. 1, 2020 to June 1, 2020. The Bureau of Labor Statistics releases unemployment figures for each month, which we assign to the first day of the following month and approximate linearly for all other days. Preliminary figures for May were released last week. Figures for Puerto Rico are not available for April or May, so the territory was not included here."), mdx("p", null, mdx("img", _extends({ parentName: "p" }, { "src": "/6ae258caff80a96040bbf31cc88cd8d4/unemployment.gif", "alt": "Animated map of unemployment in the U.S." }))), mdx("p", null, mdx("em", { parentName: "p" }, "Map 2. Unemployment rate, United States, 2/1/2020-6/1/2020. Produced with QGIS and Photoshop.")), mdx("p", null, "Scrutinize these two maps in close comparison with each other. Their timeframes are offset by one month, but they cover the recent surges in each variable. Notice the incredible differences in the regions most affected by coronavirus (and in what order) versus those affected by high unemployment."), mdx("p", null, "Coronavirus cases per capita first peaked in a handful of urban hot-spots (New York, NY; New Orleans, LA; Detroit, MI; Cincinnati, OH; Albany, GA), then spread to an entirely different set of rural hot-spots (the Oklahoma panhandle; Iowa and Southern Minnesota; Northeast Arizona; and several extreme spikes in the Midwest and South), and finally experienced a surge across the entire Sun Belt which is still ongoing."), mdx("p", null, "Unemployment, meanwhile, demonstrated a ubiquitous surge across the entire country, with Las Vegas, NV, and the entirety of Hawai\u2019i and Michigan particularly impacted."), mdx("p", null, "If coronavirus is the clear precursor to the economic crisis, why do we witness these regional differences, and why do they seem to transcend political jurisdictions? Beginning our inquiry from these maps leads directly to some initial hypotheses."), mdx("h3", { "id": "race" }, "Race"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "960px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "56.25%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/d264a43f5e5b1894b4874efad68243d3/6c7d1/race.webp 960w"], "sizes": "(max-width: 960px) 100vw, 960px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/d264a43f5e5b1894b4874efad68243d3/fde0f/race.png 960w"], "sizes": "(max-width: 960px) 100vw, 960px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/d264a43f5e5b1894b4874efad68243d3/fde0f/race.png", "alt": "race", "title": "Map of percent non-white population, United States.", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, mdx("em", { parentName: "p" }, "Map 3. Percent non-white population, United States, U.S. Census Bureau. Produced with QGIS.")), mdx("p", null, mdx("a", _extends({ parentName: "p" }, { "href": "https://www.nytimes.com/interactive/2020/07/05/us/coronavirus-latinos-african-americans-cdc-data.html", "target": "_blank", "rel": "noreferrer" }), "Race"), " appears to correlate significantly with the areas experiencing the largest coronavirus surges. The ", mdx("a", _extends({ parentName: "p" }, { "href": "https://en.wikipedia.org/wiki/Black_Belt_in_the_American_South", "target": "_blank", "rel": "noreferrer" }), "Black Belt"), " in the South and the expansive cluster of Native American reservations (particularly the ", mdx("a", _extends({ parentName: "p" }, { "href": "https://navajotimes.com/coronavirus-updates/covid-19-across-the-navajo-nation/", "target": "_blank", "rel": "noreferrer" }), "Navajo Nation"), ") in the Southwest appear to be epicenters of large outbreaks. Counties along the U.S.-Mexico land border, where many migrant laborers work often without being reflected in Census figures, have also been sites of outbreaks. Structural inequities in healthcare, workplace conditions/protections, infrastructure, and crisis preparedness almost certainly contributed to the case severity in these regions. These vectors of vulnerability have also contributed to disparities in unemployment, although it is difficult to observe this at the county level. The chart below is from a recent ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.epi.org/publication/black-workers-covid/", "target": "_blank", "rel": "noreferrer" }), "report"), " by the Economic Policy Institute covering \u201Ctwo of the most lethal preexisting conditions for coronavirus\u2014racism and economic inequality\u201D."), mdx("iframe", { width: "100%", height: "802", src: "https://www.epi.org?p=197235&view=embed&embed_template=charts_v2013_08_21&embed_date=20200712&onp=193246&utm_source=epi_press&utm_medium=chart_embed&utm_campaign=charts_v2", frameBorder: "0" }), mdx("h3", { "id": "occupation-type" }, "Occupation Type"), mdx("p", null, "Intertwined with race, differences in occupation type also serve to distort the economic burden of coronavirus. Manufacturing and front-line service jobs are more exposed to infection, as well as more susceptible to layoffs due the combination of the shutdowns, thinner profit margins, and looser workplace protections. Three of the counties (seen clearly in ", mdx("a", _extends({ parentName: "p" }, { "href": "https://siouxlandnews.com/news/coronavirus/buena-vista-county-tops-national-list-for-fastest-growing-covid-19-hotpost", "target": "_blank", "rel": "noreferrer" }), "Iowa"), ", ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.indystar.com/story/news/environment/2020/04/27/cass-county-coronavirus-cases-spike-county-home-meat-plant/3033246001/", "target": "_blank", "rel": "noreferrer" }), "Indiana"), ", and ", mdx("a", _extends({ parentName: "p" }, { "href": "https://journalstar.com/lifestyles/health-med-fit/health/dakota-county-one-of-the-nations-fastest-growing-coronavirus-hot-spots/article_c91b8158-776f-56fa-a550-e98dd89a68fe.html", "target": "_blank", "rel": "noreferrer" }), "Nebraska"), " in the first map) reaching the highest new-cases-per-capita rates throughout the pandemic are sites of Tyson Foods meat processing plants, although these particular outbreaks did not happen to result in layoffs. These occupational dynamics are suspected to be responsible for the unemployment surges in ", mdx("a", _extends({ parentName: "p" }, { "href": "https://uhero.hawaii.edu/covid-19s-uneven-impact-on-businesses-and-workers-results-from-a-uhero-chamber-of-commerce-hawaii-survey/", "target": "_blank", "rel": "noreferrer" }), "Hawai\u2019i"), ", ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.brookings.edu/blog/the-avenue/2020/06/04/why-covid-19-hit-michigan-so-hard/", "target": "_blank", "rel": "noreferrer" }), "Michigan"), ", and ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.npr.org/sections/coronavirus-live-updates/2020/05/28/864398303/the-sheer-volume-is-hard-to-capture-unemployment-in-nevada-soars-to-historic-hig", "target": "_blank", "rel": "noreferrer" }), "Nevada"), ", states which are home to large tourism, manufacturing, and accommodations industries, respectively. Differences in occupation type correlate strongly with race, and the same Economic Policy Institute ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.epi.org/publication/black-workers-covid/", "target": "_blank", "rel": "noreferrer" }), "report"), " mentioned above covers how Black workers disproportionately serve in vulnerable industries."), mdx("iframe", { width: "100%", height: "637", src: "https://www.epi.org?p=193254&view=embed&embed_template=charts_v2013_08_21&embed_date=20200712&onp=193246&utm_source=epi_press&utm_medium=chart_embed&utm_campaign=charts_v2", frameBorder: "0" }), mdx("h3", { "id": "incarceration" }, "Incarceration"), mdx("p", null, "Lastly, the extreme per-capita spikes appearing in rural ", mdx("a", _extends({ parentName: "p" }, { "href": "https://wreg.com/news/small-arkansas-county-dealing-with-rise-in-covid-19-cases/", "target": "_blank", "rel": "noreferrer" }), "Arkansas"), ", ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.marionstar.com/story/news/local/2020/04/25/marion-prison-ohio-coronavirus-outbreak-seeping-into-larger-community/3026133001/", "target": "_blank", "rel": "noreferrer" }), "Ohio"), ", and ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.wkrn.com/community/health/coronavirus/trousdale-county-leads-us-in-virus-cases-per-capita-due-to-prison/", "target": "_blank", "rel": "noreferrer" }), "Tennessee"), " in the first map happen to be in counties home to large prisons. Coronavirus has posed a profound threat to the incarcerated, a threat which ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.prisonpolicy.org/virus/virusresponse.html", "target": "_blank", "rel": "noreferrer" }), "some policy officials"), " are taking steps to counteract. Hear & read the statements from the inmates of one Maryland jail afflicted by coronavirus ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.gaspingforjustice.org/", "target": "_blank", "rel": "noreferrer" }), "here"), "."), mdx("p", null, "~"), mdx("p", null, "This is only an abbreviated overview of the dynamics at play here, and each of them could be the subject of countless studies. Together, they create an image of a self-sustaining crisis with intricate cause-&-effect relationships. The diagram below summarizes our Lab\u2019s thoughts on how these pandemic vectors are interdependent. In it we also attempt to show the difficulties in reconciling data of phenomena occurring at different spatial scales: regional and local."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1280px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "56.25%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/6f97a1cf2e76fa2378e671dd0919c0d8/4b075/covid-19-theoretical-framework_20200624.webp 1280w"], "sizes": "(max-width: 1280px) 100vw, 1280px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/6f97a1cf2e76fa2378e671dd0919c0d8/26c69/covid-19-theoretical-framework_20200624.png 1280w"], "sizes": "(max-width: 1280px) 100vw, 1280px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/6f97a1cf2e76fa2378e671dd0919c0d8/26c69/covid-19-theoretical-framework_20200624.png", "alt": "Diagram of relationships between coronavirus, occupation type, new unemployment, and race/income.", "title": "Diagram of relationships between coronavirus, occupation type, new unemployment, and race/income.", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n "))); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/unpacking-covid-19-publication-research-themes-and-urban-indications-(part-ii)https://blog.civicdatadesignlab.mit.edu/unpacking-covid-19-publication-research-themes-and-urban-indications-(part-ii)Wed, 01 Jul 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "Unpacking COVID-19 publication research themes and urban indications (Part II)", "author": "Yuan Lai", "date": "2020-07-01T00:00:00.000Z", "tags": ["Covid", "Visualization", "Data", "Health"], "hero": "images/network_overlay_100.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "My ", mdx("a", _extends({ parentName: "p" }, { "href": "https://blog.civicdatadesignlab.mit.edu/unpacking-covid-19-publication-research-themes-and-urban-indications-(part-i)", "target": "_blank", "rel": "noreferrer" }), "previous blog post"), " explored ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge", "target": "_blank", "rel": "noreferrer" }), "COVID-19 AI OPEN Research Dataset Challenge"), " manuscript data and topic modeling technique for thematic structure discovery. This exploratory data analysis (EDA) identified three major themes: epidemiology, virology, clinical studies, and unidentified topics. Using this output, we built a ", mdx("a", _extends({ parentName: "p" }, { "href": "https://public.tableau.com/profile/yuan5273#!