Merge branch 'usds:main' into main

client/src/components/AreaDetail/index.tsx

@@ -12,24 +12,26 @@ import * as constants from '../../data/constants';
 import * as EXPLORE_COPY from '../../data/copy/explore';
 import * as METHODOLOGY_COPY from '../../data/copy/methodology';
 
-export const readablePercentile = (percentile: number) => {
+export const readablePercentile = (percentile: number | null) => {
-  return Math.round(percentile * 100);
+  return percentile ? Math.round(percentile * 100) : 'N/A';
 };
 
 // Todo: Add internationalization to superscript ticket #582
-const getSuperscriptOrdinal = (percentile: number) => {
+const getSuperscriptOrdinal = (percentile: number | string) => {
-  const englishOrdinalRules = new Intl.PluralRules('en', {
+  if (typeof percentile === "number") {
-    type: 'ordinal',
+    const englishOrdinalRules = new Intl.PluralRules('en', {
-  });
+      type: 'ordinal',
-  const suffixes = {
+    });
-    zero: 'th',
+    const suffixes = {
-    one: 'st',
+      zero: 'th',
-    two: 'nd',
+      one: 'st',
-    few: 'rd',
+      two: 'nd',
-    many: 'th',
+      few: 'rd',
-    other: 'th',
+      many: 'th',
-  };
+      other: 'th',
-  return suffixes[englishOrdinalRules.select(percentile)];
+    };
+    return suffixes[englishOrdinalRules.select(percentile)];
+  }
 };
 
 interface IAreaDetailProps {
@@ -40,11 +42,13 @@ const AreaDetail = ({properties}:IAreaDetailProps) => {
   const intl = useIntl();
   const [isCommunityFocus, setIsCommunityFocus] = React.useState<boolean>(true);
 
-  const score = properties[constants.SCORE_PROPERTY_HIGH] as number;
+  console.log("Area Detail properies: ", properties);
-  const blockGroup = properties[constants.GEOID_PROPERTY];
+
-  const population = properties[constants.TOTAL_POPULATION];
+  const score = properties[constants.SCORE_PROPERTY_HIGH] ? properties[constants.SCORE_PROPERTY_HIGH] as number : 0;
-  const countyName = properties[constants.COUNTY_NAME];
+  const blockGroup = properties[constants.GEOID_PROPERTY] ? properties[constants.GEOID_PROPERTY] : "N/A";
-  const stateName = properties[constants.STATE_NAME];
+  const population = properties[constants.TOTAL_POPULATION] ? properties[constants.TOTAL_POPULATION] : "N/A";
+  const countyName = properties[constants.COUNTY_NAME] ? properties[constants.COUNTY_NAME] : "N/A";
+  const stateName = properties[constants.STATE_NAME] ? properties[constants.STATE_NAME] : "N/A";
 
   useEffect(() => {
     if (score >= constants.SCORE_BOUNDARY_PRIORITIZED ) {
@@ -65,77 +69,92 @@ const AreaDetail = ({properties}:IAreaDetailProps) => {
   const areaMedianIncome:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.AREA_MEDIAN_INCOME),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.AREA_MEDIAN_INCOME),
-    value: properties[constants.AREA_MEDIAN_INCOME_PERCENTILE],
+    value: properties[constants.AREA_MEDIAN_INCOME_PERCENTILE] ?
+    properties[constants.AREA_MEDIAN_INCOME_PERCENTILE] : null,
   };
   const eduInfo:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.EDUCATION),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.EDUCATION),
-    value: properties[constants.EDUCATION_PROPERTY_PERCENTILE],
+    value: properties[constants.EDUCATION_PROPERTY_PERCENTILE] ?
+    properties[constants.EDUCATION_PROPERTY_PERCENTILE] : null,
   };
   const poverty:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.POVERTY),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.POVERTY),
-    value: properties[constants.POVERTY_PROPERTY_PERCENTILE],
+    value: properties[constants.POVERTY_PROPERTY_PERCENTILE] ?
+    properties[constants.POVERTY_PROPERTY_PERCENTILE] : null,
   };
   const asthma:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.ASTHMA),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.ASTHMA),
-    value: properties[constants.ASTHMA_PERCENTILE],
+    value: properties[constants.ASTHMA_PERCENTILE] ?
+    properties[constants.ASTHMA_PERCENTILE] : null,
   };
   const diabetes:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.DIABETES),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.DIABETES),
-    value: properties[constants.DIABETES_PERCENTILE],
+    value: properties[constants.DIABETES_PERCENTILE] ?
+    properties[constants.DIABETES_PERCENTILE] : null,
   };
   const dieselPartMatter:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.DIESEL_PARTICULATE_MATTER),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.DIESEL_PARTICULATE_MATTER),
-    value: properties[constants.DIESEL_MATTER_PERCENTILE],
+    value: properties[constants.DIESEL_MATTER_PERCENTILE] ?
+    properties[constants.DIESEL_MATTER_PERCENTILE] : null,
   };
   const lifeExpect:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.LIFE_EXPECT),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.LIFE_EXPECT),
-    value: properties[constants.LIFE_PERCENTILE],
+    value: properties[constants.LIFE_PERCENTILE] ?
+    properties[constants.LIFE_PERCENTILE] : null,
   };
   const energyBurden:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.ENERGY_BURDEN),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.ENERGY_BURDEN),
-    value: properties[constants.ENERGY_PERCENTILE],
+    value: properties[constants.ENERGY_PERCENTILE] ?
+    properties[constants.ENERGY_PERCENTILE] : null,
   };
   const pm25:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.PM_2_5),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.PM_2_5),
-    value: properties[constants.PM25_PERCENTILE],
+    value: properties[constants.PM25_PERCENTILE] ?
+    properties[constants.PM25_PERCENTILE] : null,
   };
   const leadPaint:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.LEAD_PAINT),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.LEAD_PAINT),
-    value: properties[constants.LEAD_PAINT_PERCENTILE],
+    value: properties[constants.LEAD_PAINT_PERCENTILE] ?
+    properties[constants.LEAD_PAINT_PERCENTILE] : null,
   };
   const trafficVolume:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.TRAFFIC_VOLUME),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.TRAFFIC_VOLUME),
-    value: properties[constants.TRAFFIC_PERCENTILE],
+    value: properties[constants.TRAFFIC_PERCENTILE] ?
+    properties[constants.TRAFFIC_PERCENTILE] : null,
   };
   const wasteWater:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.WASTE_WATER),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.WASTE_WATER),
-    value: properties[constants.WASTEWATER_PERCENTILE],
+    value: properties[constants.WASTEWATER_PERCENTILE] ?
+    properties[constants.WASTEWATER_PERCENTILE] : null,
   };
   const femaRisk:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.FEMA_RISK),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.FEMA_RISK),
-    value: properties[constants.FEMA_PERCENTILE],
+    value: properties[constants.FEMA_PERCENTILE] ?
+    properties[constants.FEMA_PERCENTILE] : null,
   };
   const heartDisease:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.HEART_DISEASE),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.HEART_DISEASE),
-    value: properties[constants.HEART_PERCENTILE],
+    value: properties[constants.HEART_PERCENTILE] ?
+    properties[constants.HEART_PERCENTILE] : null,
   };
   const houseBurden:indicatorInfo = {
     label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.HOUSE_BURDEN),
     description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.HOUSE_BURDEN),
-    value: properties[constants.HOUSING_BURDEN_PROPERTY_PERCENTILE],
+    value: properties[constants.HOUSING_BURDEN_PROPERTY_PERCENTILE] ?
+    properties[constants.HOUSING_BURDEN_PROPERTY_PERCENTILE] : null,
   };
 
