{"componentChunkName":"component---node-modules-narative-gatsby-theme-novela-src-templates-article-template-tsx","path":"/housing-and-covid-19-in-san-francisco","result":{"data":{"allSite":{"edges":[{"node":{"siteMetadata":{"name":"MIT Civic Data Design Lab"}}}]}},"pageContext":{"article":{"id":"ea0429e6-dd37-5a70-982b-363daeb3cc9e","slug":"/housing-and-covid-19-in-san-francisco","secret":false,"title":"Housing and COVID-19 in San Francisco","author":"Joyce Zhao","date":"July 14th, 2020","dateForSEO":"2020-07-14T00:00:00.000Z","timeToRead":2,"excerpt":"On March 13, 2020, Mayor London Breed issued an executive order temporarily halting most evictions in San Francisco. This moratorium…","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\": \"Housing and COVID-19 in San Francisco\",\n  \"author\": \"Joyce Zhao\",\n  \"date\": \"2020-07-14T00:00:00.000Z\",\n  \"tags\": [\"Covid\"],\n  \"hero\": \"images/2019-eviction-notices-and-covid-rates.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, \"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({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.nytimes.com/2020/05/27/us/coronavirus-evictions-renters.html\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  })), \" \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.nytimes.com/2020/05/27/us/coronavirus-evictions-renters.html\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"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({\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\": \"1000px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"80%\",\n      \"position\": \"relative\",\n      \"bottom\": \"0\",\n      \"left\": \"0\",\n      \"backgroundImage\": \"url('data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAQCAYAAAAWGF8bAAAACXBIWXMAAA7DAAAOwwHHb6hkAAADDElEQVQ4y21UaU8TURTtj9KfQvzmN3+ASoxowhfFSDSIQkQ0CiqyVSgqQouBstharKUFCi1bbTvTmXa6zdJ94fjuq9OAeJOXmcy779zzzr1nLIVCAbqug575fB7VahUUp6en5xbtq6oKTdPaz2KxeCHPkk6nOVCtVkOpVEK5XIaqqeeSms0mFEXhhakg5RmGgVwud6G4JZvNQpZlnkRRrVU5A9o0wwRMJpPtG5QrZeiGjrPBATOZDERRBDE9ZQfPgpytnEqlIAhCm9VZkHMMSRsCpav+L/JKFtVKhV+R8kyG/4KSpo1GAxbzanV2qMpE1lQNUkKCEI3w7wXNQLFQbB+mQ1xH3YCqZKDn8q08Roy+W/BXKjUag7C6Bu+SHc8HnuFO1w3IieM2CKXRIn0zTPfYDx+Oll043g6yonpbJkvR0LDjc8M2/gb2iQm8Gx5Cz4MePLp/D3t+N+oMrMVUhxyNQ9g/hBwXceLy4tfj19geHEMiGG4VJA2TKRnWuSlYP1kxaZtFX38/7t7qxGfrLDY8/nb3tWwOQvgI0lEE4nEEcfa+9fAlXNe6cbDgRNMEJH3WHMuYGZ/GwqId6w4bBnu7MTjwFL5QGCrTiqKo6lDCJ5AYw+imH/HtPQSejMLZcR2erj4UWPMoLBXW3Q37EtaXVuD0BOHzuuCwDmFucgRu/z7icponlvQC5OABX9JuGLGfARzb1+Dp7MX8pQ4cTS60ABv1BhI7YWw53Zj+4sCr8Q8YGXuBqbkZ2OzL2I+IyDOW9XIFv90+RL97Ie2EEBq1ITAwxtktXr4C59XbUEW5NTbpiIC1rw4GMoH3H9/im3MRG6tO+HZDDKw1Ds1GE+L2PiKsGSesuzusGYfzK/APT2DzZi+UQAg1NnoWc4ZybBTIhkklya1INjMbYg6vzoY3paSgkGuYuySBOUxOcruawQGJAVmPACVJQiwe44BUyJwvriPTm/LIfgkpgbgo8ALcZWS9Jusy/WWMgsHBKJEGl97pINmJkogdFaW/De3lmTv4gDMrUh7Z0vTyH2iNlei6jeR/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/df5410c1f4ecabe0ce60abc489f6f04f/e00f7/2019-eviction-notices-and-covid-rates.webp 1000w\"],\n    \"sizes\": \"(max-width: 1000px) 100vw, 1000px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/df5410c1f4ecabe0ce60abc489f6f04f/332ff/2019-eviction-notices-and-covid-rates.png 1000w\"],\n    \"sizes\": \"(max-width: 1000px) 100vw, 1000px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/df5410c1f4ecabe0ce60abc489f6f04f/332ff/2019-eviction-notices-and-covid-rates.png\",\n    \"alt\": \"2019 Eviction Notices and COVID Rates by Neighborhood, San Francisco\",\n    \"title\": \"2019 Eviction Notices and COVID Rates by Neighborhood, San Francisco\",\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 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({\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\": \"1046px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"65.2963671128107%\",\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/b2e848baa908fbd1998bb49a60b1e6da/6ded6/eviction-count-by-nhood.webp 1046w\"],\n    \"sizes\": \"(max-width: 1046px) 100vw, 1046px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/b2e848baa908fbd1998bb49a60b1e6da/49d2b/eviction-count-by-nhood.png 1046w\"],\n    \"sizes\": \"(max-width: 1046px) 100vw, 1046px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/b2e848baa908fbd1998bb49a60b1e6da/49d2b/eviction-count-by-nhood.png\",\n    \"alt\": \"2019 Eviction Notice by Neighborhood, San Francisco\",\n    \"title\": \"2019 Eviction Notice by Neighborhood, San Francisco\",\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, \"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. 