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He is a dutiful albeit reformist member of the Institute of Transportation Engineers. He spends free time documenting early 20th-century mass transit plans and augmenting a collection of classical music recordings. Home in Los Angeles, his family maintains a flock of chickens.","id":"cc0437e1-8b83-5b6d-baf0-ea81af2c9a63","name":"Griffin 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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.","canonical_url":null,"subscription":true,"body":"function _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); }\n\nfunction _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; }\n\nfunction _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; }\n\n/* @jsx mdx */\nvar _frontmatter = {\n \"title\": \"Coronavirus, Occupation, Race, & Coronavirus\",\n \"author\": \"Griffin Kantz\",\n \"date\": \"2020-07-13T00:00:00.000Z\",\n \"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.\",\n \"tags\": [\"Covid\", \"Mapping\"],\n \"hero\": \"images/casespercapita_20200703.png\"\n};\n\nvar makeShortcode = function makeShortcode(name) {\n return function MDXDefaultShortcode(props) {\n console.warn(\"Component \" + name + \" was not imported, exported, or provided by MDXProvider as global scope\");\n return mdx(\"div\", props);\n };\n};\n\nvar layoutProps = {\n _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n var components = _ref.components,\n props = _objectWithoutProperties(_ref, [\"components\"]);\n\n return mdx(MDXLayout, _extends({}, layoutProps, props, {\n components: components,\n mdxType: \"MDXLayout\"\n }), 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({\n parentName: \"p\"\n }, {\n \"src\": \"/5dbbd25f7a30868f69aac87ffe63f161/casespercapita.gif\",\n \"alt\": \"Animated map of new coronavirus cases per capita in the U.S. (seven-day rolling average).\"\n }))), mdx(\"p\", null, mdx(\"em\", {\n parentName: \"p\"\n }, \"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({\n parentName: \"p\"\n }, {\n \"src\": \"/6ae258caff80a96040bbf31cc88cd8d4/unemployment.gif\",\n \"alt\": \"Animated map of unemployment in the U.S.\"\n }))), mdx(\"p\", null, mdx(\"em\", {\n parentName: \"p\"\n }, \"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\", {\n \"id\": \"race\"\n }, \"Race\"), mdx(\"p\", null, mdx(\"span\", _extends({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"960px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"56.25%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": \"url('data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAALCAYAAAB/Ca1DAAAACXBIWXMAAAsTAAALEwEAmpwYAAAC00lEQVQoz11SbUhTYRR+N7ECicgiKupn9CM0KcIfRhkIQRZFPwxDf0hhRkqIGBWJJKRkBaWBiTmRTGiyURMzl+XHajrn5uZwm5v73u6837vb3e52l93uvTa1zp/ncN7nnPPwnBdoYQRMQpAU8DEVhsrUwcCYGUcblW7LJXXAlf/aotlvhKFdRGptB/gb3igtzeR4Ml0gIJlMA4xJA7CIE5k3MIeg006W5bD0b85KEmuasI8e8doQZySCBmhG54/Fb9nJaH6GjzJsHcawz4UcTqQk/HAADCghbtOj+EkLQSW1MMyYMCK6RJCJJYJgh+y6lBlbpcOJVNoXi/tdUbrXScUKQnHmvjtKrxkx/ITQbyOpddVyl01MemyGwlG/m55FUHYGRpIr0QRjJSlmmSRpflAEiicpbywOWcmIiVdq4HPuW9DfIg7DEWnd+MC67Ef6ERGvfunIeWqaCE7AIW4K8tD9dh3NWxAPxpN0iGai/lgiAifYCD8oEmU5kvc31TjzsUzo7bYZswTc3nUeAI7jwGlFk6iyXqu83G6ejPe4DFyDdpD7GvQzJpygxkOBmA5ZJdQBJ2ZAUXQ6HMRb5kc9zYbRY0Jf7vB1ycYhstqKRMwbqD7MQ/aQx3tQ6XF2GDDik8rnTs4iCDcVDlEyh4m7px369cyoptpN37nm+c+iupdmTd7xN1Xlh2oK94Ktcaqv2np38u2LrbUlgrpmxomfKt8K02s3yrqsuiedFu1Zhdteo/J7HvAUSavqw57S9jv7Kqoqd292FoJtZ/pq6xQee1HbwnguzdtQqmjauX598ij/V+vBfzHgtFTKAracrbWSyguCcUeAPOHe8OCdwyzm5+SN2ReHWw5k6uoQIXmsH5HK7PPSV4sakRNUzoHa/k5Q0noTlN+o+Hdjcfdt6XvHgkgsljdsKnFYwEP9DzDoNIHe5UWxNgFBoG95fbFw1CtjPRv8P01eu8JeCd2gAAAAAElFTkSuQmCC')\",\n \"backgroundSize\": \"cover\",\n \"display\": \"block\"\n }\n })), \"\\n \", mdx(\"picture\", {\n parentName: \"span\"\n }, \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/d264a43f5e5b1894b4874efad68243d3/6c7d1/race.webp 960w\"],\n \"sizes\": \"(max-width: 960px) 100vw, 960px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/d264a43f5e5b1894b4874efad68243d3/fde0f/race.png 960w\"],\n \"sizes\": \"(max-width: 960px) 100vw, 960px\",\n \"type\": \"image/png\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/d264a43f5e5b1894b4874efad68243d3/fde0f/race.png\",\n \"alt\": \"race\",\n \"title\": \"Map of percent non-white population, United States.\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, mdx(\"em\", {\n parentName: \"p\"\n }, \"Map 3. Percent non-white population, United States, U.S. Census Bureau. Produced with QGIS.