/vizhome/COVID-19OpenResearchViz/Dashboard1", "target": "_blank", "rel": "noreferrer" }), "dashboard"), " to visualize manuscripts grouped by country with the quantified thematic composition (in percentage) for exploring scientific research. The topic modeling results revealed several insights: of all the manuscripts, 36% were related to \u201Cclinical\u201D studies (looking for a treatment), 27% to \u201Cvirological\u201D studies (understanding the biology of the virus), 19% to \u201Cepidemiological\u201D studies (understanding the spread of the virus), and 18% to other research studies"), mdx("p", null, "Quantifying and visualizing the current COVID-19 research landscape is interesting, but how does it reflect non-clinical factors and intersect with urban science? To explore this question, we used cleaned abstract text data to further analyze the co-occurrence of keywords that are relevant to urban science. The formation of this keyword dictionary proceeds in two steps. First, we examined the top words ranked by their appearance in the abstracts of every paper. These words are the most common terms that are related to identified themes. Because \u201Cepidemiological\u201D studies relate most to urban science and that urban science may represent a small portion of the entire COVID-19 research scope, we then added urban-related vocabulary into the dictionary, such as \u201Cplanning\u201D, \u201Chousing\u201D, \u201Csocioeconomic\u201D, and \u201Ctransportation\u201D. By analyzing the inter-correlation between these two groups of words, we may understand how urban-related terms appear in scientific research manuscripts and underlying interconnections between health and urban factors."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1392px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "61.7816091954023%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/79727b32404579b5d040c905b7639e36/a8feb/screen-shot-2020-07-01-at-7.34.01-pm.webp 1392w"], "sizes": "(max-width: 1392px) 100vw, 1392px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/79727b32404579b5d040c905b7639e36/3baca/screen-shot-2020-07-01-at-7.34.01-pm.png 1392w"], "sizes": "(max-width: 1392px) 100vw, 1392px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/79727b32404579b5d040c905b7639e36/3baca/screen-shot-2020-07-01-at-7.34.01-pm.png", "alt": "screen shot 2020 07 01 at 7 34 01 pm", "title": "Figure 1. A dictionary with COVID-19 and urban-related vocabulary.", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "With this dictionary, we then used network analysis to investigate how these terms are connected. The concept of text network analysis derives from graph theory. By associating pairs of words based on their co-appearance in a text file, network visualizations reveal complex structures and underlying patterns. Computationally, we used Sklearn in Python to convert the cleaned corpus (from Part I) into a matrix of token counts. The first output is aNbyNmatrix, where N is the number of unique vocabulary extracted from all texts. Because this matrix will be huge when analyzing thousands of abstracts a subset was extracted which represents the matrix related to the defined dictionary for the final graph visualization."), mdx("p", null, mdx("img", _extends({ parentName: "p" }, { "src": "/5523373a1b88423a1de05084c8be844c/network_growth_animation.gif", "alt": null, "title": "Figure 2. An animated COVID-19 text network visualization." }))), mdx("p", null, "Now let\u2019s look into this graph step-by-step based on the rendering completeness of graph representation. In general, graph rendering with lower completeness highlights a fundamental structure of the graph. Increasing rendering completeness extends the network complexity by including more nuanced nodes and edges. The weight of an edge, visualized as the width, indicates the prevalence of co-occurrence between any two terms. A graph with 20% completeness reveals core connections (or research interests) focusing on infection and patient, as well as some clinical and epidemiological research compared to \u201CSARS\u201D and \u201CInfluenza.\u201D Multiple urban-related terms are included here, such as \u201Cpoverty\u201D, \u201Cjob\u201D, \u201Cmobility\u201D, and \u201Cpolicy\u201D. Some terms such as design and response, may carry different urban or clinical meanings depending on specific context."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "2212px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "59.76491862567812%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/bdbc0523df844521267497d91108bfa2/8c820/network_overlay_20.webp 2212w"], "sizes": "(max-width: 2212px) 100vw, 2212px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/bdbc0523df844521267497d91108bfa2/91fce/network_overlay_20.png 2212w"], "sizes": "(max-width: 2212px) 100vw, 2212px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/bdbc0523df844521267497d91108bfa2/91fce/network_overlay_20.png", "alt": "network overlay 20", "title": "Figure 3. COVID-19 text network visualization with 20% completeness.", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "The graph with 60% completeness shows how different health conditions relate to other health, behavioral, socioeconomic, and policy factors. Some additional words indicate socioeconomic vulnerability, such as \u201Csocial, \u201Cincome,\u201D \u201Cjob,\u201D \u201Cinsurance,\u201D and \u201Cunemployment \u201CAsthma,\u201D one of the top respiratory pre-existing conditions, is associated with indoor living quality, air pollution, and insurance status. \u201CObesity,\u201D as another comorbidity found in COVID-19 patients, is associated with \u201Cdrinking,\u201D \u201Cunemployment,\u201D \u201Cpoverty,\u201D and \u201Cmobility.\u201D"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "2212px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "59.76491862567812%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/337aaa6dca07abdc2e5396bc5adb7476/8c820/network_overlay_60.webp 2212w"], "sizes": "(max-width: 2212px) 100vw, 2212px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/337aaa6dca07abdc2e5396bc5adb7476/91fce/network_overlay_60.png 2212w"], "sizes": "(max-width: 2212px) 100vw, 2212px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/337aaa6dca07abdc2e5396bc5adb7476/91fce/network_overlay_60.png", "alt": "network overlay 60", "title": "Figure 4. COVID-19 text network visualization with 60% completeness.", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "The graph with 100% completeness reveals broader racial disparities and racism during this pandemic. ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.scientificamerican.com/article/why-racism-not-race-is-a-risk-factor-for-dying-of-covid-191/", "target": "_blank", "rel": "noreferrer" }), "A recent interview with public health specialist and physician Camara Phyllis Jones"), " revealed that occupations, communities, and health care leave people of color, especially Black Americans, more exposed and less protected. The data visualizations we produced resonate with her insights on systemic racism and its impact on almost every aspect of peoples\u2019 lives. The words\u201Cneighborhood,\u201D \u201Cenvironment,\u201D \u201Chospital,\u201D \u201Cgovernment \u201Cmobility,\u201D \u201Cplanning,\u201D and \u201Cdesign\u201D imply inequities in community resources and living conditions. Others imply population health disparities, especially in pre-existing conditions such as \u201Cobesity,\u201D \u201Cdiabetes,\u201D and \u201Casthma.\u201D The Asian population, on the other hand, is associated explicitly with ", mdx("a", _extends({ parentName: "p" }, { "href": "https://items.ssrc.org/covid-19-and-the-social-sciences/the-rise-of-anti-asian-hate-in-the-wake-of-covid-19/", "target": "_blank", "rel": "noreferrer" }), "racial discimination, negative sentiments on social media, and hate crimes"), ". This indicates that people of color in different ethnic groups may face different discimination and injustice. Additional words may look nuanced in the entire graph, but represent specific population groups, such as \u201Chomeless\u201D and frontline\u201D workers."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "2212px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "59.76491862567812%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/0904d0c65999b35e30602cc297e3d935/8c820/network_overlay_100.webp 2212w"], "sizes": "(max-width: 2212px) 100vw, 2212px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/0904d0c65999b35e30602cc297e3d935/91fce/network_overlay_100.png 2212w"], "sizes": "(max-width: 2212px) 100vw, 2212px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/0904d0c65999b35e30602cc297e3d935/91fce/network_overlay_100.png", "alt": "network overlay 100", "title": "Figure 5. COVID-19 text network visualization with 100% completeness.", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "While the goal of this study was to unpack the complex research landscape, there are limitations when considering data sources as a collection of scientific research manuscripts. This partially explains why we see relatively weak links among the terms representing social, economic, demographic, and political factors. To some extent, we suspect that broader social science studies on COVID-19 may be underrepresented in the original pool of manuscripts and preprints. Novel information mining, data analytics, and knowledge discovery methods are needed for more in-depth investigation for more comprehensive understandings in this pandemic."), mdx("p", null, "In conclusion, this project used Natural Language Processing and topic modeling to identify the underlying thematic structure based on approximate 47,731 manuscripts. Network analysis and visualization of keywords further revealed the intertwined virological, clinical, and epidemiological complexities involving the spread, infection, and mortality of this infectious disease. Using text mining and graph theory, we built a network of critical terms that are relevant to both this pandemic and urban science. During this pandemic, while different groups of people may face different hardships and threats, the underlying network structure and patterns of academic manuscripts reveal prevalent inequalities and injustice that contribute to disease vulnerability and exposure risks."), mdx("p", null, mdx("strong", { parentName: "p" }, "References")), mdx("p", null, "Brainard, J., 2020. New tools aim to tame pandemic paper tsunami.Science. Available: ", mdx("a", _extends({ parentName: "p" }, { "href": "https://science.sciencemag.org/content/368/6494/924/tab-pdf", "target": "_blank", "rel": "noreferrer" }), "https://science.sciencemag.org/content/368/6494/924/tab-pdf")), mdx("p", null, "Feagin, J. and Bennefield, Z., 2014. Systemic racism and US health care.Social science & medicine, 103, pp.7-14."), mdx("p", null, "Gray, D.M., Anyane-Yeboa, A., Balzora, S., Issaka, R.B. and May, F.P., 2020. COVID-19 and the other pandemic: populations made vulnerable by systemic inequity.Nature Reviews Gastroenterology & Hepatology, pp.1-3."), mdx("p", null, "Hutson, M. 2020. Artificial-intelligence tools aim to tame the coronavirus literature.Nature. Available: ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.nature.com/articles/d41586-020-01733-7", "target": "_blank", "rel": "noreferrer" }), "https://www.nature.com/articles/d41586-020-01733-7")), mdx("p", null, "Laster Pirtle, W.N., 2020. Racial capitalism: a fundamental cause of novel coronavirus (COVID-19) pandemic inequities in the United States. Health Education & Behavior, p.1090198120922942."), mdx("p", null, "Lee, J. and Yadav M. 2020. The Rise of Anti-Asian Hate in the Wake of Covid-19. Insights from the Social Sciences. Available: ", mdx("a", _extends({ parentName: "p" }, { "href": "https://items.ssrc.org/covid-19-and-the-social-sciences/the-rise-of-anti-asian-hate-in-the-wake-of-covid-19/", "target": "_blank", "rel": "noreferrer" }), "https://items.ssrc.org/covid-19-and-the-social-sciences/the-rise-of-anti-asian-hate-in-the-wake-of-covid-19/"))); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/meet-the-urops!https://blog.civicdatadesignlab.mit.edu/meet-the-urops!Fri, 26 Jun 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "Meet the UROPs!", "author": "Evan Denmark", "date": "2020-06-26T00:00:00.000Z", "excerpt": "We have four outstanding undergrads tackling missing data this summer. Check out their upcoming work.", "tags": ["General"], "hero": "images/urop-1-1-600x244.