 

client/src/components/AreaDetail/tests/__snapshots__/areaDetail.test.tsx.snap

@@ -19,7 +19,7 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
           County:
         </span>
         <span>
-           undefined
+           N/A
         </span>
       </li>
       <li>
@@ -27,7 +27,7 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
           State: 
         </span>
         <span>
-           undefined
+           N/A
         </span>
       </li>
       <li>
@@ -134,11 +134,9 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
           Asthma
         </h4>
         <div>
-          NaN
+          N/A
           <sup>
-            <span>
+            <span />
-              th
-            </span>
           </sup>
         </div>
       </div>
@@ -156,11 +154,9 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
           Diabetes
         </h4>
         <div>
-          NaN
+          N/A
           <sup>
-            <span>
+            <span />
-              th
-            </span>
           </sup>
         </div>
       </div>
@@ -178,11 +174,9 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
           Diesel particulate matter
         </h4>
         <div>
-          NaN
+          N/A
           <sup>
-            <span>
+            <span />
-              th
-            </span>
           </sup>
         </div>
       </div>
@@ -200,11 +194,9 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
           Energy burden
         </h4>
         <div>
-          NaN
+          N/A
           <sup>
-            <span>
+            <span />
-              th
-            </span>
           </sup>
         </div>
       </div>
@@ -222,11 +214,9 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
           FEMA Risk Index
         </h4>
         <div>
-          NaN
+          N/A
           <sup>
-            <span>
+            <span />
-              th
-            </span>
           </sup>
         </div>
       </div>
@@ -244,11 +234,9 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
           Heart disease
         </h4>
         <div>
-          NaN
+          N/A
           <sup>
-            <span>
+            <span />
-              th
-            </span>
           </sup>
         </div>
       </div>
@@ -288,11 +276,9 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
           Lead paint
         </h4>
         <div>
-          NaN
+          N/A
           <sup>
-            <span>
+            <span />
-              th
-            </span>
           </sup>
         </div>
       </div>
@@ -310,11 +296,9 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
           Life expectancy
         </h4>
         <div>
-          NaN
+          N/A
           <sup>
-            <span>
+            <span />
-              th
-            </span>
           </sup>
         </div>
       </div>
@@ -332,11 +316,9 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
           PM2.5
         </h4>
         <div>
-          NaN
+          N/A
           <sup>
-            <span>
+            <span />
-              th
-            </span>
           </sup>
         </div>
       </div>
@@ -354,11 +336,9 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
           Traffic proximity and volume
         </h4>
         <div>
-          NaN
+          N/A
           <sup>
-            <span>
+            <span />
-              th
-            </span>
           </sup>
         </div>
       </div>
@@ -376,11 +356,9 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
           Wastewater discharge
         </h4>
         <div>
-          NaN
+          N/A
           <sup>
-            <span>
+            <span />
-              th
-            </span>
           </sup>
         </div>
       </div>

client/src/components/J40Map.tsx

@@ -1,6 +1,6 @@
 /* eslint-disable no-unused-vars */
 // External Libs:
-import React, {MouseEvent, useRef, useState, useMemo} from 'react';
+import React, {useRef, useState, useMemo} from 'react';
 import {Map, MapboxGeoJSONFeature, LngLatBoundsLike} from 'maplibre-gl';
 import ReactMapGL, {
   MapEvent,
@@ -136,29 +136,6 @@ const J40Map = ({location}: IJ40Interface) => {
     });
   };
 
-  const onClickTerritoryFocusButton = (event: MouseEvent<HTMLButtonElement>) => {
-    event.stopPropagation();
-    const buttonID = event.target && (event.target as HTMLElement).id;
-
-    switch (buttonID) {
-      case '48':
-        goToPlace(constants.LOWER_48_BOUNDS);
-        break;
-      case 'AK':
-        goToPlace(constants.ALASKA_BOUNDS);
-        break;
-      case 'HI':
-        goToPlace(constants.HAWAII_BOUNDS);
-        break;
-      case 'PR':
-        goToPlace(constants.PUERTO_RICO_BOUNDS);
-        break;
-
-      default:
-        break;
-    }
-  };
-
   const onTransitionStart = () => {
     setTransitionInProgress(true);
   };
@@ -273,7 +250,7 @@ const J40Map = ({location}: IJ40Interface) => {
             onClick={onClickGeolocate}
           /> : ''}
           {geolocationInProgress ? <div>Geolocation in progress...</div> : ''}
-          <TerritoryFocusControl onClickTerritoryFocusButton={onClickTerritoryFocusButton}/>
+          <TerritoryFocusControl goToPlace={goToPlace}/>
           {'fs' in flags ? <FullscreenControl className={styles.fullscreenControl}/> :'' }
 
         </ReactMapGL>

client/src/components/territoryFocusControl.tsx

@@ -1,21 +1,53 @@
 import {useIntl} from 'gatsby-plugin-intl';
-import React, {MouseEventHandler} from 'react';
+import React from 'react';
-import {_useMapControl as useMapControl} from 'react-map-gl';
+import {LngLatBoundsLike} from 'mapbox-gl';
 
 import * as styles from './territoryFocusControl.module.scss';
 import * as EXPLORE_COPY from '../data/copy/explore';
+import * as constants from '../data/constants';
 
 interface ITerritoryFocusControl {
-  onClickTerritoryFocusButton: MouseEventHandler<HTMLButtonElement>;
+  goToPlace(bounds: LngLatBoundsLike): void;
 }
 
-const TerritoryFocusControl = ({onClickTerritoryFocusButton}: ITerritoryFocusControl) => {
+
+const TerritoryFocusControl = ({goToPlace}: ITerritoryFocusControl) => {
   const intl = useIntl();
 
-  const {containerRef} = useMapControl({
+  const onClickTerritoryFocusButton = (event: React.MouseEvent<HTMLButtonElement>) => {
-    // @ts-ignore // Types have not caught up yet, see https://github.com/visgl/react-map-gl/issues/1492
+    event.stopPropagation();
-    onClick: onClickTerritoryFocusButton,
+    const buttonID = event.target && (event.target as HTMLElement).id;
-  });
+
+    switch (buttonID) {
+      case '48':
+        goToPlace(constants.LOWER_48_BOUNDS);
+        break;
+      case 'AK':
+        goToPlace(constants.ALASKA_BOUNDS);
+        break;
+      case 'HI':
+        goToPlace(constants.HAWAII_BOUNDS);
+        break;
+      case 'PR':
+        goToPlace(constants.PUERTO_RICO_BOUNDS);
+        break;
+      case 'GU':
+        goToPlace(constants.GUAM_BOUNDS);
+        break;
+      case 'AS':
+        goToPlace(constants.AMERICAN_SAMOA_BOUNDS);
+        break;
+      case 'MP':
+        goToPlace(constants.MARIANA_ISLAND_BOUNDS);
+        break;
+      case 'VI':
+        goToPlace(constants.US_VIRGIN_ISLANDS_BOUNDS);
+        break;
+
+      default:
+        break;
+    }
+  };
 
   const territories = [
     {
@@ -34,6 +66,22 @@ const TerritoryFocusControl = ({onClickTerritoryFocusButton}: ITerritoryFocusCon
       short: intl.formatMessage(EXPLORE_COPY.MAP.PR_SHORT),
       long: intl.formatMessage(EXPLORE_COPY.MAP.PR_LONG),
     },
+    {
+      short: intl.formatMessage(EXPLORE_COPY.MAP.GU_SHORT),
+      long: intl.formatMessage(EXPLORE_COPY.MAP.GU_LONG),
+    },
+    {
+      short: intl.formatMessage(EXPLORE_COPY.MAP.AS_SHORT),
+      long: intl.formatMessage(EXPLORE_COPY.MAP.AS_LONG),
+    },
+    {
+      short: intl.formatMessage(EXPLORE_COPY.MAP.MP_SHORT),
+      long: intl.formatMessage(EXPLORE_COPY.MAP.MP_LONG),
+    },
+    {
+      short: intl.formatMessage(EXPLORE_COPY.MAP.VI_SHORT),
+      long: intl.formatMessage(EXPLORE_COPY.MAP.VI_LONG),
+    },
   ];
   // the offset for this array should map the territories variable
   const territoriesIconClassName = [
@@ -41,16 +89,20 @@ const TerritoryFocusControl = ({onClickTerritoryFocusButton}: ITerritoryFocusCon
     'mapboxgl-ctrl-zoom-to-ak',
     'mapboxgl-ctrl-zoom-to-hi',
     'mapboxgl-ctrl-zoom-to-pr',
+    'mapboxgl-ctrl-zoom-to-gu',
+    'mapboxgl-ctrl-zoom-to-as',
+    'mapboxgl-ctrl-zoom-to-mp',
+    'mapboxgl-ctrl-zoom-to-vi',
   ];
 
   return (
-    <div ref={containerRef} className={styles.territoryFocusContainer}>
+    <div className={styles.territoryFocusContainer}>
       <div className={'mapboxgl-ctrl mapboxgl-ctrl-group'}>
         {territories.map((territory, index) =>
           <button
             id={territory.short}
             key={territory.short}
-            onClick={onClickTerritoryFocusButton}
+            onClick={(e) => onClickTerritoryFocusButton(e)}
             className={'mapboxgl-ctrl-icon ' + territoriesIconClassName[index]}
             aria-label={intl.formatMessage(
                 {

client/src/data/constants.tsx

@@ -121,6 +121,11 @@ export const AMERICAN_SAMOA_BOUNDS: LngLatBoundsLike = [
   [-168.1433, -11.046934],
 ];
 