\")), mdx(\"p\", null, mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://www.nytimes.com/interactive/2020/07/05/us/coronavirus-latinos-african-americans-cdc-data.html\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"Race\"), \" appears to correlate significantly with the areas experiencing the largest coronavirus surges. The \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://en.wikipedia.org/wiki/Black_Belt_in_the_American_South\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"Black Belt\"), \" in the South and the expansive cluster of Native American reservations (particularly the \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://navajotimes.com/coronavirus-updates/covid-19-across-the-navajo-nation/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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({\n parentName: \"p\"\n }, {\n \"href\": \"https://www.epi.org/publication/black-workers-covid/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"report\"), \" by the Economic Policy Institute covering \\u201Ctwo of the most lethal preexisting conditions for coronavirus\\u2014racism and economic inequality\\u201D.\"), mdx(\"iframe\", {\n width: \"100%\",\n height: \"802\",\n 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\",\n frameBorder: \"0\"\n }), mdx(\"h3\", {\n \"id\": \"occupation-type\"\n }, \"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({\n parentName: \"p\"\n }, {\n \"href\": \"https://siouxlandnews.com/news/coronavirus/buena-vista-county-tops-national-list-for-fastest-growing-covid-19-hotpost\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"Iowa\"), \", \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://www.indystar.com/story/news/environment/2020/04/27/cass-county-coronavirus-cases-spike-county-home-meat-plant/3033246001/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"Indiana\"), \", and \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"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\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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({\n parentName: \"p\"\n }, {\n \"href\": \"https://uhero.hawaii.edu/covid-19s-uneven-impact-on-businesses-and-workers-results-from-a-uhero-chamber-of-commerce-hawaii-survey/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"Hawai\\u2019i\"), \", \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://www.brookings.edu/blog/the-avenue/2020/06/04/why-covid-19-hit-michigan-so-hard/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"Michigan\"), \", and \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"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\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"Nevada\"), \", states which are home to large tourism, manufacturing, and accommodations industries, respectively. 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source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _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; }\n\nfunction _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; }\n\n/* @jsx mdx */\nvar _frontmatter = {\n \"title\": \"The Importance of the \\\"Starting Point\\\" in Tracking COVID by Region\",\n \"author\": \"Griffin Kantz\",\n \"date\": \"2020-06-15T00:00:00.000Z\",\n \"excerpt\": \"\",\n \"tags\": [\"Covid\", \"Data\", \"Visualization\"],\n \"hero\": \"images/covid-19-critical-mass_graphic.png\"\n};\n\nvar makeShortcode = function makeShortcode(name) {\n return function MDXDefaultShortcode(props) {\n console.warn(\"Component \" + name + \" was not imported, exported, or provided by MDXProvider as global scope\");\n return mdx(\"div\", props);\n };\n};\n\nvar layoutProps = {\n _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n var components = _ref.components,\n props = _objectWithoutProperties(_ref, [\"components\"]);\n\n return mdx(MDXLayout, _extends({}, layoutProps, props, {\n components: components,\n mdxType: \"MDXLayout\"\n }), 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({\n parentName: \"p\"\n }, {\n \"href\": \"https://www.cdc.gov/mmwr/volumes/69/wr/mm6915e1.htm\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"idiosyncrasies of people\\u2019s daily behaviors\"), \".