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, mdx("em", { parentName: "p" }, "The Missing Data Project brings together the investigative storytelling and technical skills of MIT\u2019s Civic Data Design Lab. Throughout the summer and beyond, we will have lab members as well as external experts contribute to projects that highlight missing data. This summer, we have four current and recent undergraduate students developing their own projects and we\u2019ve asked them to introduce themselves and their summer goals.")), mdx("p", null, mdx("strong", { parentName: "p" }, "Joyce Zhao")), mdx("p", null, "Hi it\u2019s Joyce! I just graduated from Wellesley College, and I\u2019m looking forward to working with the CDDL this summer. I\u2019ve been examining the intersections of housing insecurity, gentrification, and COVID-19 within the city of San Francisco, California. Despite a moratorium, eviction notices are still being filed, prompting me to wonder who is still at risk of eviction, where these evictions occur, and how the city responds in the midst of a pandemic. From initial observations, it\u2019s clear that some neighborhoods in San Francisco have been hit harder by COVID-19 than others. In this project, I hope to use data to spark conversations and raise questions about the relationship between COVID-19 and housing injustice, as well as engage with work done by advocacy groups and organizers in San Francisco."), mdx("p", null, mdx("strong", { parentName: "p" }, "Brian Williams")), mdx("p", null, "Hey! I\u2019m an aspiring protein engineer, who\u2019s actively looking for ways to uplift marginalized communities everywhere. I am a rising junior at MIT, majoring in Biological Engineering and minoring in Black Studies, and am looking forward to working in the Civic Data Design Lab this summer! So far, I have been analyzing and visualizing data reports on racial and ethnic data tied to COVID-19 test results and related deaths. In this project, I\u2019m hoping to explain the confusing (and not so surprising) trends in the data and shed light on the reasons why discrepancies exist between states when reporting race in COVID-19 tests and deaths. Why is there missing data here, and what does this tell us? About testing strategies? About practicality? About policy?"), mdx("p", null, "I am passionate about understanding how policy decisions drive economic and social change. As an avid creative, I love to model and write poetry, and I play varsity baseball at MIT."), mdx("p", null, mdx("strong", { parentName: "p" }, "Amy Fang")), mdx("p", null, "I graduated from MIT in 2020, majoring in Mechanical Engineering and minoring in Anthropology and Design. In my semester, I took my first urban planning class,11.158 Behavior and Policy: Connections in Transportation, which has catalyzed a newfound fascination for all things behaviorally irrational (yet undeniably human) and excessively complicated. During this time, I became engaged to NUMTOT in a whirlwind romance, who helped her expose the inequitable safety misperceptions of shared micromobility."), mdx("p", null, "Today, I fulfill my insatiable hunger for urban planning through mouthwatering memes, palatable PDF readings from classes I will never take, and spicy opportunities like the Missing Data Project. My first project investigates contradictory policies on the county, state, and national levels, specifically in the realm of everyday COVID-19 public safety practices. Just like my \u201Cmissing\u201D data sources, she thrives in ambiguity."), mdx("p", null, mdx("strong", { parentName: "p" }, "Yu Jing Chen")), mdx("p", null, "Hi! I\u2019m a rising junior at MIT studying 11-6 (Urban Studies and Planning with Computer Science) and considering a minor in Entrepreneurship and Innovation!On campus, I can be found pretty much everywhere\u2014 I\u2019m beyond grateful to have found community in various corners of campus, all the while forging space and platforms to empower those that are traditionally unheard across campus."), mdx("p", null, "Regarding my project, if there\u2019s one thing to know about COVID-19, it\u2019s that it does not discriminate. Yet when looking at the statistics, the numbers seem to tell a different story. For indigenous communities, COVID-19 has become all too familiar, with Navajo Nation surpassing New York\u2014once the epicenter of the pandemic\u2014in positive per-capita cases. Through this project, I hope to dive into the different reasons why this is the case and mapping out the reality in order to read into the disparities that exist. From water hotspots to the availability of public infrastructure, this data can be used to help shape conclusions, but should never be taken out of context of any qualitative research or community engagement. At the end of the day, by overlaying the population breakdown and spread across Navajo Nation into this analysis, I hope to shed light on any systemic factors and patterns that may come to explain the adverse situation many indigenous communities have found themselves in during this global pandemic."), mdx("p", null, "If not spending time pondering questions like these, I love to occupy my time exploring free events around my city or knitting to some quality Spotify playlists and am always looking for new songs to add!")); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/unpacking-covid-19-publication-research-themes-and-urban-indications-(part-i)https://blog.civicdatadesignlab.mit.edu/unpacking-covid-19-publication-research-themes-and-urban-indications-(part-i)Fri, 19 Jun 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "Unpacking COVID-19 publication research themes and urban indications (Part I)", "author": "Yuan Lai", "date": "2020-06-19T00:00:00.000Z", "tags": ["Covid", "Data", "Visualization"], "hero": "images/dashboard.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "The ongoing COVID-19 pandemic brings both deep and broad impacts worldwide, calling for all research efforts to tackle the uncertainty and urgency involving the novel virus. With unknown pathogens, epidemiological characteristics, and transmission patterns, the new virus (SARS-CoV-2) inevitably brings inconsistency, discrepancies, and debates among the scientific community. Additionally, the rapid transmission speed and large scale of the infected population requires timely responses despite the above uncertainties."), mdx("h2", { "id": "processing-covid-19-manuscripts-metadata" }, "Processing COVID-19 manuscripts metadata"), mdx("p", null, "On March 17th, the White House Office of Science and Technology Policy launched a ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge", "target": "_blank", "rel": "noreferrer" }), "COVID-19 AI OPEN Research Dataset Challenge"), " in a partnership with Allen Institute for Artificial Intelligence (AI2), the Chan Zuckerberg Initiative, Microsoft Research, Georgetown University\u2019s Center for Security and Emerging Technology, and National Institutes of Health. Hosted on ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.kaggle.com/", "target": "_blank", "rel": "noreferrer" }), "Kaggle"), ", an online community of data scientists and machine learning experts owned by Google, the published dataset contains more than 29,000 research articles (over 13,000 with full text) on SARS-CoV-2 and COVID-19 clinical studies, public health response, population characteristics, and epidemiology. Each publication has been parsed into separate JSON files with its metadata, authors\u2019 information, abstract, and full manuscript."), mdx("p", null, "One core challenge is the utilization of data science and machine learning for better collection, organization, and audition of surging manuscripts. This data exploration aims to unpack the domains and progression of COVID-19 related studies by unpacking currently available research publications. We adopt both quantitative and qualitative methods from computer science and urban planning with the goal of stimulating interdisciplinary discussion and research collaboration and supporting more inclusive approaches to address some immediate and long-term problems. We first summarize a retrospective overview of COVID-19 research challenges and progression in the data science community through this Kaggle challenge. Text analysis and visualization identify several key findings and critical factors that are highly relevant to COVID-19."), mdx("h2", { "id": "quantifying-covid-19-research-thematic-structure" }, "Quantifying COVID-19 research thematic structure"), mdx("p", null, "This study proceeds in the following steps. First, we explore the entire collection of PDF-parsed datasets to understand the COVID-19 research landscape. To do this, we establish a pipeline to process these publications\u2019 metadata and abstracts from an extensive collection of JSON files (n=47,731) in a Python environment. Each file includes key information such as the title, number of authors, authors\u2019 origin (country), and a full text of the abstract, extracted from its associated research article. We further clean the abstract text data (e.g., removing the stop words, lemmatization, vectorization) to generate a descriptive summary of popular words (single word, bi-gram, and tri-gram), number of authors, and origin (by the first author\u2019s country). Using ", mdx("a", _extends({ parentName: "p" }, { "href": "https://en.wikipedia.org/wiki/Topic_model", "target": "_blank", "rel": "noreferrer" }), "LDA topic modeling"), " techniques, we train a model with the processed texts to discover underlying topic groups and corresponding keywords."), mdx("p", null, "We expect to identify not only major research interests but also a small subset of publications that may relate to urban science through this exploratory analysis. To find the latter subset, we filter abstract texts based on a list of urban science-related vocabulary, such as \u201Curban planning\u201D, \u201Cpublic health\u201D, \u201Cenvironment\u201D, \u201Csocial distancing\u201D, \u201Ctransportation\u201D, \u201Cmobility\u201D, \u201Chousing\u201D, \u201Ccommunity\u201D, and \u201Crace\u201D. This process extracts a subset (n=3914) from all manuscripts to further identify publications that may relate to cities and urban science. Analyzing urban-related articles will provide more detailed insights into current research interests and key findings relevant to planning, policy, and operation in cities. Using topic modeling technique, we quantify each article\u2019s thematic composition with four components:"), mdx("ol", null, mdx("li", { parentName: "ol" }, mdx("strong", { parentName: "li" }, "Epidemiological"), " research on infection prevention and control, including the effectiveness of different