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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. 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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. Differences in occupation type correlate strongly with race, and the same Economic Policy Institute \", 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\"), \" mentioned above covers how Black workers disproportionately serve in vulnerable industries.\"), mdx(\"iframe\", {\n    width: \"100%\",\n    height: \"637\",\n    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\",\n    frameBorder: \"0\"\n  }), mdx(\"h3\", {\n    \"id\": \"incarceration\"\n  }, \"Incarceration\"), mdx(\"p\", null, \"Lastly, the extreme per-capita spikes appearing in rural \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://wreg.com/news/small-arkansas-county-dealing-with-rise-in-covid-19-cases/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"Arkansas\"), \", \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.marionstar.com/story/news/local/2020/04/25/marion-prison-ohio-coronavirus-outbreak-seeping-into-larger-community/3026133001/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"Ohio\"), \", and \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.wkrn.com/community/health/coronavirus/trousdale-county-leads-us-in-virus-cases-per-capita-due-to-prison/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"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({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.prisonpolicy.org/virus/virusresponse.html\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"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({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.gaspingforjustice.org/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"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({\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\": \"1280px\"\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\": 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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\": \"Unpacking COVID-19 publication research themes and urban indications (Part II)\",\n  \"author\": \"Yuan Lai\",\n  \"date\": \"2020-07-01T00:00:00.000Z\",\n  \"tags\": [\"Covid\", \"Visualization\", \"Data\", \"Health\"],\n  \"hero\": \"images/network_overlay_100.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, \"My \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://blog.civicdatadesignlab.mit.edu/unpacking-covid-19-publication-research-themes-and-urban-indications-(part-i)\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"previous blog post\"), \" explored \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"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({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://public.tableau.com/profile/yuan5273#!/vizhome/COVID-19OpenResearchViz/Dashboard1\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"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({\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\": \"1392px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"61.7816091954023%\",\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/79727b32404579b5d040c905b7639e36/a8feb/screen-shot-2020-07-01-at-7.34.01-pm.webp 1392w\"],\n    \"sizes\": \"(max-width: 1392px) 100vw, 1392px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/79727b32404579b5d040c905b7639e36/3baca/screen-shot-2020-07-01-at-7.34.01-pm.png 1392w\"],\n    \"sizes\": \"(max-width: 1392px) 100vw, 1392px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/79727b32404579b5d040c905b7639e36/3baca/screen-shot-2020-07-01-at-7.34.01-pm.png\",\n    \"alt\": \"screen shot 2020 07 01 at 7 34 01 pm\",\n    \"title\": \"Figure 1. A dictionary with COVID-19 and urban-related vocabulary.\",\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, \"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({\n    parentName: \"p\"\n  }, {\n    \"src\": \"/5523373a1b88423a1de05084c8be844c/network_growth_animation.gif\",\n    \"alt\": null,\n    \"title\": \"Figure 2. An animated COVID-19 text network visualization.