\"), mdx(\"p\", null, \"In New York state, where COVID \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://www.cdc.gov/mmwr/volumes/69/wr/mm6922e1.htm?s_cid=mm6922e1_w\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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\", {\n parentName: \"p\"\n }, mdx(\"a\", _extends({\n parentName: \"em\"\n }, {\n \"href\": \"https://ig.ft.com/coronavirus-chart/?areas=usa&areas=gbr&cumulative=0&logScale=1&perMillion=0&values=deaths\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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({\n parentName: \"p\"\n }, {\n \"href\": \"http://91-divoc.com/pages/covid-visualization/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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\", {\n \"id\": \"what-threshold-makes-the-most-sense\"\n }, \"What threshold makes the most sense?\"), mdx(\"p\", null, \"In our analysis, we examined the \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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\", {\n parentName: \"p\"\n }, \"X\"), \" total deaths was reached, where \", mdx(\"em\", {\n parentName: \"p\"\n }, \"X\"), \" = 1, 2, 5, 10, 20, 50, 100, 200, 500, 1,000, and 2,000.\"), mdx(\"p\", null, mdx(\"em\", {\n parentName: \"p\"\n }, \"(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\", {\n parentName: \"p\"\n }, \"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({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"1375px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"14.399999999999999%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": \"url('data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAADCAYAAACTWi8uAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAAaUlEQVQI12WOSwqAMAwF041oF/VXFcSVqAsP1d7/Fk4gBdHCMJS+vFSEk3MWc51SuvEFB+wwGBus9hahJeve8/Ipc9DARDDgQikMttBjb65e879CDZwK4RF3Wg4L9xkrvWWD/TiWjtLzABCJOGODeCV/AAAAAElFTkSuQmCC')\",\n \"backgroundSize\": \"cover\",\n \"display\": \"block\"\n }\n })), \"\\n \", mdx(\"picture\", {\n parentName: \"span\"\n }, \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/c6ac1b1d9779498361dbb02cc978e5f5/0fffa/covid-19-critical-mass_equations.webp 1375w\"],\n \"sizes\": \"(max-width: 1375px) 100vw, 1375px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/c6ac1b1d9779498361dbb02cc978e5f5/8ff9b/covid-19-critical-mass_equations.png 1375w\"],\n \"sizes\": \"(max-width: 1375px) 100vw, 1375px\",\n \"type\": \"image/png\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/c6ac1b1d9779498361dbb02cc978e5f5/8ff9b/covid-19-critical-mass_equations.png\",\n \"alt\": \"Equations: log(deaths) = alpha + beta*days + epsilon. Deaths = lambda*e^(beta*days) + epsilon, lambda = e^alpha.\",\n \"title\": \"Equations: log(deaths) = alpha + beta*days + epsilon. Deaths = lambda*e^(beta*days) + epsilon, lambda = e^alpha.\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, mdx(\"em\", {\n parentName: \"p\"\n }, \"(Here,\"), \" \\u03B1 \", mdx(\"em\", {\n parentName: \"p\"\n }, \"and\"), \" \\u03B2 \", mdx(\"em\", {\n parentName: \"p\"\n }, \"are the y-intercept and slope of the regression and\"), \" \\u03B5 \", mdx(\"em\", {\n parentName: \"p\"\n }, \"is the residual error.)\")), mdx(\"p\", null, \"Below in Chart 1, see an interactive graph illustrating the data for \", mdx(\"em\", {\n parentName: \"p\"\n }, \"X\"), \" = 1 death (the day of the first recorded COVID death in each MSA).\"), mdx(\"iframe\", {\n width: \"500\",\n height: \"400\",\n frameBorder: \"0\",\n scrolling: \"no\",\n src: \"//plotly.com/~GriffinK/3.embed\"\n }), mdx(\"p\", null, mdx(\"em\", {\n parentName: \"p\"\n }, \"Chart 1. Rendered in R, ggplot2, and Plotly. Hover over points to see more information.\")), mdx(\"p\", null, \"When \", mdx(\"em\", {\n parentName: \"p\"\n }, \"X\"), \" = 1, the trajectories on the right (\", mdx(\"em\", {\n parentName: \"p\"\n }, \"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\", {\n parentName: \"p\"\n }, \"X\"), \", any data that would appear on the left (\", mdx(\"em\", {\n parentName: \"p\"\n }, \"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\", {\n parentName: \"p\"\n }, \"X\"), \", we are able to see the trajectories on the \", mdx(\"em\", {\n parentName: \"p\"\n }, \"after\"), \" side begin to coalesce and the data on the \", mdx(\"em\", {\n parentName: \"p\"\n }, \"before\"), \" side start to grow. For the highest \", mdx(\"em\", {\n parentName: \"p\"\n }, \"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\", {\n parentName: \"p\"\n }, \"X\"), \", visualized:\"), mdx(\"iframe\", {\n width: \"500\",\n height: \"400\",\n frameBorder: \"0\",\n scrolling: \"no\",\n src: \"//plotly.com/~GriffinK/5.embed\"\n }), mdx(\"iframe\", {\n width: \"500\",\n height: \"400\",\n frameBorder: \"0\",\n scrolling: \"no\",\n src: \"//plotly.com/~GriffinK/9.embed\"\n }), mdx(\"iframe\", {\n width: \"500\",\n height: \"400\",\n