response strategies and public health measures, such as quarantine, community contact reduction, travel restriction, social distancing at school and workplaces, personal protective equipment (PPE), and public health digital surveillance. Popular terms representing this theme include\u201Cpublic\u201D, \u201Coutbreak\u201D, \u201Cpandemic\u201D, \u201Csocial\u201D, \u201Cepidemic\u201D, \u201Cspread\u201D, \u201Cpopulation\u201D, \u201Ctransmission\u201D, \u201Cglobal\u201D, \u201Cdistancing\u201D, \u201Cresponse\u201D, etc. We consider this is the most relevant theme for urban science."), mdx("li", { parentName: "ol" }, mdx("strong", { parentName: "li" }, "Virological"), " research on SARS-CoV-2, including its genetic sequence, origin, evolution, and genomic differences by geography, transmission, incubation, mutation, and stability in various environments. This also includes material studies on viral shedding from humans (stool, urine, blood, nasal discharge), the persistence of virus on different surface material (e,g., copper, stainless steel, plastic), the virus\u2019 susceptibility to cleaning or disinfecting agents, the physical science of the virus spread, and decontamination mechanics as well as virus transmission patterns involving seasonality, environment (e.g., humidity, temperature), community spread, and asymptomatic transmission during incubation. Popular terms representing this theme include\u201Ccell\u201D, \u201Cprotein\u201D, \u201Cvirus\u201D, \u201Chost\u201D, \u201Cimmune\u201D, \u201Cintracellular\u201D, \u201Cgene\u201D, \u201Cantiviral\u201D, \u201Creplication\u201D, etc."), mdx("li", { parentName: "ol" }, mdx("strong", { parentName: "li" }, "Clinical"), " trials and medical evidence for therapeutic interventions including the efficacy of treatment, or diagnostic findings on infected patients and antibody testing. This also includes patient descriptions, virus incubation period, length of hospital stay, and asymptomatic likelihood. Studies regarding high-risk patient groups with a medical history and pre-existing conditions, such as hypertension, diabetes, heart disease, cardio and cerebrovascular diseases, respiratory diseases are included. Popular terms representing this theme include\u201Csars\u201D, \u201Cinfluenza\u201D, \u201Cmers\u201D, \u201Cpatient\u201D, \u201Cacute\u201D, \u201Cclinical\u201D, \u201Cpathogen\u201D, \u201Csyndrome\u201D, etc."), mdx("li", { parentName: "ol" }, mdx("strong", { parentName: "li" }, "Others"), " represent miscellaneous themes besides the above three.")), mdx("iframe", { src: "https://public.tableau.com/views/COVID-19OpenResearchViz/Dashboard1?:showVizHome=no&:embed=true", width: "100%", height: "600", allowFullScreen: true }), mdx("h2", { "id": "visualizing-urban-related-manuscripts" }, "Visualizing urban-related manuscripts"), mdx("p", null, "The ", mdx("a", _extends({ parentName: "p" }, { "href": "https://public.tableau.com/views/COVID-19OpenResearchViz/Dashboard1?:language=en&:display_count=y&:toolbar=n&:origin=viz_share_link", "target": "_blank", "rel": "noreferrer" }), "interactive dashboard"), " enables users to quickly browse urban-related manuscripts in different countries, sorted by relevance to epidemiology. In the next post, we will discuss two questions related to urban science:"), mdx("ol", null, mdx("li", { parentName: "ol" }, "How do epidemiological research findings on virus transmission and community spread indicate the new norm of urban life?"), mdx("li", { parentName: "ol" }, "How does social science contribute to COVID-19 research, especially when addressing unexpected conflicts and controversies involving socioeconomic equity, environmental justice, data ethics, and policy fairness?")), mdx("p", null, "Since the beginning of the pandemic, we have witnessed ", mdx("a", _extends({ parentName: "p" }, { "href": "https://nationalpost.com/news/a-matter-of-trust-covid-19-pandemic-has-tested-public-confidence-in-science-like-never-before", "target": "_blank", "rel": "noreferrer" }), "debates and mistrust in science amid this pandemic"), ", ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(20)30249-7/fulltext", "target": "_blank", "rel": "noreferrer" }), "revealing injustice,"), " ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.npr.org/sections/health-shots/2020/04/21/838763690/opinion-u-s-must-avoid-building-racial-bias-into-covid-19-emergency-guidance", "target": "_blank", "rel": "noreferrer" }), "biases"), ", and ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(20)30191-1/fulltext", "target": "_blank", "rel": "noreferrer" }), "uncertainty"), " in treatment, testing, policy, and public services. In MIT Course 11-6 (", mdx("a", _extends({ parentName: "p" }, { "href": "https://urban-science.mit.edu/", "target": "_blank", "rel": "noreferrer" }), "Urban Science and Planning with Computer Science"), "), we believe in the importance of addressing broader social, environmental, and political challenges at an urban scale through both technology and planning methods. In Part II, we will further discuss how cities and urban science experts can integrate scientific insights with action and further contribute to collective research, as well as note the impact of potential missing data on under-represented population groups. We hope this data visualization can support researchers who are interested in cities and further connect scientific insights with local community actions.")); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/the-importance-of-the-"starting-point"-in-tracking-covid-by-regionhttps://blog.civicdatadesignlab.mit.edu/the-importance-of-the-"starting-point"-in-tracking-covid-by-regionMon, 15 Jun 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "The Importance of the \"Starting Point\" in Tracking COVID by Region", "author": "Griffin Kantz", "date": "2020-06-15T00:00:00.000Z", "excerpt": "", "tags": ["Covid", "Data", "Visualization"], "hero": "images/covid-19-critical-mass_graphic.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "When comparing how different regions have been impacted by the coronavirus over time, it is important to define a \u201Cstarting point\u201D: an early timepoint in the spread of the virus from which the timepoints of future observations can be measured. Although one may think that the natural place to start would be a region\u2019s first recorded COVID case or the first COVID fatality, this can lead to improper or uninformative region-to-region comparisons. Testing at the beginning of a local COVID outbreak can be seriously unreliable, and the spread of the disease from the first handful of cases to the next is tied to the ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.cdc.gov/mmwr/volumes/69/wr/mm6915e1.htm", "target": "_blank", "rel": "noreferrer" }), "idiosyncrasies of people\u2019s daily behaviors"), "."), mdx("p", null, "In New York state, where COVID ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.cdc.gov/mmwr/volumes/69/wr/mm6922e1.htm?s_cid=mm6922e1_w", "target": "_blank", "rel": "noreferrer" }), "may have arrived more than one month before the first confirmed case"), " on March 1, 2020, confirmed fatalities soared from 1 death to nearly 500 in just two weeks. Conversely, the Norfolk-Newport News area of Virginia had still not witnessed ten deaths one month after its first. "), mdx("p", null, "COVID metrics between different afflicted regions appear to begin behaving in a more predictable manner once the disease has reached a critical mass and begun spreading widely. COVID data analysts try to set the starting point for their measurements at some level where this critical mass has likely been reached and the initial random variation has dissipated."), mdx("p", null, "Different modelers will choose different thresholds. The ", mdx("em", { parentName: "p" }, mdx("a", _extends({ parentName: "em" }, { "href": "https://ig.ft.com/coronavirus-chart/?areas=usa&areas=gbr&cumulative=0&logScale=1&perMillion=0&values=deaths", "target": "_blank", "rel": "noreferrer" }), "Financial Times")), " measures new cases or deaths by country from the day 10 cases/day or 3 deaths/day was reached, and cumulative cases/deaths from the day of the 100th case or death. The ", mdx("a", _extends({ parentName: "p" }, { "href": "http://91-divoc.com/pages/covid-visualization/", "target": "_blank", "rel": "noreferrer" }), "data visualization at 91-divoc.com"), " measures by nation from the day of the 100th case or the 10th death; for regions, it starts at 20 cases or 5 deaths."), mdx("h2", { "id": "what-threshold-makes-the-most-sense" }, "What threshold makes the most sense?"), mdx("p", null, "In our analysis, we examined the ", mdx("a", _extends({ parentName: "p" }, { "href": "https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series", "target": "_blank", "rel": "noreferrer" }), "Johns Hopkins CSSE time series dataset"), " (up to 5/31/20) to determine how varying the choice of starting point affects the precision of forecasts. We chose to analyze counts of confirmed COVID fatalities, which are more reliable early-stage figures than confirmed COVID cases. (However, deaths always lag cases by up to two weeks.)"), mdx("p", null, "First, we grouped U.S. county-level COVID death counts into the top 100 most populous Census metropolitan statistical areas (MSAs), which adhere to county boundaries. By population, the largest of these is New York-Newark-Jersey City and the smallest is Chattanooga."), mdx("p", null, "Next, we shifted the daily figures for the MSAs to synchronize the two weeks before and two weeks after the day ", mdx("em", { parentName: "p" }, "X"), " total deaths was reached, where ", mdx("em", { parentName: "p" }, "X"), " = 1, 2, 5, 10, 20, 50, 100, 200, 500, 1,000, and 2,000."), mdx("p", null, mdx("em", { parentName: "p" }, "(Why these amounts? Contagions spread exponentially, and these numbers break the span from 1 to 2,000 into roughly equal logarithmic intervals. As of today, only three MSAs have reached 5,000 total deaths. And why two weeks? That is roughly the duration of a COVID infection.)")), mdx("p", null, "To determine the efficacy of each threshold of ", mdx("em", { parentName: "p" }, "X"), ", we created regressions for the \u201Cbefore\u201D and \u201Cafter\u201D weeks, based on the linear formula and its exponential transformation shown below:"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1375px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "14.399999999999999%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAADCAYAAACTWi8uAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAAaUlEQVQI12WOSwqAMAwF041oF/VXFcSVqAsP1d7/Fk4gBdHCMJS+vFSEk3MWc51SuvEFB+wwGBus9hahJeve8/Ipc9DARDDgQikMttBjb65e879CDZwK4RF3Wg4L9xkrvWWD/TiWjtLzABCJOGODeCV/AAAAAElFTkSuQmCC')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/c6ac1b1d9779498361dbb02cc978e5f5/0fffa/covid-19-critical-mass_equations.webp 1375w"], "sizes": "(max-width: 1375px) 100vw, 1375px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/c6ac1b1d9779498361dbb02cc978e5f5/8ff9b/covid-19-critical-mass_equations.png 1375w"], "sizes": "(max-width: 1375px) 100vw, 1375px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/c6ac1b1d9779498361dbb02cc978e5f5/8ff9b/covid-19-critical-mass_equations.png", "alt": "Equations: log(deaths) = alpha + beta*days + epsilon. Deaths = lambda*e^(beta*days) + epsilon, lambda = e^alpha.", "title": "Equations: log(deaths) = alpha + beta*days + epsilon. Deaths = lambda*e^(beta*days) + epsilon, lambda = e^alpha.", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, mdx("em", { parentName: "p" }, "(Here,"), " \u03B1 ", mdx("em", { parentName: "p" }, "and"), " \u03B2 ", mdx("em", { parentName: "p" }, "are the y-intercept and slope of the regression and"), " \u03B5 ", mdx("em", { parentName: "p" }, "is the residual error.)")), mdx("p", null, "Below in Chart 1, see an interactive graph illustrating the data for ", mdx("em", { parentName: "p" }, "X"), " = 1 death (the day of the first recorded COVID death in each MSA)."), mdx("iframe", { width: "500", height: "400", frameBorder: "0", scrolling: "no", src: "//plotly.com/~GriffinK/3.embed" }), mdx("p", null, mdx("em", { parentName: "p" }, "Chart 1. Rendered in R, ggplot2, and Plotly. Hover over points to see more information.")), mdx("p", null, "When ", mdx("em", { parentName: "p" }, "X"), " = 1, the trajectories on the right (", mdx("em", { parentName: "p" }, "after"), ") side of the graph diverge widely, signifying that this observation point occurs too early in the spread of the virus to meaningfully predict or compare trajectories. Additionally, for this and the next few values of ", mdx("em", { parentName: "p" }, "X"), ", any data that would appear on the left (", mdx("em", { parentName: "p" }, "before"), ") side of the graph are inscrutable, since counts of zero deaths have an infinitesimal logarithmic value and must therefore be discarded."), mdx("p", null, "When moving to higher thresholds of ", mdx("em", { parentName: "p" }, "X"), ", we are able to see the trajectories on the ", mdx("em", { parentName: "p" }, "after"), " side begin to coalesce and the data on the ", mdx("em", { parentName: "p" }, "before"), " side start to grow. For the highest ", mdx("em", { parentName: "p" }, "X"), ", few MSAs have yet reached those death counts, so the trajectories on both sides of the graph are much fewer in number. Here are selected values of ", mdx("em", { parentName: "p" }, "X"), ", visualized:"), mdx("iframe", { width: "500", height: "400", frameBorder: "0", scrolling: "no", src: "//plotly.com/~GriffinK/5.embed" }), mdx("iframe", { width: "500", height: "400", frameBorder: "0", scrolling: "no", src: "//plotly.com/~GriffinK/9.embed" }), mdx("iframe", { width: "500", height: "400", frameBorder: "0", scrolling: "no", src: "//plotly.com/~GriffinK/15.embed" }), mdx("p", null, mdx("em", { parentName: "p" }, "Charts 2-4. Rendered in R, ggplot2, and Plotly. Hover over points to see more information.")), mdx("p", null, "Across all these test values of ", mdx("em", { parentName: "p" }, "X"), ", the mean square error (MSE) of the observations to the ", mdx("em", { parentName: "p" }, "before"), " and ", mdx("em", { parentName: "p" }, "after"), " regression lines is what attracts our attention, more so than the regressions themselves. The MSE allows us to understand how closely the trajectories coalesce around the regression line, and thus around each other. A low MSE implies that the observations fit well around the line, indicating better predictability. For the first few values of ", mdx("em", { parentName: "p" }, "X"), ", the MSE is lower on the ", mdx("em", { parentName: "p" }, "before"), " side than on the ", mdx("em", { parentName: "p" }, "after"), " side. For larger ", mdx("em", { parentName: "p" }, "X"), ", the opposite is true: the ", mdx("em", { parentName: "p" }, "after"), " MSE is lower and the ", mdx("em", { parentName: "p" }, "before"), " MSE is higher."), mdx("p", null, "As ", mdx("em", { parentName: "p" }, "X"), " increases, the point at which the ", mdx("em", { parentName: "p" }, "after"), " MSE becomes less than the ", mdx("em", { parentName: "p" }, "before"), " MSE is where the near future becomes more predictable than the near past. In our data, this point occurs just shy of ", mdx("em", { parentName: "p" }, "X"), " = 50, as visualized in the graph below:"), mdx("iframe", { width: "500", height: "300", frameBorder: "0", scrolling: "no", src: "//plotly.com/~GriffinK/19.embed" }), mdx("p", null, mdx("em", { parentName: "p" }, "Chart 5. Mean square error for each value of"), " X", mdx("em", { parentName: "p" }, ".")), mdx("p", null, "The data shown in this graphic imply that the fatality trajectories have lost most of their early-stage variability around the time of the 50th death. After reaching this threshold, the trajectories behave more consistently \u2014 not totally in lockstep, in fact far from it, but more consistently than at any point before."), mdx("p", null, "The widening difference between the ", mdx("em", { parentName: "p" }, "before"), " and ", mdx("em", { parentName: "p" }, "after"), " MSE curves beyond ", mdx("em", { parentName: "p" }, "X"), " = 50 indicates that 100, 200, or 500 might be even better thresholds, but we must bear in mind the passage of time. The higher we set ", mdx("em", { parentName: "p" }, "X"), " as the starting point, the more precision we gain in modeling the future, but the more data we are willingly discarding. We should not choose a starting point so late in the outbreak that we end up ignoring weeks of mid-phase growth in the fatality count for the sake of a more precise model."), mdx("p", null, "The steep decline in both MSE curves at ", mdx("em", { parentName: "p" }, "X"), " = 1,000 and 2,000 is an artifact of the rarity of those high death counts as of this month (June); data is simply too scarce for these ", mdx("em", { parentName: "p" }, "X"), ". If more regions across the U.S. were suffering COVID fatality rates that severe, we could expect to see the ", mdx("em", { parentName: "p" }, "before"), " MSE curve trend further and further upwards."), mdx("h2", { "id": "what-we-can-learn" }, "What we can learn"), mdx("p", null, "This analysis shows how the growth in COVID fatalities in U.S. urban regions reaches a \u201Ccritical mass\u201D and loses its early-stage variability at some time around the 50th death. Graphing MSE shows that setting a higher starting point for measurements enables greater precision, but this comes with the price of discarding informative data."), mdx("p", null, "This is an important finding for comparative analysis and future COVID time-series data visualizations. Yet, we must caution against overstating the rigor of this analysis. We are not epidemiologists and this is not a professional epidemiological study. We have regressed over the variable of time but not over variables of human behavior or system factors. Hopefully this analysis, albeit rough, can impart some mathematical basis to the assumptions underlying future analysis."), mdx("p", null, "Download our data tables for this post ", mdx("a", _extends({ parentName: "p" }, { "href": "https://github.com/civic-data-design-lab/COVID-critical-mass", "target": "_blank", "rel": "noreferrer" }), "here"), ".")); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/implications-of-missing-data:-gaps-in-covid-19-data-by-race-and-ethnicityhttps://blog.civicdatadesignlab.mit.edu/implications-of-missing-data:-gaps-in-covid-19-data-by-race-and-ethnicityFri, 05 Jun 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "Implications of Missing Data: Gaps in COVID-19 Data by Race & Ethnicity", "author": "Chenab Navalkha", "date": "2020-06-05T00:00:00.000Z", "tags": ["Covid", "Health", "Mapping"], "hero": "images/screen-shot-2020-05-22-at-10.08.27-am.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "The COVID-19 pandemic has highlighted the importance of collecting and reporting data on health outcomes by race and ethnicity in order to quantify the way in which the virus\u2019s impacts fall along existing lines of health and structural inequity. Just this past week, the federal government ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.npr.org/sections/coronavirus-live-updates/2020/06/04/869815033/race-ethnicity-data-to-be-required-with-coronavirus-tests-in-u-s", "target": "_blank", "rel": "noreferrer" }), "announced"), " that it will require data on race and ethnicity to be collected for all COVID-19 tests."), mdx("p", null, "The push for these data sits within a broader conversation about the importance of collecting racial data that has been ongoing among those working toward ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.google.com/url?q=https://www.healthaffairs.org/do/10.1377/hblog20200507.469145/full/?utm_source%3DNewsletter%26utm_medium%3Demail%26utm_content%3DEye%2BOn%2BHealth%2BReform%253A%2BRisk%2BCorridors%252C%2BCOVID-19%252C%2BAnd%2BThe%2BACA%253B%2BCOVID-19%253A%2BFederal%2BFunding%2BFor%2BContact%2BTracing%253B%2BMedicaid%2BMCOs%2BAnd%2BPayment%2BReform%253B%2BInequity%26utm_campaign%3DHAT%2B5-11-20%26&sa=D&ust=1590158619227000&usg=AFQjCNGo8Ae6MN6_8xi21Iua4cbQU62_Yg", "target": "_blank", "rel": "noreferrer" }), "health equity"), " and to mitigate the negative impacts of the ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.google.com/url?q=https://patientengagementhit.com/news/social-determinants-of-health-comorbidities-sway-covid-19-severity&sa=D&ust=1590158619229000&usg=AFQjCNHUbDqYt4PiAPAbP4WYpvXRBwCHVg", "target": "_blank", "rel": "noreferrer" }), "\u201Csocial determinants of health\u201D"), " \u2013 or the place-based conditions that impact the health of individuals and communities. "), mdx("p", null, "Over the last month or so, ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.google.com/url?q=https://www.theatlantic.com/ideas/archive/2020/04/stop-looking-away-race-covid-19-victims/609250/&sa=D&ust=1590158619231000&usg=AFQjCNGvZda446O1AMbKQ8ZIARTVGGXPNw", "target": "_blank", "rel": "noreferrer" }), "more"), " and more articles have been published discussing the disparate impact of COVID-19 on ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.google.com/url?q=https://labblog.uofmhealth.org/rounds/racial-disparities-time-of-covid-19&sa=D&ust=1590158619228000&usg=AFQjCNHWT_KM3b_dszEG9VEUE-ItDnojJA", "target": "_blank", "rel": "noreferrer" }), "African American"), ", ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.washingtonpost.com/national/coronavirus-navajo-nation-crisis/2020/05/11/b2a35c4e-91fe-11ea-a0bc-4e9ad4866d21_story.html", "target": "_blank", "rel": "noreferrer" }), "Indigenous"), ", and ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.google.com/url?q=https://www.nytimes.com/2020/05/07/us/coronavirus-latinos-disparity.html&sa=D&ust=1590158619219000&usg=AFQjCNGB63MElzGWkmKzA6k6VgNhgCAgtA", "target": "_blank", "rel": "noreferrer" }), "Latinx"), " people. These articles bring to light the ways in which existing inequities \u2013 both in terms of access to resources and healthcare, and resulting from historic processes of discrimination such as ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.npr.org/2017/05/03/526655831/a-forgotten-history-of-how-the-u-s-government-segregated-america", "target": "_blank", "rel": "noreferrer" }), "redlining"), " \u2013 are manifesting in COVID-19 infection and mortality rates."), mdx("p", null, "This burgeoning discussion also has brought into clear focus the inconsistencies in how data on race and ethnicity are collected and shared at the state level. Some states have robust data on COVID-19 testing results and death rates that include granular information by race and ethnicity and have made these data easily accessible to the public. Other states are lagging behind. "), mdx("p", null, "One of the clear principles among those working to mitigate health disparities is the need to collect and report race and ethnicity data in order to understand differences in health outcomes before we can determine how to intervene in order to mitigate these inequities. "), mdx("p", null, "Our lab decided to look into how states have been collecting and sharing data on COVID-19 testing and deaths by race. ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.google.com/url?q=https://covidtracking.com/race&sa=D&ust=1590158619219000&usg=AFQjCNESMUbMb9qT-r1q-f8XRMbXnNj73g", "target": "_blank", "rel": "noreferrer" }), "The COVID Tracking Project"), " recently teamed up with the Antiracist Research & Policy Center to aggregate race data by state and to highlight where the gaps are. (Check out their regularly updated racial data tracker ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.google.com/url?q=https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vTfUQPxkhP_CRcGmnnpUBihnTNZ9Z8pcizII4_sc2o2n3opOoAJdAM4CRTJBI339tou8LWnQrqbTMgH/pubhtml%23&sa=D&ust=1590158619220000&usg=AFQjCNEYWrNViMBob1DzXPfjotH7ablwGA", "target": "_blank", "rel": "noreferrer" }), "here"), ".)"), mdx("p", null, "Using their data, we created these maps that show the percent of missing data on race and ethnicity among those who have tested positive for COVID-19, by state. The darker colors indicate states where the race and ethnicity of those who have tested positive for COVID-19 is least known."), mdx("iframe", { id: "carto", title: "Carto Map", src: "https://mit.carto.com/u/chenab/builder/760496a1-4886-40c3-be1c-73ae28cfb2b9/embed", width: "100%", height: "520", allow: "" }), mdx("p", null, mdx("em", { parentName: "p" }, "(Data pulled June 4, 2020 from ", mdx("a", _extends({ parentName: "em" }, { "href": "https://covidtracking.com/race", "target": "_blank", "rel": "noreferrer" }), "https://covidtracking.com/race"), ")")), mdx("iframe", { id: "carto-2", title: "Carto Map Ethnicity", src: "https://mit.carto.com/u/chenab/builder/03122e0c-35fb-4950-be0c-00af2281175c/embed", width: "100%", height: "520", allow: "" }), mdx("p", null, mdx("em", { parentName: "p" }, "(Data pulled June 4, 2020 from ", mdx("a", _extends({ parentName: "em" }, { "href": "https://covidtracking.com/race", "target": "_blank", "rel": "noreferrer" }), "https://covidtracking.com/race"), ")")), mdx("p", null, "It\u2019s important to note, though, that even where testing data and outcomes are reported by race and ethnicity, an outstanding question remains of who is and isn\u2019t being tested. Even the most \u201Ccomplete\u201D datasets don\u2019t answer this more fundamental question. Though the federal government\u2019s new requirement is vital to filling gaps in the data, this question of who is and isn\u2019t tested will persist until testing becomes more ubiquitous."), mdx("p", null, "Understanding where these data are missing is, of course, just one piece of the larger conversation about how the structural features of our society have left some communities more vulnerable to the pandemic than others. If you\u2019re interested, ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.google.com/url?q=https://www.healthaffairs.org/do/10.1377/hblog20200507.469145/full/?utm_source%3DNewsletter%26utm_medium%3Demail%26utm_content%3DEye%2BOn%2BHealth%2BReform%253A%2BRisk%2BCorridors%252C%2BCOVID-19%252C%2BAnd%2BThe%2BACA%253B%2BCOVID-19%253A%2BFederal%2BFunding%2BFor%2BContact%2BTracing%253B%2BMedicaid%2BMCOs%2BAnd%2BPayment%2BReform%253B%2BInequity%26utm_campaign%3DHAT%2B5-11-20%26&sa=D&ust=1590158619230000&usg=AFQjCNE3I8JdtgTHBTX5Z4m1lHlh8LGnBQ", "target": "_blank", "rel": "noreferrer" }), "here"), " is a great piece that delves into how inequity is playing a role as a \u201Cpre-existing condition\u201D in our current healthcare system.")); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/covid-19-tests-and-inequalityhttps://blog.civicdatadesignlab.mit.edu/covid-19-tests-and-inequalityFri, 29 May 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "Covid-19 Tests and Inequality", "author": "Zhuangyuan (Yuan) Fan", "date": "2020-05-29T00:00:00.000Z", "tags": ["General", "Covid"], "hero": "images/image1-01.jpg" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, mdx("em", { parentName: "p" }, "Chart 1. The graph above shows the changes of test per capita and percentage of positive tests returned. The size of the circle indicate the median household income")), mdx("h2", { "id": "introduction" }, "Introduction"), mdx("p", null, mdx("a", _extends({ parentName: "p" }, { "href": "https://www.newyorker.com/magazine/2020/05/04/seattles-leaders-let-scientists-take-the-lead-new-yorks-did-not", "target": "_blank", "rel": "noreferrer" }), "New York City was slow to respond to COVID-19."), " It took the city officials over a month to shift adequate testing resources to areas that were suffering most: New York\u2019s low-income and disproportionately minority neighborhoods and households. "), mdx("p", null, "Many journalists and civil society actors have begun to raise alarm at what is proving to be an economic and social disparity in our governments\u2019 response. For the few state and local governments that have released details data on COVID19 fatalities, researchers on our team begin to observe a clear uneven pattern: low-income communities have been much harder hit by COVID19 than high-income communities. We also know that widespread testing is essential to both helping infected individuals and also containing the virus at a regional level. However, income should not decide whether or not one has access to adequate testing and care. "), mdx("p", null, "Several recent studies have attempted to analyze if state and local testing strategies are disbursed and utilized in more high-income than low-income regions. Two studies, by Borjas, G. J. (2020) and Schmitt-Groh\xE9 (2020) respectively, used zip code level data in New York City. Using data from April 5th, Borjas finds that people residing in poor neighborhoods were less likely to be tested than people residing in rich neighborhoods, while, with data from April 2nd to April 13th, Schmitt-Groh\xE9 finds that the distribution of Covid-19 tests was equal across income brackets. "), mdx("p", null, "\u2018in comparing these two results, it become more evident that a key contributing factor in the differences is ", mdx("strong", { parentName: "p" }, mdx("em", { parentName: "strong" }, "time")), "."), mdx("h2", { "id": "timing-is-everything" }, "Timing is everything."), mdx("p", null, "These confounding conclusions motivated researchers in our lab to explore the relationship of Covid-19 testing, income levels, and time. First, we compare the zip code level testing and confirmed cases data from April 1st to May 4th in New York City", mdx("strong", { parentName: "p" }, "."), " Low-income zip codes show a higher fraction of positive testing results across time. Although the data from early April show a higher concentration of testing in high-income zip codes, this trend reversed itself over time in the last month."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "3000px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "55.00000000000001%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/f4856cf31163d53c693eca57a941f7cc/a3bc4/image2.webp 2500w", "/static/f4856cf31163d53c693eca57a941f7cc/09625/image2.webp 3000w"], "sizes": "(max-width: 3000px) 100vw, 3000px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/f4856cf31163d53c693eca57a941f7cc/412e4/image2.png 2500w", "/static/f4856cf31163d53c693eca57a941f7cc/22748/image2.png 3000w"], "sizes": "(max-width: 3000px) 100vw, 3000px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/f4856cf31163d53c693eca57a941f7cc/22748/image2.png", "alt": "image2", "title": "New York City Median Household Income and % Positive Cases Per Test", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "3000px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "55.00000000000001%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/d854378ad53ac6214d29861cb9253466/a3bc4/image4.webp 2500w", "/static/d854378ad53ac6214d29861cb9253466/09625/image4.webp 3000w"], "sizes": "(max-width: 3000px) 100vw, 3000px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/d854378ad53ac6214d29861cb9253466/412e4/image4.png 2500w", "/static/d854378ad53ac6214d29861cb9253466/22748/image4.png 3000w"], "sizes": "(max-width: 3000px) 100vw, 3000px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/d854378ad53ac6214d29861cb9253466/22748/image4.png", "alt": "image4", "title": "New York City Median Household Income and Test Per Capita ", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "Second, to go one step further we divided the median household income and the testing rate per capita into quintiles. We assigned each quintile a score from 1 to 5: 1 representing the lowest quintile and 5 representing the highest quintile. We then add the testing rate per capita score with the household income score created a map that illuminates where there is disparity between income and testing. A score of 10 represents the highest income and testing rates, while a score of 2 represents lowest income and testing rates. In the map (Graph 4.1) we can see the extreme color of blue and red diminishing between early April and early May. However, there are still places that remain at score 2."), mdx("p", null, "If we plot positive returns per test rate (at zip code level) by quintile against the test per capita by quintile (Graph 4.2), we show a map with dark red indicating very high likelihood of getting a positive return per test but the overall tests per capita remain low. The blue color indicates the zip code has a very positive per test rate but high test per capita rate. These maps uncover the fact that even with the trend reversing, we still have many zip codes that are left behind. For example, as of May 4th, Flushing in Queens (11355) has a positive cases per test rate as high as 46.7% but their overall test per capita is less than 3%. On the contrary, Staten Island (10305) has a positive cases per test rate at 36.9% and its overall test per capita rate is around 7.2%. 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"backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/8f267b3f473b3a575e179b2b67d2afff/62d33/mapforweb2.webp 1574w"], "sizes": "(max-width: 1574px) 100vw, 1574px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/8f267b3f473b3a575e179b2b67d2afff/2c1f1/mapforweb2.png 1574w"], "sizes": "(max-width: 1574px) 100vw, 1574px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/8f267b3f473b3a575e179b2b67d2afff/2c1f1/mapforweb2.png", "alt": "mapforweb2", "title": "Positive per test rate vs. Test per capita", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "There are potentially many factors that contributed to New York\u2019s decisions on where to allocation COVID19 resources. It could be because her healthcare system is just simply responding to the number of confirmed cases. Whatever the reason, we all have an obligation to understand and learn from these failings in order to better inform more equitable designs and plans. Many of us don\u2019t have the luxury of time. So it is left to our planners, policy-makers, and public health officials to ensure that we make the very best of it. To know and never doubt that income should never dictate the acquisition of something so priceless - a life well lived"), mdx("h2", { "id": "reference" }, "Reference:"), mdx("p", null, "[", "1] Borjas, G. J. (2020).", mdx("em", { parentName: "p" }, "Demographic determinants of testing incidence and COVID-19 infections in New York City neighborhoods"), "(No. w26952). National Bureau of Economic Research."), mdx("p", null, "[", "2] Schmitt-Groh\xE9, S., Teoh, K., & Uribe, M. (2020).", mdx("em", { parentName: "p" }, "Covid-19: Testing Inequality in New York City"), "(No. w27019). National Bureau of Economic Research.")); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/creating-best-practices-for-visualizing-covid-datahttps://blog.civicdatadesignlab.mit.edu/creating-best-practices-for-visualizing-covid-dataThu, 21 May 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "Creating Best Practices for Visualizing Covid Data", "author": "Griffin Kantz", "date": "2020-05-21T00:00:00.000Z", "excerpt": "The global importance of the SARS-CoV-2/COVID-19 pandemic, and the salience of eye-catching data visualizations in these times, necessitate a profoundly judicious use of data variables and normalizations. Here is a survey of professional guidance on this topic.", "tags": ["Covid", "Data", "Visualization"], "hero": "images/blog_griffin-k_responsible-covid-data-visualization_header.