+export const US_VIRGIN_ISLANDS_BOUNDS: LngLatBoundsLike = [
+  [-65.5782239, 17.6739145],
+  [-64.2704123, 18.7495796],
+];
+
 export const DEFAULT_CENTER = [32.4687126, -86.502136];
 
 // Opacity

client/src/data/copy/explore.tsx

@@ -57,6 +57,16 @@ export const MAP = defineMessages({
     defaultMessage: 'Zoom in to the state or regional level to see prioritized communities on the map.',
     description: 'zoom warning on map',
   },
+  SEARCH_PLACEHOLDER: {
+    id: 'map.search.placeholder.text',
+    defaultMessage: 'Enter a city, state or ZIP',
+    description: 'placeholder text for search',
+  },
+  SEARCH_RESULTS_EMPTY_MESSAGE: {
+    id: 'map.search.results.empty.text',
+    defaultMessage: 'No location found. Please try another location.',
+    description: 'text displaying message for no search results found',
+  },
   LOWER48_SHORT: {
     id: 'map.territoryFocus.lower48.short',
     defaultMessage: '48',
@@ -97,15 +107,45 @@ export const MAP = defineMessages({
     defaultMessage: 'Puerto Rico',
     description: 'The full name indicating the bounds of Puerto Rico',
   },
-  SEARCH_PLACEHOLDER: {
+  GU_SHORT: {
-    id: 'map.search.placeholder.text',
+    id: 'map.territoryFocus.guam.short',
-    defaultMessage: 'Enter a city, state or ZIP',
+    defaultMessage: 'GU',
-    description: 'placeholder text for search',
+    description: 'The abbreviated name indicating the bounds of Guam',
   },
-  SEARCH_RESULTS_EMPTY_MESSAGE: {
+  GU_LONG: {
-    id: 'map.search.results.empty.text',
+    id: 'map.territoryFocus.guam.long',
-    defaultMessage: 'No location found. Please try another location.',
+    defaultMessage: 'Guam',
-    description: 'text displaying message for no search results found',
+    description: 'The full name indicating the bounds of Guam',
+  },
+  AS_SHORT: {
+    id: 'map.territoryFocus.american.samoa.short',
+    defaultMessage: 'AS',
+    description: 'The abbreviated name indicating the bounds of American Somoa',
+  },
+  AS_LONG: {
+    id: 'map.territoryFocus.american.samoa.long',
+    defaultMessage: 'American Somoa',
+    description: 'The full name indicating the bounds of American Somoa',
+  },
+  MP_SHORT: {
+    id: 'map.territoryFocus.commonwealth.nmp.short',
+    defaultMessage: 'MP',
+    description: 'The abbreviated name indicating the bounds of Commonwealth of Northern Mariana Islands',
+  },
+  MP_LONG: {
+    id: 'map.territoryFocus.commonwealth.nmp.long',
+    defaultMessage: 'Commonwealth of Northern Mariana Islands',
+    description: 'The full name indicating the bounds of Commonwealth of Northern Mariana Islands',
+  },
+  VI_SHORT: {
+    id: 'map.territoryFocus.us.virgin.islands.short',
+    defaultMessage: 'VI',
+    description: 'The abbreviated name indicating the bounds of US Virgin Islands',
+  },
+  VI_LONG: {
+    id: 'map.territoryFocus.us.virgin.islands.long',
+    defaultMessage: 'US Virgin Islands',
+    description: 'The full name indicating the bounds of US Virgin Islands',
   },
 });
 

client/src/images/mapbtn-48.svg

@@ -1 +1,21 @@
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client/src/images/mapbtn-AK.svg

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client/src/images/mapbtn-AS.svg

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client/src/images/mapbtn-GU.svg

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client/src/images/mapbtn-HI.svg

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client/src/images/mapbtn-MP.svg

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client/src/images/mapbtn-PR.svg

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+		C8.7,7.8,8.2,7.7,7.5,7.7H6.4V10.9z"/>
+	<path d="M12.9,15.6V6.4H16c0.6,0,1.2,0.1,1.7,0.3s0.9,0.5,1.2,0.8c0.3,0.4,0.4,0.9,0.4,1.6c0,0.7-0.2,1.3-0.5,1.7
+		c-0.3,0.4-0.8,0.7-1.3,0.9l2.2,3.8h-1.8l-2-3.6h-1.4v3.6H12.9z M14.5,10.7h1.3c0.6,0,1.1-0.1,1.4-0.4c0.3-0.3,0.5-0.7,0.5-1.2
+		c0-0.5-0.2-0.9-0.5-1.1c-0.3-0.2-0.8-0.3-1.4-0.3h-1.3V10.7z"/>
+</g>
+</svg>

client/src/images/mapbtn-VI.svg

@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 26.0.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 24 24" style="enable-background:new 0 0 24 24;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill:none;}
+</style>
+<path class="st0" d="M0,0h24v24H0L0,0z"/>
+<g>
+	<path d="M9,15.6L6.1,6.4h1.7L9.1,11c0.1,0.5,0.3,1,0.4,1.5c0.1,0.5,0.3,1,0.4,1.5H10c0.2-0.5,0.3-1,0.4-1.5c0.1-0.5,0.2-1,0.4-1.5
+		l1.3-4.6h1.7l-2.8,9.2H9z"/>
+	<path d="M14.8,15.6V6.4h1.6v9.2H14.8z"/>
+</g>
+</svg>

client/src/styles/global.scss

@@ -262,6 +262,30 @@ This section will outline styles that are component specific
       background-image: url("../images/mapbtn-PR.svg");
     }
   }
+
+  button.mapboxgl-ctrl-zoom-to-gu {
+    .mapboxgl-ctrl-icon {
+      background-image: url("../images/mapbtn-GU.svg");
+    }
+  }
+
+  button.mapboxgl-ctrl-zoom-to-as {
+    .mapboxgl-ctrl-icon {
+      background-image: url("../images/mapbtn-AS.svg");
+    }
+  }
+
+  button.mapboxgl-ctrl-zoom-to-mp {
+    .mapboxgl-ctrl-icon {
+      background-image: url("../images/mapbtn-MP.svg");
+    }
+  }
+
+  button.mapboxgl-ctrl-zoom-to-vi {
+    .mapboxgl-ctrl-icon {
+      background-image: url("../images/mapbtn-VI.svg");
+    }
+  }
 }
 
 //Area Detail Component

data/data-pipeline/data_pipeline/etl/constants.py

@@ -49,6 +49,11 @@ DATASET_LIST = [
         "module_dir": "geocorr",
         "class_name": "GeoCorrETL",
     },
+    {
+        "name": "child_opportunity_index",
+        "module_dir": "child_opportunity_index",
+        "class_name": "ChildOpportunityIndex",
+    },
     {
         "name": "mapping_inequality",
         "module_dir": "mapping_inequality",

data/data-pipeline/data_pipeline/etl/score/constants.py

@@ -4,6 +4,8 @@ import datetime
 import pandas as pd
 from data_pipeline.config import settings
 