frameBorder: \"0\",\n scrolling: \"no\",\n src: \"//plotly.com/~GriffinK/15.embed\"\n }), mdx(\"p\", null, mdx(\"em\", {\n parentName: \"p\"\n }, \"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\", {\n parentName: \"p\"\n }, \"X\"), \", the mean square error (MSE) of the observations to the \", mdx(\"em\", {\n parentName: \"p\"\n }, \"before\"), \" and \", mdx(\"em\", {\n parentName: \"p\"\n }, \"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\", {\n parentName: \"p\"\n }, \"X\"), \", the MSE is lower on the \", mdx(\"em\", {\n parentName: \"p\"\n }, \"before\"), \" side than on the \", mdx(\"em\", {\n parentName: \"p\"\n }, \"after\"), \" side. For larger \", mdx(\"em\", {\n parentName: \"p\"\n }, \"X\"), \", the opposite is true: the \", mdx(\"em\", {\n parentName: \"p\"\n }, \"after\"), \" MSE is lower and the \", mdx(\"em\", {\n parentName: \"p\"\n }, \"before\"), \" MSE is higher.\"), mdx(\"p\", null, \"As \", mdx(\"em\", {\n parentName: \"p\"\n }, \"X\"), \" increases, the point at which the \", mdx(\"em\", {\n parentName: \"p\"\n }, \"after\"), \" MSE becomes less than the \", mdx(\"em\", {\n parentName: \"p\"\n }, \"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\", {\n parentName: \"p\"\n }, \"X\"), \" = 50, as visualized in the graph below:\"), mdx(\"iframe\", {\n width: \"500\",\n height: \"300\",\n frameBorder: \"0\",\n scrolling: \"no\",\n src: \"//plotly.com/~GriffinK/19.embed\"\n }), mdx(\"p\", null, mdx(\"em\", {\n parentName: \"p\"\n }, \"Chart 5. Mean square error for each value of\"), \" X\", mdx(\"em\", {\n parentName: \"p\"\n }, \".\")), 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\", {\n parentName: \"p\"\n }, \"before\"), \" and \", mdx(\"em\", {\n parentName: \"p\"\n }, \"after\"), \" MSE curves beyond \", mdx(\"em\", {\n parentName: \"p\"\n }, \"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\", {\n parentName: \"p\"\n }, \"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\", {\n parentName: \"p\"\n }, \"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\", {\n parentName: \"p\"\n }, \"X\"), \". If more regions across the U.S. were suffering COVID fatality rates that severe, we could expect to see the \", mdx(\"em\", {\n parentName: \"p\"\n }, \"before\"), \" MSE curve trend further and further upwards.\"), mdx(\"h2\", {\n \"id\": \"what-we-can-learn\"\n }, \"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({\n parentName: \"p\"\n }, {\n \"href\": \"https://github.com/civic-data-design-lab/COVID-critical-mass\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"here\"), \".\"));\n}\n;\nMDXContent.isMDXComponent = 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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({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/hashtag/datasaveslives?lang=en\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"#\", \"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. 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going on [image of graph] I have nothing to promote but please stay home, wash your hands, and stop publishing charts with default Excel formatting\\\"\",\n \"title\": \"tweet3\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, \"^(Ahem, anyone using a \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/bencasselman/status/1258136404363808769\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"\\u201Ccubic model\\u201D\"), \".)\"), mdx(\"p\", null, \"See the \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/ferrisjabr/status/1221145299084726273\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"thread below\"), \" and the \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://www.theatlantic.com/technology/archive/2020/01/china-coronavirus-twitter/605644/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), mdx(\"em\", {\n parentName: \"a\"\n }, \"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({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"1328px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"85.09036144578313%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": 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\"backgroundSize\": \"cover\",\n \"display\": \"block\"\n }\n })), \"\\n \", mdx(\"picture\", {\n parentName: \"span\"\n }, \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/aaa42fa7f634e3b8184b58478608815f/909b8/screen-shot-2020-05-19-at-6.44.47-am.webp 1328w\"],\n \"sizes\": \"(max-width: 1328px) 100vw, 1328px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/aaa42fa7f634e3b8184b58478608815f/fa900/screen-shot-2020-05-19-at-6.44.47-am.png 1328w\"],\n \"sizes\": \"(max-width: 1328px) 100vw, 1328px\",\n \"type\": \"image/png\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/aaa42fa7f634e3b8184b58478608815f/fa900/screen-shot-2020-05-19-at-6.44.47-am.png\",\n \"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...