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "The global importance of the SARS-CoV-2/COVID-19 pandemic, and the salience of eye-catching data visualizations in these times, necessitate a profoundly judicious use of data variables and normalizations. Inappropriate choices in these respects can contribute to misconceptions about the magnitudes of various aspects of the crisis, either in comparison to each other or in comparison to other global issues. Unchecked misconceptions about quantitative data in public health have the potential to enable harm, a hazard which such tags as ", mdx("a", _extends({ parentName: "p" }, { "href": "https://twitter.com/hashtag/datasaveslives?lang=en", "target": "_blank", "rel": "noreferrer" }), "#", "datasaveslives"), " may have the effect of blanketing over."), mdx("p", null, "Epidemiology in particular is wrought with terminology and dynamic relationships which are not immediately intuitive to understand. In the first weeks of COVID-19\u2019s arrival to the English-speaking world, sage precautionary advice in this vein made its way through the data science Twitterverse:"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1342px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "76.15499254843517%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/b4724b12d84759b40ea9b8fb2cab1999/41ea3/screen-shot-2020-05-19-at-6.40.06-am.webp 1342w"], "sizes": "(max-width: 1342px) 100vw, 1342px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/b4724b12d84759b40ea9b8fb2cab1999/9fef8/screen-shot-2020-05-19-at-6.40.06-am.png 1342w"], "sizes": "(max-width: 1342px) 100vw, 1342px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/b4724b12d84759b40ea9b8fb2cab1999/9fef8/screen-shot-2020-05-19-at-6.40.06-am.png", "alt": "Tweet by Nate Silver: \"This is not unique to coronavirus, but it feels like the people who know the *most* about something often express more uncertainty and doubt than people who have some adjacent knowledge but fall short of being subject-matter experts. 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More recent words of wisdom:"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1340px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "108.65671641791046%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": 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with default Excel formatting\"", "title": "tweet3", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "^(Ahem, anyone using a ", mdx("a", _extends({ parentName: "p" }, { "href": "https://twitter.com/bencasselman/status/1258136404363808769", "target": "_blank", "rel": "noreferrer" }), "\u201Ccubic model\u201D"), ".)"), mdx("p", null, "See the ", mdx("a", _extends({ parentName: "p" }, { "href": "https://twitter.com/ferrisjabr/status/1221145299084726273", "target": "_blank", "rel": "noreferrer" }), "thread below"), " and the ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.theatlantic.com/technology/archive/2020/01/china-coronavirus-twitter/605644/", "target": "_blank", "rel": "noreferrer" }), mdx("em", { parentName: "a" }, "Atlantic"), " piece covering the tweet that prompted it"), " for a demonstration of the hazard of well-intentioned but under-informed data analysis."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1328px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "85.09036144578313%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/aaa42fa7f634e3b8184b58478608815f/909b8/screen-shot-2020-05-19-at-6.44.47-am.webp 1328w"], "sizes": "(max-width: 1328px) 100vw, 1328px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/aaa42fa7f634e3b8184b58478608815f/fa900/screen-shot-2020-05-19-at-6.44.47-am.png 1328w"], "sizes": "(max-width: 1328px) 100vw, 1328px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/aaa42fa7f634e3b8184b58478608815f/fa900/screen-shot-2020-05-19-at-6.44.47-am.png", "alt": "Tweet by Ferris Jabr: \"Following numerous critiques, the most inaccurate tweet in the original viral thread disappeared/was probably deleted without explanation or follow-up correction. For transparency and posterity, this is what it looked like. The info in the pictured tweet is unequivocally wrong\" [image of tweet by Dr. Eric Feigl Ding: \"SUMMARY: So what does this mean for the world??? We are now faced with the most virulent virus epidemic the world has ever seen. An R0=3.8 means that it exceeds SARS's modest 0.49 viral attack rate by 7.75x -- almost 8 fold! A virus that spreads 8 times faster than SARS...\"]", "title": "tweet4", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "Even among infectious disease experts, consensus on the best practices for modeling the incoming data would adapt over time (", mdx("a", _extends({ parentName: "p" }, { "href": "https://twitter.com/neil_ferguson/status/1243294815200124928", "target": "_blank", "rel": "noreferrer" }), "thread"), "):"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1324px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "52.87009063444109%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/3d8969f7f0e3203a38c7436bd7f5772c/33b3d/screen-shot-2020-05-19-at-6.45.02-am.webp 1324w"], "sizes": "(max-width: 1324px) 100vw, 1324px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/3d8969f7f0e3203a38c7436bd7f5772c/b58f7/screen-shot-2020-05-19-at-6.45.02-am.png 1324w"], "sizes": "(max-width: 1324px) 100vw, 1324px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/3d8969f7f0e3203a38c7436bd7f5772c/b58f7/screen-shot-2020-05-19-at-6.45.02-am.png", "alt": "Tweet by neil_ferguson: \"1/4 - I think it would be helpful if I cleared up some confusion that has emerged in recent days. Some have interpreted my evidence to a UK parliamentary committee as indicating we have substantially revised our assessments of the potential mortality impact of COVID-19.\"", "title": "tweet5", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "All these epistemological limitations fit under the umbrella of the ", mdx("a", _extends({ parentName: "p" }, { "href": "https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect", "target": "_blank", "rel": "noreferrer" }), "Dunning-Kruger effect"), ":"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "800px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "71.74999999999999%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/jpeg;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/658e085757182365014640f9cc50c084/8d2ea/image1.webp 800w"], "sizes": "(max-width: 800px) 100vw, 800px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/658e085757182365014640f9cc50c084/c60e9/image1.jpg 800w"], "sizes": "(max-width: 800px) 100vw, 800px", "type": "image/jpeg" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/658e085757182365014640f9cc50c084/c60e9/image1.jpg", "alt": "Dunning-Kruger Effect. Diagram showing relationship between knowledge in field and confidence.", "title": "Dunning-Kruger", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, mdx("em", { parentName: "p" }, "Original creator of this diagram unknown.")), mdx("p", null, "Following this evolving consensus on best practices, we can often observe improvements over time in some of the COVID data visualizations which have managed to reach a wider audience, and these revisions are instructive."), mdx("p", null, "For example, ", mdx("a", _extends({ parentName: "p" }, { "href": "https://covidactnow.org/", "target": "_blank", "rel": "noreferrer" }), "covidactnow.org"), " has added models of the infection growth rate (with confidence intervals) and the positive test rate to its forecasts of state-by-state hospital capacity, which were more simplistic ", mdx("a", _extends({ parentName: "p" }, { "href": "https://web.archive.org/web/20200327060650/http:/www.covidactnow.org/state/NY", "target": "_blank", "rel": "noreferrer" }), "in the site\u2019s first few weeks"), "."), mdx("p", null, "The ", mdx("a", _extends({ parentName: "p" }, { "href": "http://91-divoc.com/pages/covid-visualization/", "target": "_blank", "rel": "noreferrer" }), "interactive COVID tool at 91-divoc.com"), " was already making the best of multiple approaches upon its debut, showing gross and per-capita case counts by country and region, and allowing users to toggle between linear and logarithmic scales on the y-axis. Like other popular COVID tools, 91-divoc brackets the x-axes of its graphs around early quantitative thresholds such as ", mdx("em", { parentName: "p" }, "days since 100 cases"), ", and by late April had shifted its default view from total cases to one-week trailing averages of new cases to better illustrate flattening growth. In mid-April the site added forecast trendlines for countries, but days later opted to truncate those forecasts to seven days forward so as to \u201Cavoid extreme extrapolation\u201D. Peruse 91-divoc\u2019s change log ", mdx("a", _extends({ parentName: "p" }, { "href": "http://91-divoc.com/pages/covid-visualization/changes.html", "target": "_blank", "rel": "noreferrer" }), "here"), "."), mdx("p", null, "The most important prevailing debates on best practices for COVID data visualization concern the proper selection of variables and denominators."), mdx("p", null, "The modeler\u2019s choice between gross counts and per-capita normalizations depends on the purpose of their model. Gross counts accurately measure the growth of local outbreaks, whereas per-capita rates better depict the burden on a nation/region\u2019s healthcare system and policymaking apparatus. Some sentiments in favor of per-capita normalizations are ", mdx("a", _extends({ parentName: "p" }, { "href": "https://twitter.com/NateSilver538/status/1245132431818178561", "target": "_blank", "rel": "noreferrer" }), "quite inflexble"), ", but perhaps wrongly so. Observe the linear graph of cases by country captured on March 25 from 91-divoc\u2019s tool:"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "960px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "55.104166666666664%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/jpeg;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/46ecf1d1e90e59fe79421cb3eb716699/6c7d1/image2.webp 960w"], "sizes": "(max-width: 960px) 100vw, 960px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/46ecf1d1e90e59fe79421cb3eb716699/1fe05/image2.jpg 960w"], "sizes": "(max-width: 960px) 100vw, 960px", "type": "image/jpeg" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/46ecf1d1e90e59fe79421cb3eb716699/1fe05/image2.jpg", "alt": "COVID-19 Cases by Country from 91-divoc.com, captured on March 25, 2020.", "title": "COVID-19 Cases by Country from 91-divoc.com, captured on March 25, 2020.", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "Now observe the linear graph of cases per capita, captured on the same day from the same tool:"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "960px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "55.208333333333336%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": 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["/static/0d552191a3e2c10067d053bce86032a9/6c7d1/image3.webp 