+from data_pipeline.score import field_names
+
 # Base Paths
 DATA_PATH = Path(settings.APP_ROOT) / "data"
 TMP_PATH = DATA_PATH / "tmp"
@@ -59,88 +61,92 @@ SCORE_DOWNLOADABLE_ZIP_FILE_PATH = (
 # Column subsets
 CENSUS_COUNTIES_COLUMNS = ["USPS", "GEOID", "NAME"]
 TILES_SCORE_COLUMNS = [
-    "GEOID10_TRACT",
+    field_names.GEOID_TRACT_FIELD,
-    "State Name",
+    field_names.STATE_FIELD,
-    "County Name",
+    field_names.COUNTY_FIELD,
-    "Total population",
+    field_names.TOTAL_POP_FIELD,
-    "Score D (percentile)",
+    field_names.SCORE_D + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Score D (top 25th percentile)",
+    field_names.SCORE_D + field_names.TOP_25_PERCENTILE_SUFFIX,
-    "Score E (percentile)",
+    field_names.SCORE_E + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Score E (top 25th percentile)",
+    field_names.SCORE_E + field_names.TOP_25_PERCENTILE_SUFFIX,
-    "Score G (communities)",
+    field_names.SCORE_G_COMMUNITIES,
-    "Score G",
+    field_names.SCORE_G,
-    "Definition L (communities)",
+    field_names.SCORE_L_COMMUNITIES,
-    "Definition L (percentile)",
+    field_names.SCORE_L + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Poverty (Less than 200% of federal poverty line) (percentile)",
+    field_names.POVERTY_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Percent individuals age 25 or over with less than high school degree (percentile)",
+    field_names.HIGH_SCHOOL_ED_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Linguistic isolation (percent) (percentile)",
+    field_names.LINGUISTIC_ISO_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Unemployed civilians (percent) (percentile)",
+    field_names.UNEMPLOYMENT_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Housing burden (percent) (percentile)",
+    field_names.HOUSING_BURDEN_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Diagnosed diabetes among adults aged >=18 years (percentile)",
+    field_names.DIABETES_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Current asthma among adults aged >=18 years (percentile)",
+    field_names.ASTHMA_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Coronary heart disease among adults aged >=18 years (percentile)",
+    field_names.HEART_DISEASE_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Life expectancy (years) (percentile)",
+    field_names.LIFE_EXPECTANCY_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Traffic proximity and volume (percentile)",
+    field_names.TRAFFIC_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "FEMA Risk Index Expected Annual Loss Score (percentile)",
+    field_names.FEMA_RISK_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Energy burden (percentile)",
+    field_names.ENERGY_BURDEN_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Wastewater discharge (percentile)",
+    field_names.WASTEWATER_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Percent pre-1960s housing (lead paint indicator) (percentile)",
+    field_names.LEAD_PAINT_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Diesel particulate matter (percentile)",
+    field_names.DIESEL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Particulate matter (PM2.5) (percentile)",
+    field_names.PM25_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Median household income (% of AMI) (percentile)",
+    field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD
-    "Percent of individuals < 200% Federal Poverty Line (percentile)",
+    + field_names.PERCENTILE_FIELD_SUFFIX,
+    field_names.POVERTY_LESS_THAN_200_FPL_FIELD
+    + field_names.PERCENTILE_FIELD_SUFFIX,
 ]
 
 # columns to round floats to 2 decimals
 TILES_SCORE_FLOAT_COLUMNS = [
-    "Score D (percentile)",
+    field_names.SCORE_D + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Score D (top 25th percentile)",
+    field_names.SCORE_D + field_names.TOP_25_PERCENTILE_SUFFIX,
-    "Score E (percentile)",
+    field_names.SCORE_E + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Score E (top 25th percentile)",
+    field_names.SCORE_E + field_names.TOP_25_PERCENTILE_SUFFIX,
-    "Definition L (percentile)",
+    field_names.SCORE_L + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Poverty (Less than 200% of federal poverty line)",
+    field_names.POVERTY_FIELD,
-    "Percent individuals age 25 or over with less than high school degree",
+    field_names.HIGH_SCHOOL_ED_FIELD,
-    "Linguistic isolation (percent)",
+    field_names.LINGUISTIC_ISO_FIELD,
-    "Unemployed civilians (percent)",
+    field_names.UNEMPLOYMENT_FIELD,
-    "Housing burden (percent)",
+    field_names.HOUSING_BURDEN_FIELD,
-    "Poverty (Less than 200% of federal poverty line) (percentile)",
+    field_names.POVERTY_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Percent individuals age 25 or over with less than high school degree (percentile)",
+    field_names.HIGH_SCHOOL_ED_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Linguistic isolation (percent) (percentile)",
+    field_names.LINGUISTIC_ISO_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Unemployed civilians (percent) (percentile)",
+    field_names.UNEMPLOYMENT_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Housing burden (percent) (percentile)",
+    field_names.HOUSING_BURDEN_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Diagnosed diabetes among adults aged >=18 years (percentile)",
+    field_names.DIABETES_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Current asthma among adults aged >=18 years (percentile)",
+    field_names.ASTHMA_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Coronary heart disease among adults aged >=18 years (percentile)",
+    field_names.HEART_DISEASE_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Life expectancy (years) (percentile)",
+    field_names.LIFE_EXPECTANCY_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Traffic proximity and volume (percentile)",
+    field_names.TRAFFIC_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "FEMA Risk Index Expected Annual Loss Score (percentile)",
+    field_names.FEMA_RISK_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Energy burden (percentile)",
+    field_names.ENERGY_BURDEN_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Wastewater discharge (percentile)",
+    field_names.WASTEWATER_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Percent pre-1960s housing (lead paint indicator) (percentile)",
+    field_names.LEAD_PAINT_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Diesel particulate matter (percentile)",
+    field_names.DIESEL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Particulate matter (PM2.5) (percentile)",
+    field_names.PM25_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-    "Median household income (% of AMI) (percentile)",
+    field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD
-    "Percent of individuals < 200% Federal Poverty Line (percentile)",
+    + field_names.PERCENTILE_FIELD_SUFFIX,
+    field_names.POVERTY_LESS_THAN_200_FPL_FIELD
+    + field_names.PERCENTILE_FIELD_SUFFIX,
 ]
 TILES_ROUND_NUM_DECIMALS = 2
 
 DOWNLOADABLE_SCORE_INDICATOR_COLUMNS_BASIC = [
-    "Area Median Income (State or metropolitan)",
+    field_names.AMI_FIELD,
-    "Percent of individuals < 100% Federal Poverty Line",
+    field_names.POVERTY_LESS_THAN_100_FPL_FIELD,
-    "Percent individuals age 25 or over with less than high school degree",
+    field_names.HIGH_SCHOOL_ED_FIELD,
-    "Diagnosed diabetes among adults aged >=18 years",
+    field_names.DIABETES_FIELD,
-    "Current asthma among adults aged >=18 years",
+    field_names.ASTHMA_FIELD,
-    "Coronary heart disease among adults aged >=18 years",
+    field_names.HEART_DISEASE_FIELD,
-    "Life expectancy (years)",
+    field_names.LIFE_EXPECTANCY_FIELD,
-    "Traffic proximity and volume",
+    field_names.TRAFFIC_FIELD,
-    "FEMA Risk Index Expected Annual Loss Score",
+    field_names.FEMA_RISK_FIELD,
-    "Energy burden",
+    field_names.ENERGY_BURDEN_FIELD,
-    "Housing burden (percent)",
+    field_names.HOUSING_BURDEN_FIELD,
-    "Wastewater discharge",
+    field_names.WASTEWATER_FIELD,
-    "Percent pre-1960s housing (lead paint indicator)",
+    field_names.LEAD_PAINT_FIELD,
-    "Diesel particulate matter",
+    field_names.DIESEL_FIELD,
-    "Particulate matter (PM2.5)",
+    field_names.PM25_FIELD,
-    "Total population",
+    field_names.TOTAL_POP_FIELD,
 ]
 
 # For every indicator above, we want to include percentile and min-max normalized variants also
@@ -155,11 +161,12 @@ DOWNLOADABLE_SCORE_INDICATOR_COLUMNS_FULL = list(
 
 # Finally we augment with the GEOID10, county, and state
 DOWNLOADABLE_SCORE_COLUMNS = [
-    "GEOID10_TRACT",
+    field_names.GEOID_TRACT_FIELD,
-    "County Name",
+    field_names.COUNTY_FIELD,
-    "State Name",
+    field_names.STATE_FIELD,
-    "Score G (communities)",
+    field_names.SCORE_G_COMMUNITIES,
-    "Median household income (% of AMI)",
+    field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD,
-    "Median household income (% of state median household income) (percentile)",
+    field_names.MEDIAN_INCOME_AS_PERCENT_OF_STATE_FIELD
+    + field_names.PERCENTILE_FIELD_SUFFIX,
     *DOWNLOADABLE_SCORE_INDICATOR_COLUMNS_FULL,
 ]

data/data-pipeline/data_pipeline/etl/score/etl_score.py

@@ -1,4 +1,6 @@
 import functools
+from collections import namedtuple
+
 import pandas as pd
 
 from data_pipeline.etl.base import ExtractTransformLoad
@@ -29,6 +31,7 @@ class ScoreETL(ExtractTransformLoad):
         self.persistent_poverty_df: pd.DataFrame
         self.census_decennial_df: pd.DataFrame
         self.census_2010_df: pd.DataFrame
+        self.child_opportunity_index_df: pd.DataFrame
 
     def extract(self) -> None:
         logger.info("Loading data sets from disk.")
@@ -162,6 +165,19 @@ class ScoreETL(ExtractTransformLoad):
             low_memory=False,
         )
 
+        # Load COI data
+        child_opportunity_index_csv = (
+            constants.DATA_PATH
+            / "dataset"
+            / "child_opportunity_index"
+            / "usa.csv"
+        )
+        self.child_opportunity_index_df = pd.read_csv(
+            child_opportunity_index_csv,
+            dtype={self.GEOID_TRACT_FIELD_NAME: "string"},
+            low_memory=False,
+        )
+
     def _join_tract_dfs(self, census_tract_dfs: list) -> pd.DataFrame:
         logger.info("Joining Census Tract dataframes")
 