\\\"]\",\n \"title\": \"tweet4\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\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({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/neil_ferguson/status/1243294815200124928\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"thread\"), \"):\"), mdx(\"p\", null, mdx(\"span\", _extends({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"1324px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"52.87009063444109%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": \"url('data:image/png;base64,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')\",\n \"backgroundSize\": \"cover\",\n \"display\": \"block\"\n }\n })), \"\\n \", mdx(\"picture\", {\n parentName: \"span\"\n }, \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/3d8969f7f0e3203a38c7436bd7f5772c/33b3d/screen-shot-2020-05-19-at-6.45.02-am.webp 1324w\"],\n \"sizes\": \"(max-width: 1324px) 100vw, 1324px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/3d8969f7f0e3203a38c7436bd7f5772c/b58f7/screen-shot-2020-05-19-at-6.45.02-am.png 1324w\"],\n \"sizes\": \"(max-width: 1324px) 100vw, 1324px\",\n \"type\": \"image/png\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/3d8969f7f0e3203a38c7436bd7f5772c/b58f7/screen-shot-2020-05-19-at-6.45.02-am.png\",\n \"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.\\\"\",\n \"title\": \"tweet5\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, \"All these epistemological limitations fit under the umbrella of the \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"Dunning-Kruger effect\"), \":\"), mdx(\"p\", null, mdx(\"span\", _extends({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"800px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"71.74999999999999%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": \"url('data:image/jpeg;base64,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')\",\n \"backgroundSize\": \"cover\",\n \"display\": \"block\"\n }\n })), \"\\n \", mdx(\"picture\", {\n parentName: \"span\"\n }, \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/658e085757182365014640f9cc50c084/8d2ea/image1.webp 800w\"],\n \"sizes\": \"(max-width: 800px) 100vw, 800px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/658e085757182365014640f9cc50c084/c60e9/image1.jpg 800w\"],\n \"sizes\": \"(max-width: 800px) 100vw, 800px\",\n \"type\": \"image/jpeg\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/658e085757182365014640f9cc50c084/c60e9/image1.jpg\",\n \"alt\": \"Dunning-Kruger Effect. Diagram showing relationship between knowledge in field and confidence.\",\n \"title\": \"Dunning-Kruger\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, mdx(\"em\", {\n parentName: \"p\"\n }, \"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({\n parentName: \"p\"\n }, {\n \"href\": \"https://covidactnow.org/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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({\n parentName: \"p\"\n }, {\n \"href\": \"https://web.archive.org/web/20200327060650/http:/www.covidactnow.org/state/NY\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"in the site\\u2019s first few weeks\"), \".\"), mdx(\"p\", null, \"The \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"http://91-divoc.com/pages/covid-visualization/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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\", {\n parentName: \"p\"\n }, \"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({\n parentName: \"p\"\n }, {\n \"href\": \"http://91-divoc.com/pages/covid-visualization/changes.html\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/NateSilver538/status/1245132431818178561\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"960px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"55.104166666666664%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": 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[\"/static/46ecf1d1e90e59fe79421cb3eb716699/6c7d1/image2.webp 960w\"],\n \"sizes\": \"(max-width: 960px) 100vw, 960px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/46ecf1d1e90e59fe79421cb3eb716699/1fe05/image2.jpg 960w\"],\n \"sizes\": \"(max-width: 960px) 100vw, 960px\",\n \"type\": \"image/jpeg\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/46ecf1d1e90e59fe79421cb3eb716699/1fe05/image2.jpg\",\n \"alt\": \"COVID-19 Cases by Country from 91-divoc.com, captured on March 25, 2020.