\"\n  }))), 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({\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\": \"2212px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"59.76491862567812%\",\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/bdbc0523df844521267497d91108bfa2/8c820/network_overlay_20.webp 2212w\"],\n    \"sizes\": \"(max-width: 2212px) 100vw, 2212px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/bdbc0523df844521267497d91108bfa2/91fce/network_overlay_20.png 2212w\"],\n    \"sizes\": \"(max-width: 2212px) 100vw, 2212px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/bdbc0523df844521267497d91108bfa2/91fce/network_overlay_20.png\",\n    \"alt\": \"network overlay 20\",\n    \"title\": \"Figure 3. COVID-19 text network visualization with 20% completeness.\",\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, \"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({\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\": \"2212px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"59.76491862567812%\",\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/337aaa6dca07abdc2e5396bc5adb7476/8c820/network_overlay_60.webp 2212w\"],\n    \"sizes\": \"(max-width: 2212px) 100vw, 2212px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/337aaa6dca07abdc2e5396bc5adb7476/91fce/network_overlay_60.png 2212w\"],\n    \"sizes\": \"(max-width: 2212px) 100vw, 2212px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/337aaa6dca07abdc2e5396bc5adb7476/91fce/network_overlay_60.png\",\n    \"alt\": \"network overlay 60\",\n    \"title\": \"Figure 4. COVID-19 text network visualization with 60% completeness.\",\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, \"The graph with 100% completeness reveals broader racial disparities and racism during this pandemic. \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.scientificamerican.com/article/why-racism-not-race-is-a-risk-factor-for-dying-of-covid-191/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"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({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://items.ssrc.org/covid-19-and-the-social-sciences/the-rise-of-anti-asian-hate-in-the-wake-of-covid-19/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"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({\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\": \"2212px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"59.76491862567812%\",\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/0904d0c65999b35e30602cc297e3d935/8c820/network_overlay_100.webp 2212w\"],\n    \"sizes\": \"(max-width: 2212px) 100vw, 2212px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/0904d0c65999b35e30602cc297e3d935/91fce/network_overlay_100.png 2212w\"],\n    \"sizes\": \"(max-width: 2212px) 100vw, 2212px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/0904d0c65999b35e30602cc297e3d935/91fce/network_overlay_100.png\",\n    \"alt\": \"network overlay 100\",\n    \"title\": \"Figure 5. COVID-19 text network visualization with 100% completeness.\",\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, \"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\", {\n    parentName: \"p\"\n  }, \"References\")), mdx(\"p\", null, \"Brainard, J., 2020. New tools aim to tame pandemic paper tsunami.Science. Available: \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://science.sciencemag.org/content/368/6494/924/tab-pdf\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"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({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.nature.com/articles/d41586-020-01733-7\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"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({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://items.ssrc.org/covid-19-and-the-social-sciences/the-rise-of-anti-asian-hate-in-the-wake-of-covid-19/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"https://items.ssrc.org/covid-19-and-the-social-sciences/the-rise-of-anti-asian-hate-in-the-wake-of-covid-19/\")));\n}\n;\nMDXContent.isMDXComponent = 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