@@ -255,6 +271,7 @@ class ScoreETL(ExtractTransformLoad):
             self.census_acs_median_incomes_df,
             self.census_decennial_df,
             self.census_2010_df,
+            self.child_opportunity_index_df,
         ]
 
         # Sanity check each data frame before merging.
@@ -305,7 +322,7 @@ class ScoreETL(ExtractTransformLoad):
             field_names.FEMA_RISK_FIELD,
             field_names.URBAN_HEURISTIC_FIELD,
             field_names.AIR_TOXICS_CANCER_RISK_FIELD,
-            field_names.RESPITORY_HAZARD_FIELD,
+            field_names.RESPIRATORY_HAZARD_FIELD,
             field_names.DIESEL_FIELD,
             field_names.PM25_FIELD,
             field_names.OZONE_FIELD,
@@ -323,6 +340,7 @@ class ScoreETL(ExtractTransformLoad):
             field_names.HIGH_SCHOOL_ED_FIELD,
             field_names.UNEMPLOYMENT_FIELD,
             field_names.MEDIAN_HOUSE_VALUE_FIELD,
+            field_names.COLLEGE_ATTENDANCE_FIELD,
             field_names.EXPECTED_BUILDING_LOSS_RATE_FIELD,
             field_names.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD,
             field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD,
@@ -333,6 +351,9 @@ class ScoreETL(ExtractTransformLoad):
             field_names.CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010,
             field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2009,
             field_names.CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009,
+            field_names.EXTREME_HEAT_FIELD,
+            field_names.HEALTHY_FOOD_FIELD,
+            field_names.IMPENETRABLE_SURFACES_FIELD,
         ]
 
         non_numeric_columns = [
@@ -340,7 +361,32 @@ class ScoreETL(ExtractTransformLoad):
             field_names.PERSISTENT_POVERTY_FIELD,
         ]
 
-        columns_to_keep = non_numeric_columns + numeric_columns
+        # For some columns, high values are "good", so we want to reverse the percentile
+        # so that high values are "bad" and any scoring logic can still check if it's
+        # >= some threshold.
+        # TODO: Add more fields here.
+        #  https://github.com/usds/justice40-tool/issues/970
+        ReversePercentile = namedtuple(
+            typename="ReversePercentile",
+            field_names=["field_name", "low_field_name"],
+        )
+        reverse_percentiles = [
+            # This dictionary follows the format:
+            # <field name> : <field name for low values>
+            # for instance, 3rd grade reading level : Low 3rd grade reading level.
+            # This low field will not exist yet, it is only calculated for the
+            # percentile.
+            ReversePercentile(
+                field_name=field_names.READING_FIELD,
+                low_field_name=field_names.LOW_READING_FIELD,
+            )
+        ]
+
+        columns_to_keep = (
+            non_numeric_columns
+            + numeric_columns
+            + [rp.field_name for rp in reverse_percentiles]
+        )
 
         df_copy = df[columns_to_keep].copy()
 
@@ -375,6 +421,19 @@ class ScoreETL(ExtractTransformLoad):
                 df_copy[col] - min_value
             ) / (max_value - min_value)
 
+        # Create reversed percentiles for these fields
+        for reverse_percentile in reverse_percentiles:
+            # Calculate reverse percentiles
+            # For instance, for 3rd grade reading level (score from 0-500),
+            # calculate reversed percentiles and give the result the name
+            # `Low 3rd grade reading level (percentile)`.
+            df_copy[
+                f"{reverse_percentile.low_field_name}"
+                f"{field_names.PERCENTILE_FIELD_SUFFIX}"
+            ] = df_copy[reverse_percentile.field_name].rank(
+                pct=True, ascending=False
+            )
+
         # Special logic: create a combined population field.
         # We sometimes run analytics on "population", and this makes a single field
         # that is either the island area's population in 2009 or the state's

data/data-pipeline/data_pipeline/etl/score/etl_score_post.py

@@ -276,12 +276,15 @@ class PostScoreETL(ExtractTransformLoad):
             inplace=False,
         )
 
-        logger.info("Writing downloadable csv")
-        downloadable_df_copy.to_csv(csv_path, index=False)
-
         logger.info("Writing downloadable excel")
         downloadable_df_copy.to_excel(excel_path, index=False)
 
+        logger.info("Writing downloadable csv")
+        downloadable_df_copy[self.GEOID_TRACT_FIELD_NAME] = (
+            '"' + downloadable_df_copy[self.GEOID_TRACT_FIELD_NAME] + '"'
+        )
+        downloadable_df_copy.to_csv(csv_path, index=False)
+
         logger.info("Compressing files")
         files_to_compress = [csv_path, excel_path, pdf_path]
         zip_files(zip_path, files_to_compress)

data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py

@@ -114,6 +114,27 @@ class CensusACSETL(ExtractTransformLoad):
         )
         self.HIGH_SCHOOL_ED_FIELD = "Percent individuals age 25 or over with less than high school degree"
 
+        # College attendance fields
+        self.COLLEGE_ATTENDANCE_TOTAL_POPULATION_ASKED = (
+            "B14004_001E"  # Estimate!!Total
+        )
+        self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PUBLIC = "B14004_003E"  # Estimate!!Total!!Male!!Enrolled in public college or graduate school
+        self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PRIVATE = "B14004_008E"  # Estimate!!Total!!Male!!Enrolled in private college or graduate school
+        self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PUBLIC = "B14004_019E"  # Estimate!!Total!!Female!!Enrolled in public college or graduate school
+        self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PRIVATE = "B14004_024E"  # Estimate!!Total!!Female!!Enrolled in private college or graduate school
+
+        self.COLLEGE_ATTENDANCE_FIELDS = [
+            self.COLLEGE_ATTENDANCE_TOTAL_POPULATION_ASKED,
+            self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PUBLIC,
+            self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PRIVATE,
+            self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PUBLIC,
+            self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PRIVATE,
+        ]
+
+        self.COLLEGE_ATTENDANCE_FIELD = (
+            "Percent enrollment in college or graduate school"
+        )
+
         self.RE_FIELDS = [
             "B02001_001E",
             "B02001_002E",
@@ -156,15 +177,30 @@ class CensusACSETL(ExtractTransformLoad):
 
         self.STATE_GEOID_FIELD_NAME = "GEOID2"
 
+        self.COLUMNS_TO_KEEP = (
+            [
+                self.GEOID_TRACT_FIELD_NAME,
+                self.UNEMPLOYED_FIELD_NAME,
+                self.LINGUISTIC_ISOLATION_FIELD_NAME,
+                self.MEDIAN_INCOME_FIELD_NAME,
+                self.POVERTY_LESS_THAN_100_PERCENT_FPL_FIELD_NAME,
+                self.POVERTY_LESS_THAN_150_PERCENT_FPL_FIELD_NAME,
+                self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME,
+                self.MEDIAN_HOUSE_VALUE_FIELD_NAME,
+                self.HIGH_SCHOOL_ED_FIELD,
+                self.COLLEGE_ATTENDANCE_FIELD,
+            ]
+            + self.RE_OUTPUT_FIELDS
+            + [self.PERCENT_PREFIX + field for field in self.RE_OUTPUT_FIELDS]
+        )
+
         self.df: pd.DataFrame
 
     def extract(self) -> None:
         # Define the variables to retrieve
         variables = (
             [
-                # Income field
                 self.MEDIAN_INCOME_FIELD,
-                # House value
                 self.MEDIAN_HOUSE_VALUE_FIELD,
             ]
             + self.EMPLOYMENT_FIELDS
@@ -172,6 +208,7 @@ class CensusACSETL(ExtractTransformLoad):
             + self.POVERTY_FIELDS
             + self.EDUCATIONAL_FIELDS
             + self.RE_FIELDS
+            + self.COLLEGE_ATTENDANCE_FIELDS
         )
 
         self.df = retrieve_census_acs_data(
@@ -308,6 +345,14 @@ class CensusACSETL(ExtractTransformLoad):
             df["B03003_003E"] / df["B03003_001E"]
         )
 
+        # Calculate college attendance:
+        df[self.COLLEGE_ATTENDANCE_FIELD] = (
+            df[self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PUBLIC]
+            + df[self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PRIVATE]
+            + df[self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PUBLIC]
+            + df[self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PRIVATE]
+        ) / df[self.COLLEGE_ATTENDANCE_TOTAL_POPULATION_ASKED]
+
         # Save results to self.
         self.df = df
 
@@ -317,23 +362,7 @@ class CensusACSETL(ExtractTransformLoad):
         # mkdir census
         self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
 