\",\n \"title\": \"COVID-19 Cases by Country from 91-divoc.com, captured on March 25, 2020.\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\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({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"960px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"55.208333333333336%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": 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[\"/static/0d552191a3e2c10067d053bce86032a9/6c7d1/image3.webp 960w\"],\n \"sizes\": \"(max-width: 960px) 100vw, 960px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/0d552191a3e2c10067d053bce86032a9/1fe05/image3.jpg 960w\"],\n \"sizes\": \"(max-width: 960px) 100vw, 960px\",\n \"type\": \"image/jpeg\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/0d552191a3e2c10067d053bce86032a9/1fe05/image3.jpg\",\n \"alt\": \"COVID-19 Cases per Capita by Country from 91-divoc.com, captured on March 25, 2020.\",\n \"title\": \"COVID-19 Cases per Capita by Country from 91-divoc.com, captured on March 25, 2020.\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\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\", {\n parentName: \"p\"\n }, \"Financial Times\"), \" have earned praise, makes his team\\u2019s case against using per-capita rates in graphs (\", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/jburnmurdoch/status/1249445458264698880?ref_src=twsrc%5Etfw\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"link\"), \"):\"), mdx(\"p\", null, mdx(\"span\", _extends({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"506px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n 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\"backgroundSize\": \"cover\",\n \"display\": \"block\"\n }\n })), \"\\n \", mdx(\"picture\", {\n parentName: \"span\"\n }, \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/9d60f9c11fb960caa002786bff6b6a4c/3bde5/screen-shot-2020-05-19-at-6.48.50-am.webp 506w\"],\n \"sizes\": \"(max-width: 506px) 100vw, 506px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/9d60f9c11fb960caa002786bff6b6a4c/30942/screen-shot-2020-05-19-at-6.48.50-am.png 506w\"],\n \"sizes\": \"(max-width: 506px) 100vw, 506px\",\n \"type\": \"image/png\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/9d60f9c11fb960caa002786bff6b6a4c/30942/screen-shot-2020-05-19-at-6.48.50-am.png\",\n \"alt\": \"Tweets by John Burn-Murdoch: \\\"Here’s a video where I explain why we’re using log scales, showing absolute numbers instead of per capita, and much more: [video link: 'Everything you need to know about that pink graph mapping 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] \\\"\",\n \"title\": \"tweet05\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, \"In the \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/CT_Bergstrom/status/1249930293928030209\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"thread below\"), \", Carl T. Bergstrom of the University of Washington explains how per-capita rates \", mdx(\"em\", {\n parentName: \"p\"\n }, \"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({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"1346px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"62.704309063893014%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": \"url('data:image/png;base64,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')\",\n \"backgroundSize\": \"cover\",\n \"display\": \"block\"\n }\n })), \"\\n \", mdx(\"picture\", {\n parentName: \"span\"\n }, \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/3f8f0179165a2e9a980f52c1b3c9d7af/8170e/screen-shot-2020-05-19-at-6.50.42-am.webp 1346w\"],\n \"sizes\": \"(max-width: 1346px) 100vw, 1346px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/3f8f0179165a2e9a980f52c1b3c9d7af/4eb26/screen-shot-2020-05-19-at-6.50.42-am.png 1346w\"],\n \"sizes\": \"(max-width: 1346px) 100vw, 1346px\",\n \"type\": \"image/png\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/3f8f0179165a2e9a980f52c1b3c9d7af/4eb26/screen-shot-2020-05-19-at-6.50.42-am.png\",\n \"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.