960w"], "sizes": "(max-width: 960px) 100vw, 960px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/0d552191a3e2c10067d053bce86032a9/1fe05/image3.jpg 960w"], "sizes": "(max-width: 960px) 100vw, 960px", "type": "image/jpeg" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/0d552191a3e2c10067d053bce86032a9/1fe05/image3.jpg", "alt": "COVID-19 Cases per Capita by Country from 91-divoc.com, captured on March 25, 2020.", "title": "COVID-19 Cases per Capita by Country from 91-divoc.com, captured on March 25, 2020.", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "It would seem here that the Vatican City is careening towards anarchy at an unprecedented rate\u2014an incorrect implication emerging from our choice of scale and normalization."), mdx("p", null, "Here John Burn-Murdoch, whose graphs for the ", mdx("em", { parentName: "p" }, "Financial Times"), " have earned praise, makes his team\u2019s case against using per-capita rates in graphs (", mdx("a", _extends({ parentName: "p" }, { "href": "https://twitter.com/jburnmurdoch/status/1249445458264698880?ref_src=twsrc%5Etfw", "target": "_blank", "rel": "noreferrer" }), "link"), "):"), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "506px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "295.65217391304344%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": 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coronavirus death rates by country by @jburnmurdoch'] And a chart showing why we're using absolute numbers rather than population-adjusted rates: [linked tweet: 'A quick chart for those who keep asking for per-capita adjustment: Here’s population vs total death toll one week after 10th death. No relationship. As I’ve been saying, population does not affect pace of spread. All per-capita figures do is make smaller countries look worse.'] [scatter plot chart with trendline] \"", "title": "tweet05", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "In the ", mdx("a", _extends({ parentName: "p" }, { "href": "https://twitter.com/CT_Bergstrom/status/1249930293928030209", "target": "_blank", "rel": "noreferrer" }), "thread below"), ", Carl T. Bergstrom of the University of Washington explains how per-capita rates ", mdx("em", { parentName: "p" }, "can"), " be responsibly compared between countries if the curves are left-aligned to starting positions of a given fractional infection rate. However, he also advises that per-capita comparisons between regions are preferable to those between countries."), mdx("p", null, mdx("span", _extends({ parentName: "p" }, { "className": "gatsby-resp-image-wrapper", "style": { "position": "relative", "display": "block", "marginLeft": "auto", "marginRight": "auto", "maxWidth": "1346px" } }), "\n ", mdx("span", _extends({ parentName: "span" }, { "className": "gatsby-resp-image-background-image", "style": { "paddingBottom": "62.704309063893014%", "position": "relative", "bottom": "0", "left": "0", "backgroundImage": "url('data:image/png;base64,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')", "backgroundSize": "cover", "display": "block" } })), "\n ", mdx("picture", { parentName: "span" }, "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/3f8f0179165a2e9a980f52c1b3c9d7af/8170e/screen-shot-2020-05-19-at-6.50.42-am.webp 1346w"], "sizes": "(max-width: 1346px) 100vw, 1346px", "type": "image/webp" })), "\n ", mdx("source", _extends({ parentName: "picture" }, { "srcSet": ["/static/3f8f0179165a2e9a980f52c1b3c9d7af/4eb26/screen-shot-2020-05-19-at-6.50.42-am.png 1346w"], "sizes": "(max-width: 1346px) 100vw, 1346px", "type": "image/png" })), "\n ", mdx("img", _extends({ parentName: "picture" }, { "className": "gatsby-resp-image-image", "src": "/static/3f8f0179165a2e9a980f52c1b3c9d7af/4eb26/screen-shot-2020-05-19-at-6.50.42-am.png", "alt": "Tweet by Carl T. Bergstrom: \"1. When plotting epidemic curves or death totals, should we divide by population size? Here on twitter this question has generated a lot more heat than light. The answer is a bit subtle and so while I’ve tweeted about this before I want to address it in more detail.\"", "title": "tweet06", "loading": "lazy", "style": { "width": "100%", "height": "100%", "margin": "0", "verticalAlign": "middle", "position": "absolute", "top": "0", "left": "0" } })), "\n "), "\n ")), mdx("p", null, "There is also volatile disagreement on the proper selection of variables. Many sources, ", mdx("a", _extends({ parentName: "p" }, { "href": "https://fivethirtyeight.com/features/coronavirus-case-counts-are-meaningless/", "target": "_blank", "rel": "noreferrer" }), mdx("em", { parentName: "a" }, "FiveThirtyEight"), " among them"), ", point out the futility of using reported case counts from governments (of inconsistent trustworthiness) using inconsistent testing methods. Burn-Murdoch\u2019s detractors critique his visuals\u2019 acceptance of China\u2019s published counts at face value. ", mdx("a", _extends({ parentName: "p" }, { "href": "https://blog.datawrapper.de/coronaviruscharts/", "target": "_blank", "rel": "noreferrer" }), "Datawrapper"), " recommends avoiding this problem by instead using confirmed death counts, which are harder to misreport but still imperfect since COVID can be an indirect cause of death."), mdx("p", null, "What are some general best practices and precautions for analyzing and visualizing COVID data? Here are some excellent sources addressing this question."), mdx("ul", null, mdx("li", { parentName: "ul" }, mdx("strong", { parentName: "li" }, mdx("a", _extends({ parentName: "strong" }, { "href": "https://www.tableau.com/about/blog/2020/3/ten-considerations-you-create-another-chart-about-covid-19", "target": "_blank", "rel": "noreferrer" }), "10 considerations before you create another chart about COVID-19")), " (03/13/20) by Amanda Makulec, Operations Director for the Data Visualization Societ", mdx("a", _extends({ parentName: "li" }, { "href": "http://news.mit.edu/2020/catherine-dignazio-visualizing-covid-19-data-0414", "target": "_blank", "rel": "noreferrer" })), "y"), mdx("li", { parentName: "ul" }, mdx("strong", { parentName: "li" }, mdx("a", _extends({ parentName: "strong" }, { "href": "http://news.mit.edu/2020/catherine-dignazio-visualizing-covid-19-data-0414", "target": "_blank", "rel": "noreferrer" }), "3 Questions: Catherine D\u2019Ignazio on visualizing COVID-19 data")), " (04/13/20) profiling MIT assistant professor D\u2019Ignazio;", mdx("a", _extends({ parentName: "li" }, { "href": "https://www.esri.com/arcgis-blog/products/product/mapping/mapping-coronavirus-responsibly/", "target": "_blank", "rel": "noreferrer" }))), mdx("li", { parentName: "ul" }, mdx("strong", { parentName: "li" }, mdx("a", _extends({ parentName: "strong" }, { "href": "https://www.esri.com/arcgis-blog/products/product/mapping/mapping-coronavirus-responsibly/", "target": "_blank", "rel": "noreferrer" }), "Mapping coronavirus, responsibly")), " (02/25/20) by Kenneth Field for ESRI;"), mdx("li", { parentName: "ul" }, mdx("strong", { parentName: "li" }, mdx("a", _extends({ parentName: "strong" }, { "href": "https://fivethirtyeight.com/features/why-its-so-freaking-hard-to-make-a-good-covid-19-model/", "target": "_blank", "rel": "noreferrer" }), "Why It\u2019s So Freaking Hard to Make a Good COVID-19 Model")), " (03/31/20) by Maggie Koerth, Laura Bronner, and Jasmine Mithani for ", mdx("em", { parentName: "li" }, "FiveThirtyEight"), ";"), mdx("li", { parentName: "ul" }, "Once again, ", mdx("a", _extends({ parentName: "li" }, { "href": "https://twitter.com/EvanMPeck/status/1235568532840120321", "target": "_blank", "rel": "noreferrer" }), "this Twitter thread"), " by Evan M. Peck of Bucknell University.")), mdx("p", null, "For trustworthy visualizations, explore ", mdx("a", _extends({ parentName: "p" }, { "href": "https://www.tableau.com/about/blog/2020/4/most-interesting-data-vizzes-covid-19-weve-seen-media-so-far", "target": "_blank", "rel": "noreferrer" }), "these gems selected by Tableau"), " or the", mdx("a", _extends({ parentName: "p" }, { "href": "https://blog.datawrapper.de/coronaviruscharts/", "target": "_blank", "rel": "noreferrer" }), " charts featured by Datawrapper"), "."), mdx("p", null, "The COVID-19 pandemic has underscored the importance of informed epidemiological data analysis. Often, the niche expertise required for this analysis can be a hazard ", mdx("a", _extends({ parentName: "p" }, { "href": "https://twitter.com/ferrisjabr/status/1221146622341443584", "target": "_blank", "rel": "noreferrer" }), "even to credentialed experts"), ". The democratic, collaborative nature of public forum data science can help us meet the demands of this vexing global problem. But, this does not suggest that every individual or team reporting on COVID has an equal claim to accuracy; rather, it implies that the analysis challenges are larger than any one mind can confidently answer.")); } ; MDXContent.isMDXComponent = true;https://blog.civicdatadesignlab.mit.edu/introducing-the-cddl-blog!https://blog.civicdatadesignlab.mit.edu/introducing-the-cddl-blog!Fri, 15 May 2020 00:00:00 GMTfunction _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); } function _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; } function _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; } /* @jsx mdx */ var _frontmatter = { "title": "Introducing the CDDL Blog! ", "author": "Tess McCann", "date": "2020-05-15T00:00:00.000Z", "tags": ["General"], "hero": "images/screen-shot-2020-05-15-at-3.21.28-am.png" }; var makeShortcode = function makeShortcode(name) { return function MDXDefaultShortcode(props) { console.warn("Component " + name + " was not imported, exported, or provided by MDXProvider as global scope"); return mdx("div", props); }; }; var layoutProps = { _frontmatter: _frontmatter }; var MDXLayout = "wrapper"; return function MDXContent(_ref) { var components = _ref.components, props = _objectWithoutProperties(_ref, ["components"]); return mdx(MDXLayout, _extends({}, layoutProps, props, { components: components, mdxType: "MDXLayout" }), mdx("p", null, "Since March, we at the Civic Data Design Lab (CDDL) have been collecting and analyzing data surrounding the COVID-19 pandemic. Our work has always been about understanding data and using it to call attention to the needs and interests of citizens on the margins of policy development. The global pandemic, and its quantitative nature, has made this mission all the more crucial. COVID-19 is exposing existing inequalities in our country and across the globe. We urgently must document these disparities as they emerge to have a chance of addressing them."), mdx("p", null, "We are starting this blog in order to share the work we\u2019re doing with a larger audience. We join a large community of scientists, designers, and urbanists around the world who are already engaged in open conversations about COVID-19 through the lens of its data\u2014its nuances, its stories, and its calls to action. As in our other projects, we\u2019re experimenting with collection tools and visualization strategies that allow us to highlight phenomena that might otherwise be overlooked."), mdx("p", null, "Our first post highlights how tricky it is to draw informed conclusions from COVID-19 data. Upcoming posts will delve into the inequalities of the outbreak, particularly how testing opportunities relate to income levels across the country."), mdx("p", null, "As the situation with COVID-19 evolves, so will this blog. We hope to use this space to start conversations toward change and we look forward to the dialogues that come from it.")); } ; MDXContent.isMDXComponent = true;