-        columns_to_include = (
+        self.df[self.COLUMNS_TO_KEEP].to_csv(
-            [
-                self.GEOID_TRACT_FIELD_NAME,
-                self.UNEMPLOYED_FIELD_NAME,
-                self.LINGUISTIC_ISOLATION_FIELD_NAME,
-                self.MEDIAN_INCOME_FIELD_NAME,
-                self.POVERTY_LESS_THAN_100_PERCENT_FPL_FIELD_NAME,
-                self.POVERTY_LESS_THAN_150_PERCENT_FPL_FIELD_NAME,
-                self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME,
-                self.MEDIAN_HOUSE_VALUE_FIELD_NAME,
-                self.HIGH_SCHOOL_ED_FIELD,
-            ]
-            + self.RE_OUTPUT_FIELDS
-            + [self.PERCENT_PREFIX + field for field in self.RE_OUTPUT_FIELDS]
-        )
-
-        self.df[columns_to_include].to_csv(
             path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False
         )
 

data/data-pipeline/data_pipeline/etl/sources/child_opportunity_index/etl.py

@@ -0,0 +1,120 @@
+from pathlib import Path
+import pandas as pd
+
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.score import field_names
+from data_pipeline.utils import get_module_logger, unzip_file_from_url
+
+logger = get_module_logger(__name__)
+
+
+class ChildOpportunityIndex(ExtractTransformLoad):
+    """ETL Child Opportunity Index data.
+
+    COI compiles a number of useful data sets. In the future, we could pull these
+    data sets in directly from their original creators.
+
+    Data dictionary available when you download zip from `self.COI_FILE_URL`.
+
+    Data source overview: https://data.diversitydatakids.org/dataset/coi20-child-opportunity-index-2-0-database.
+
+    Full technical documents: https://www.diversitydatakids.org/sites/default/files/2020-02/ddk_coi2.0_technical_documentation_20200212.pdf.
+
+    Github repo: https://github.com/diversitydatakids/COI/
+
+    """
+
+    def __init__(self):
+        self.COI_FILE_URL = (
+            "https://data.diversitydatakids.org/datastore/zip/f16fff12-b1e5-4f60-85d3-"
+            "3a0ededa30a0?format=csv"
+        )
+
+        self.OUTPUT_PATH: Path = (
+            self.DATA_PATH / "dataset" / "child_opportunity_index"
+        )
+
+        self.TRACT_INPUT_COLUMN_NAME = "geoid"
+        self.EXTREME_HEAT_INPUT_FIELD = "HE_HEAT"
+        self.HEALTHY_FOOD_INPUT_FIELD = "HE_FOOD"
+        self.IMPENETRABLE_SURFACES_INPUT_FIELD = "HE_GREEN"
+        self.READING_INPUT_FIELD = "ED_READING"
+
+        # Constants for output
+        self.COLUMNS_TO_KEEP = [
+            self.GEOID_TRACT_FIELD_NAME,
+            field_names.EXTREME_HEAT_FIELD,
+            field_names.HEALTHY_FOOD_FIELD,
+            field_names.IMPENETRABLE_SURFACES_FIELD,
+            field_names.READING_FIELD,
+        ]
+
+        self.raw_df: pd.DataFrame
+        self.output_df: pd.DataFrame
+
+    def extract(self) -> None:
+        logger.info("Starting 51MB data download.")
+
+        unzip_file_from_url(
+            file_url=self.COI_FILE_URL,
+            download_path=self.TMP_PATH,
+            unzipped_file_path=self.TMP_PATH / "child_opportunity_index",
+        )
+
+        self.raw_df = pd.read_csv(
+            filepath_or_buffer=self.TMP_PATH
+            / "child_opportunity_index"
+            / "raw.csv",
+            # The following need to remain as strings for all of their digits, not get
+            # converted to numbers.
+            dtype={
+                self.TRACT_INPUT_COLUMN_NAME: "string",
+            },
+            low_memory=False,
+        )
+
+    def transform(self) -> None:
+        logger.info("Starting transforms.")
+
+        output_df = self.raw_df.rename(
+            columns={
+                self.TRACT_INPUT_COLUMN_NAME: self.GEOID_TRACT_FIELD_NAME,
+                self.EXTREME_HEAT_INPUT_FIELD: field_names.EXTREME_HEAT_FIELD,
+                self.HEALTHY_FOOD_INPUT_FIELD: field_names.HEALTHY_FOOD_FIELD,
+                self.IMPENETRABLE_SURFACES_INPUT_FIELD: field_names.IMPENETRABLE_SURFACES_FIELD,
+                self.READING_INPUT_FIELD: field_names.READING_FIELD,
+            }
+        )
+
+        # Sanity check the tract field.
+        if len(output_df[self.GEOID_TRACT_FIELD_NAME].str.len().unique()) != 1:
+            raise ValueError("Wrong tract length.")
+
+        # COI has two rows per tract: one for 2010 and one for 2015.
+        output_df = output_df[output_df["year"] == 2015]
+
+        # Convert percents from 0-100 to 0-1 to standardize with our other fields.
+        percent_fields_to_convert = [
+            field_names.HEALTHY_FOOD_FIELD,
+            field_names.IMPENETRABLE_SURFACES_FIELD,
+        ]
+
+        for percent_field_to_convert in percent_fields_to_convert:
+            output_df[percent_field_to_convert] = (
+                output_df[percent_field_to_convert] / 100
+            )
+
+        self.output_df = output_df
+
+    def validate(self) -> None:
+        logger.info("Validating data.")
+
+        pass
+
+    def load(self) -> None:
+        logger.info("Saving CSV")
+
+        self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
+        self.output_df[self.COLUMNS_TO_KEEP].to_csv(
+            path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False
+        )

data/data-pipeline/data_pipeline/etl/sources/ejscreen/etl.py

@@ -8,6 +8,12 @@ logger = get_module_logger(__name__)
 
 
 class EJSCREENETL(ExtractTransformLoad):
+    """Load EJSCREEN data.
+
+    Data dictionary:
+        https://gaftp.epa.gov/EJSCREEN/2019/2019_EJSCREEN_columns_explained.csv
+    """
+
     def __init__(self):
         self.EJSCREEN_FTP_URL = "https://edap-arcgiscloud-data-commons.s3.amazonaws.com/EJSCREEN2020/EJSCREEN_Tract_2020_USPR.csv.zip"
         self.EJSCREEN_CSV = self.TMP_PATH / "EJSCREEN_Tract_2020_USPR.csv"
@@ -19,7 +25,7 @@ class EJSCREENETL(ExtractTransformLoad):
             field_names.TOTAL_POP_FIELD,
             # pylint: disable=duplicate-code
             field_names.AIR_TOXICS_CANCER_RISK_FIELD,
-            field_names.RESPITORY_HAZARD_FIELD,
+            field_names.RESPIRATORY_HAZARD_FIELD,
             field_names.DIESEL_FIELD,
             field_names.PM25_FIELD,
             field_names.OZONE_FIELD,
@@ -61,7 +67,7 @@ class EJSCREENETL(ExtractTransformLoad):
                 # but I think that's the direction we'd like to move all ETL classes. - LMB
                 "ACSTOTPOP": field_names.TOTAL_POP_FIELD,
                 "CANCER": field_names.AIR_TOXICS_CANCER_RISK_FIELD,
-                "RESP": field_names.RESPITORY_HAZARD_FIELD,
+                "RESP": field_names.RESPIRATORY_HAZARD_FIELD,
                 "DSLPM": field_names.DIESEL_FIELD,
                 "PM25": field_names.PM25_FIELD,
                 "OZONE": field_names.OZONE_FIELD,

data/data-pipeline/data_pipeline/ipython/scoring_comparison.ipynb

@@ -479,7 +479,7 @@
     "comparison_fields = [\n",
     "    field_names.POVERTY_LESS_THAN_100_FPL_FIELD,\n",
     "    field_names.POVERTY_LESS_THAN_200_FPL_FIELD,\n",
-    "    field_names.MEDIAN_INCOME_PERCENT_AMI_FIELD,\n",
+    "    field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD,\n",
     "    field_names.LINGUISTIC_ISO_FIELD,\n",
     "    field_names.UNEMPLOYMENT_FIELD,\n",
     "    field_names.HIGH_SCHOOL_ED_FIELD,\n",

data/data-pipeline/data_pipeline/score/field_names.py

@@ -1,6 +1,12 @@
 # Suffixes
 PERCENTILE_FIELD_SUFFIX = " (percentile)"
 MIN_MAX_FIELD_SUFFIX = " (min-max normalized)"
+TOP_25_PERCENTILE_SUFFIX = " (top 25th percentile)"
+
+# Geographic field names
+GEOID_TRACT_FIELD = "GEOID10_TRACT"
+STATE_FIELD = "State Name"
+COUNTY_FIELD = "County Name"
 