\\\"\",\n \"title\": \"tweet06\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, \"There is also volatile disagreement on the proper selection of variables. Many sources, \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://fivethirtyeight.com/features/coronavirus-case-counts-are-meaningless/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), mdx(\"em\", {\n parentName: \"a\"\n }, \"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({\n parentName: \"p\"\n }, {\n \"href\": \"https://blog.datawrapper.de/coronaviruscharts/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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\", {\n parentName: \"ul\"\n }, mdx(\"strong\", {\n parentName: \"li\"\n }, mdx(\"a\", _extends({\n parentName: \"strong\"\n }, {\n \"href\": \"https://www.tableau.com/about/blog/2020/3/ten-considerations-you-create-another-chart-about-covid-19\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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({\n parentName: \"li\"\n }, {\n \"href\": \"http://news.mit.edu/2020/catherine-dignazio-visualizing-covid-19-data-0414\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n })), \"y\"), mdx(\"li\", {\n parentName: \"ul\"\n }, mdx(\"strong\", {\n parentName: \"li\"\n }, mdx(\"a\", _extends({\n parentName: \"strong\"\n }, {\n \"href\": \"http://news.mit.edu/2020/catherine-dignazio-visualizing-covid-19-data-0414\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"3 Questions: Catherine D\\u2019Ignazio on visualizing COVID-19 data\")), \" (04/13/20) profiling MIT assistant professor D\\u2019Ignazio;\", mdx(\"a\", _extends({\n parentName: \"li\"\n }, {\n \"href\": \"https://www.esri.com/arcgis-blog/products/product/mapping/mapping-coronavirus-responsibly/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }))), mdx(\"li\", {\n parentName: \"ul\"\n }, mdx(\"strong\", {\n parentName: \"li\"\n }, mdx(\"a\", _extends({\n parentName: \"strong\"\n }, {\n \"href\": \"https://www.esri.com/arcgis-blog/products/product/mapping/mapping-coronavirus-responsibly/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"Mapping coronavirus, responsibly\")), \" (02/25/20) by Kenneth Field for ESRI;\"), mdx(\"li\", {\n parentName: \"ul\"\n }, mdx(\"strong\", {\n parentName: \"li\"\n }, mdx(\"a\", _extends({\n parentName: \"strong\"\n }, {\n \"href\": \"https://fivethirtyeight.com/features/why-its-so-freaking-hard-to-make-a-good-covid-19-model/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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\", {\n parentName: \"li\"\n }, \"FiveThirtyEight\"), \";\"), mdx(\"li\", {\n parentName: \"ul\"\n }, \"Once again, \", mdx(\"a\", _extends({\n parentName: \"li\"\n }, {\n \"href\": \"https://twitter.com/EvanMPeck/status/1235568532840120321\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"this Twitter thread\"), \" by Evan M. Peck of Bucknell University.\")), mdx(\"p\", null, \"For trustworthy visualizations, explore \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://www.tableau.com/about/blog/2020/4/most-interesting-data-vizzes-covid-19-weve-seen-media-so-far\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"these gems selected by Tableau\"), \" or the\", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://blog.datawrapper.de/coronaviruscharts/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \" 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({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/ferrisjabr/status/1221146622341443584\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"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.\"));\n}\n;\nMDXContent.isMDXComponent = 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He assists the Civic Data Design Lab with endeavors related to mobility themes, such as the Digital Matatus and NextStop projects. He is avidly loyal to his home city, Los Angeles. He earned a Bachelor of Science in policy and planning in 2017 from the University of Southern California's School of Public Policy, graduating as the department's class valedictorian. He worked at KOA Corporation, a transportation planning and engineering consultant in California, for 15 months before beginning his studies at MIT in Fall 2018. This past summer, he interned at the San Francisco Municipal Transportation Agency. He is a dutiful albeit reformist member of the Institute of Transportation Engineers. He spends free time documenting early 20th-century mass transit plans and augmenting a collection of classical music recordings. Home in Los Angeles, his family maintains a flock of chickens.","id":"cc0437e1-8b83-5b6d-baf0-ea81af2c9a63","name":"Griffin 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