 # Score file field names
 SCORE_A = "Score A"
@@ -21,6 +27,7 @@ SCORE_I = "Score I"
 SCORE_I_COMMUNITIES = "Score I (communities)"
 SCORE_K = "NMTC (communities)"
 SCORE_K_COMMUNITIES = "Score K (communities)"
+SCORE_L = "Definition L"
 SCORE_L_COMMUNITIES = "Definition L (communities)"
 L_CLIMATE = "Climate Factor (Definition L)"
 L_ENERGY = "Energy Factor (Definition L)"
@@ -45,7 +52,6 @@ POVERTY_LESS_THAN_150_FPL_FIELD = (
 POVERTY_LESS_THAN_100_FPL_FIELD = (
     "Percent of individuals < 100% Federal Poverty Line"
 )
-MEDIAN_INCOME_PERCENT_AMI_FIELD = "Median household income (% of AMI)"
 STATE_MEDIAN_INCOME_FIELD = (
     "Median household income (State; 2019 inflation-adjusted dollars)"
 )
@@ -57,6 +63,8 @@ MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD = "Median household income (% of AMI)"
 PERSISTENT_POVERTY_FIELD = "Persistent Poverty Census Tract"
 AMI_FIELD = "Area Median Income (State or metropolitan)"
 
+COLLEGE_ATTENDANCE_FIELD = "Percent enrollment in college or graduate school"
+
 # Climate
 FEMA_RISK_FIELD = "FEMA Risk Index Expected Annual Loss Score"
 EXPECTED_BUILDING_LOSS_RATE_FIELD = (
@@ -81,6 +89,7 @@ RMP_FIELD = "Proximity to Risk Management Plan (RMP) facilities"
 TSDF_FIELD = "Proximity to TSDF sites"
 NPL_FIELD = "Proximity to NPL sites"
 AIR_TOXICS_CANCER_RISK_FIELD = "Air toxics cancer risk"
+RESPIRATORY_HAZARD_FIELD = "Respiratory hazard index"
 
 # Housing
 HOUSING_BURDEN_FIELD = "Housing burden (percent)"
@@ -96,7 +105,6 @@ DIABETES_FIELD = "Diagnosed diabetes among adults aged >=18 years"
 ASTHMA_FIELD = "Current asthma among adults aged >=18 years"
 HEART_DISEASE_FIELD = "Coronary heart disease among adults aged >=18 years"
 LIFE_EXPECTANCY_FIELD = "Life expectancy (years)"
-RESPITORY_HAZARD_FIELD = "Respiratory hazard index"
 CANCER_FIELD = "Cancer (excluding skin cancer) among adults aged >=18 years"
 HEALTH_INSURANCE_FIELD = (
     "Current lack of health insurance among adults aged 18-64 years"
@@ -200,30 +208,65 @@ HOLC_GRADE_D_TRACT_50_PERCENT_FIELD: str = "Tract is >50% HOLC Grade D"
 HOLC_GRADE_D_TRACT_75_PERCENT_FIELD: str = "Tract is >75% HOLC Grade D"
 
 
+# Child Opportunity Index data
+# Summer days with maximum temperature above 90F.
+EXTREME_HEAT_FIELD = "Summer days above 90F"
+
+# Percentage households without a car located further than a half-mile from the
+# nearest supermarket.
+HEALTHY_FOOD_FIELD = "Percent low access to healthy food"
+
+# Percentage impenetrable surface areas such as rooftops, roads or parking lots.
+IMPENETRABLE_SURFACES_FIELD = "Percent impenetrable surface areas"
+
+# Percentage third graders scoring proficient on standardized reading tests,
+# converted to NAEP scale score points.
+READING_FIELD = "Third grade reading proficiency"
+LOW_READING_FIELD = "Low third grade reading proficiency"
+
+# Names for individual factors being exceeded
 # Climate Change
 EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for expected population loss rate and is low income"
 EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for expected agriculture loss rate and is low income"
 EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for expected building loss rate and is low income"
+EXTREME_HEAT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = (
+    f"At or above the {PERCENTILE}th percentile for summer days above 90F and "
+    f"the median house value is less than {MEDIAN_HOUSE_VALUE_PERCENTILE}th "
+    f"percentile and is low income"
+)
 
 # Clean energy and efficiency
 PM25_EXPOSURE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for PM2.5 exposure and is low income"
 ENERGY_BURDEN_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for energy burden and is low income"
+
 # Clean transportation
 DIESEL_PARTICULATE_MATTER_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for diesel particulate matter and is low income"
 TRAFFIC_PROXIMITY_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for traffic proximity and is low income"
+
 # Affordable and Sustainable Housing
-LEAD_PAINT_MEDIAN_HOME_VALUE_LOW_INCOME_FIELD = (
+LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = (
     f"At or above the {PERCENTILE}th percentile for lead paint and"
-    " the median house value is less than {MEDIAN_HOUSE_VALUE_PERCENTILE}th percentile and is low income"
+    f" the median house value is less than {MEDIAN_HOUSE_VALUE_PERCENTILE}th "
+    f"percentile and is low income"
 )
 HOUSING_BURDEN_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for housing burden and is low income"
+
+IMPENETRABLE_SURFACES_LOW_INCOME_FIELD = (
+    f"At or above the {PERCENTILE}th percentile for impenetrable surfaces and is low "
+    f"income"
+)
+
 # Remediation and Reduction of Legacy Pollution
 RMP_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for proximity to RMP sites and is low income"
 SUPERFUND_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for proximity to superfund sites and is low income"
 HAZARDOUS_WASTE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for proximity to hazardous waste facilities and is low income"
+AIR_TOXICS_CANCER_RISK_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for air toxics cancer risk and is low income"
+RESPIRATORY_HAZARD_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for respiratory hazard index and is low income"
+
 # Critical Clean Water and Waste Infrastructure
 WASTEWATER_DISCHARGE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for wastewater discharge and is low income"
-# Health Burden
+
+# Health Burdens
 DIABETES_LOW_INCOME_FIELD = (
     f"At or above the {PERCENTILE}th percentile for diabetes and is low income"
 )
@@ -234,25 +277,35 @@ HEART_DISEASE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for
 
 LIFE_EXPECTANCY_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for life expectancy and is low income"
 
+HEALTHY_FOOD_LOW_INCOME_FIELD = (
+    f"At or above the {PERCENTILE}th percentile for low "
+    f"access to healthy food and is low income"
+)
+
 # Workforce
 UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD = (
     f"At or above the {PERCENTILE}th percentile for unemployment"
-    " and low HS education"
+    " and has low HS education"
 )
 
 LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD = (
     f"At or above the {PERCENTILE}th percentile for households in linguistic isolation"
-    " and low HS education"
+    " and has low HS education"
 )
 
 POVERTY_LOW_HS_EDUCATION_FIELD = (
     f"At or above the {PERCENTILE}th percentile for households at or below 100% federal poverty level"
-    " and low HS education"
+    " and has low HS education"
+)
+
+LOW_READING_LOW_HS_EDUCATION_FIELD = (
+    f"At or above the {PERCENTILE}th percentile for low 3rd grade reading proficiency"
+    " and has low HS education"
 )
 
 MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD = (
     f"At or below the {PERCENTILE}th percentile for median income"
-    "  and low HS education"
+    "  and has low HS education"
 )
 
 THRESHOLD_COUNT = "Total threshold criteria exceeded"

data/data-pipeline/data_pipeline/score/score_c.py

@@ -54,7 +54,7 @@ class ScoreC(Score):
             [
                 field_names.AIR_TOXICS_CANCER_RISK_FIELD
                 + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.RESPITORY_HAZARD_FIELD
+                field_names.RESPIRATORY_HAZARD_FIELD
                 + field_names.PERCENTILE_FIELD_SUFFIX,
                 field_names.DIESEL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
                 field_names.PM25_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,

data/data-pipeline/data_pipeline/score/score_f.py

@@ -36,7 +36,7 @@ class ScoreF(Score):
             )
             | (
                 self.df[
-                    field_names.RESPITORY_HAZARD_FIELD
+                    field_names.RESPIRATORY_HAZARD_FIELD
                     + field_names.PERCENTILE_FIELD_SUFFIX
                 ]
                 > 0.9

data/data-pipeline/data_pipeline/score/score_l.py

@@ -177,6 +177,8 @@ class ScoreL(Score):
             field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_FIELD,
             field_names.EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_FIELD,
             field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_FIELD,
+            field_names.EXTREME_HEAT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD,
+            field_names.IMPENETRABLE_SURFACES_LOW_INCOME_FIELD,
         ]
 
         expected_population_loss_threshold = (
@@ -203,6 +205,28 @@ class ScoreL(Score):
             >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
         )
 
+        extreme_heat_median_home_value_threshold = (
+            self.df[
+                field_names.EXTREME_HEAT_FIELD
+                + field_names.PERCENTILE_FIELD_SUFFIX
+            ]
+            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
+        ) & (
+            self.df[
+                field_names.MEDIAN_HOUSE_VALUE_FIELD
+                + field_names.PERCENTILE_FIELD_SUFFIX
+            ]
+            <= self.MEDIAN_HOUSE_VALUE_THRESHOLD
+        )
+
+        impenetrable_surfaces_threshold = (
+            self.df[
+                field_names.IMPENETRABLE_SURFACES_FIELD
+                + field_names.PERCENTILE_FIELD_SUFFIX
+            ]
+            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
+        )
+
         self.df[field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_FIELD] = (
             expected_population_loss_threshold
             & self.df[field_names.FPL_200_SERIES]
@@ -218,6 +242,18 @@ class ScoreL(Score):
             & self.df[field_names.FPL_200_SERIES]
         )
 
+        self.df[
+            field_names.EXTREME_HEAT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD
+        ] = (
+            extreme_heat_median_home_value_threshold
+            & self.df[field_names.FPL_200_SERIES]
+        )
+
+        self.df[field_names.IMPENETRABLE_SURFACES_LOW_INCOME_FIELD] = (
+            impenetrable_surfaces_threshold
+            & self.df[field_names.FPL_200_SERIES]
+        )
+
         self._increment_total_eligibility_exceeded(climate_eligibility_columns)
 
         return self.df[climate_eligibility_columns].any(axis="columns")
@@ -320,11 +356,11 @@ class ScoreL(Score):
         # poverty level. Source: Census's American Community Survey]
 
         housing_eligibility_columns = [
-            field_names.LEAD_PAINT_MEDIAN_HOME_VALUE_LOW_INCOME_FIELD,
+            field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD,
             field_names.HOUSING_BURDEN_LOW_INCOME_FIELD,
         ]
 
-        lead_paint_median_house_hold_threshold = (
+        lead_paint_median_home_value_threshold = (
             self.df[
                 field_names.LEAD_PAINT_FIELD
                 + field_names.PERCENTILE_FIELD_SUFFIX
@@ -347,8 +383,8 @@ class ScoreL(Score):
         )
 
         # series by series indicators
-        self.df[field_names.LEAD_PAINT_MEDIAN_HOME_VALUE_LOW_INCOME_FIELD] = (
+        self.df[field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD] = (
-            lead_paint_median_house_hold_threshold
+            lead_paint_median_home_value_threshold
             & self.df[field_names.FPL_200_SERIES]
         )
 
@@ -371,6 +407,8 @@ class ScoreL(Score):
             field_names.RMP_LOW_INCOME_FIELD,
             field_names.SUPERFUND_LOW_INCOME_FIELD,
             field_names.HAZARDOUS_WASTE_LOW_INCOME_FIELD,
+            field_names.AIR_TOXICS_CANCER_RISK_LOW_INCOME_FIELD,
+            field_names.RESPIRATORY_HAZARD_LOW_INCOME_FIELD,
         ]
 
         rmp_sites_threshold = (
@@ -390,6 +428,22 @@ class ScoreL(Score):
             >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
         )
 
+        air_toxics_cancer_risk_threshold = (
+            self.df[
+                field_names.AIR_TOXICS_CANCER_RISK_FIELD
+                + field_names.PERCENTILE_FIELD_SUFFIX
+            ]
+            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
+        )
+
+        respiratory_hazard_risk_threshold = (
+            self.df[
+                field_names.RESPIRATORY_HAZARD_FIELD
+                + field_names.PERCENTILE_FIELD_SUFFIX
+            ]
+            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
+        )
+
         # individual series-by-series
         self.df[field_names.RMP_LOW_INCOME_FIELD] = (
             rmp_sites_threshold & self.df[field_names.FPL_200_SERIES]
@@ -400,6 +454,14 @@ class ScoreL(Score):
         self.df[field_names.HAZARDOUS_WASTE_LOW_INCOME_FIELD] = (
             tsdf_sites_threshold & self.df[field_names.FPL_200_SERIES]
         )
+        self.df[field_names.AIR_TOXICS_CANCER_RISK_LOW_INCOME_FIELD] = (
+            air_toxics_cancer_risk_threshold
+            & self.df[field_names.FPL_200_SERIES]
+        )
+        self.df[field_names.RESPIRATORY_HAZARD_LOW_INCOME_FIELD] = (
+            respiratory_hazard_risk_threshold
+            & self.df[field_names.FPL_200_SERIES]
+        )
 
         self._increment_total_eligibility_exceeded(
             pollution_eligibility_columns
@@ -449,6 +511,7 @@ class ScoreL(Score):
             field_names.DIABETES_LOW_INCOME_FIELD,
             field_names.ASTHMA_LOW_INCOME_FIELD,
             field_names.HEART_DISEASE_LOW_INCOME_FIELD,
+            field_names.HEALTHY_FOOD_LOW_INCOME_FIELD,
             field_names.LIFE_EXPECTANCY_LOW_INCOME_FIELD,
         ]
 
@@ -474,6 +537,14 @@ class ScoreL(Score):
             >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
         )
 
+        healthy_food_threshold = (
+            self.df[
+                field_names.HEALTHY_FOOD_FIELD
+                + field_names.PERCENTILE_FIELD_SUFFIX
+            ]
+            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
+        )
+
         life_expectancy_threshold = (
             self.df[
                 field_names.LIFE_EXPECTANCY_FIELD
@@ -496,6 +567,9 @@ class ScoreL(Score):
         self.df[field_names.LIFE_EXPECTANCY_LOW_INCOME_FIELD] = (
             life_expectancy_threshold & self.df[field_names.FPL_200_SERIES]
         )
+        self.df[field_names.HEALTHY_FOOD_LOW_INCOME_FIELD] = (
+            healthy_food_threshold & self.df[field_names.FPL_200_SERIES]
+        )
 
         self._increment_total_eligibility_exceeded(health_eligibility_columns)
 
@@ -513,6 +587,15 @@ class ScoreL(Score):
         # Where the high school degree achievement rates for adults 25 years and older is less than 95%
         # (necessary to screen out university block groups)
 
+        # Workforce criteria for states fields.
+        workforce_eligibility_columns = [
+            field_names.UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD,
+            field_names.POVERTY_LOW_HS_EDUCATION_FIELD,
+            field_names.LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD,
+            field_names.MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD,
+            field_names.LOW_READING_LOW_HS_EDUCATION_FIELD,
+        ]
+
         high_scool_achievement_rate_threshold = (
             self.df[field_names.HIGH_SCHOOL_ED_FIELD]
             >= self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD
@@ -528,7 +611,7 @@ class ScoreL(Score):
 
         median_income_threshold = (
             self.df[
-                field_names.MEDIAN_INCOME_PERCENT_AMI_FIELD
+                field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD
                 + field_names.PERCENTILE_FIELD_SUFFIX
             ]
             # Note: a high median income as a % of AMI is good, so take 1 minus the threshold to invert it.
@@ -552,6 +635,14 @@ class ScoreL(Score):
             >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
         )
 
+        low_reading_threshold = (
+            self.df[
+                field_names.LOW_READING_FIELD
+                + field_names.PERCENTILE_FIELD_SUFFIX
+            ]
+            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
+        )
+
         self.df[field_names.LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD] = (
             linguistic_isolation_threshold
             & high_scool_achievement_rate_threshold
@@ -569,15 +660,9 @@ class ScoreL(Score):
             unemployment_threshold & high_scool_achievement_rate_threshold
         )
 
-        # Workforce criteria for states fields that create indicator columns
+        self.df[field_names.LOW_READING_LOW_HS_EDUCATION_FIELD] = (
-        # for each tract in order to indicate whether they met any of the four
+            low_reading_threshold & high_scool_achievement_rate_threshold
-        # criteria. We will used this create individual indicator columns.
+        )
-        workforce_eligibility_columns = [
-            field_names.UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD,
-            field_names.POVERTY_LOW_HS_EDUCATION_FIELD,
-            field_names.LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD,
-            field_names.MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD,
-        ]
 
         workforce_combined_criteria_for_states = self.df[
             workforce_eligibility_columns