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Merge branch 'main' into esfoobar-usds/1062-implement-changes-export-files

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Jorge Escobar 2022-01-06 12:18:51 -05:00
commit d01bbc7dfa
32 changed files with 1463 additions and 562 deletions
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2
INSTALLATION-es.md
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@ -25,7 +25,7 @@ Descargar desde [sitio web](https://git-scm.com/download/win)
2. Valide la instalación escribiendo `brew -v` en la terminal y asegúrese de que se muestre un número de versión.
### Instalar Node usando NVM
<!-- markdown-link-check-disable-next-line -->
Esto funcionará tanto para MacOS como para Win10. Siga las instrucciones de este [enlace](https://medium.com/@nodesource/installing-node-js-tutorial-using-nvm-5c6ff5925dd8)
### Configuración de IDE

2
client/README.md
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@ -19,6 +19,7 @@ This README contains the following content:
#### Install Node using NVM
<!-- markdown-link-check-disable-next-line -->
This will work for both MacOS and Win10. Follow instructions on this [link](https://medium.com/@nodesource/installing-node-js-tutorial-using-nvm-5c6ff5925dd8). Be sure to read through the whole doc to find the sections within each step relevant to you (e.g. if you're using Homebrew, when you get to Step 2 look for the section, "Install NVM with Homebrew").
If you install NVM using Homebrew, make sure to read the output in terminal after you run `brew install nvm`. You will need to add a few lines to your ~/.bash_profile and perhaps complete a couple other tasks.
@ -70,6 +71,7 @@ DATA_SOURCE env variable in the docker-compose.yml. See [environment variables](
#### Troubleshooting docker
<!-- markdown-link-check-disable-next-line -->
- If an error is thrown about [running out of space](https://medium.com/@wlarch/no-space-left-on-device-when-using-docker-compose-why-c4a2c783c6f6) on device see this for ways to reclaim space.

18
client/src/components/AreaDetail/areaDetail.module.scss
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@ -82,3 +82,21 @@ $sidePanelLabelFontColor: #171716;
font-size: medium;
}
}
// The following class is used in the AccordionItems in the AreaDetail component.
// The Accordion component (parent of AccordionItems) requires some CSS overrides.
// Local styling is not allowing the override.
// The override is needed to push into the bounds of the Accordion component's styles.
// To override this, in globals.scss, we set the this .categoryHeader's vertical alignment by
// setting styles in:
// .usa-accordion__content > *:first-child
// This first child of the accordion content is the category header:
.categoryHeader {
display: flex;
justify-content: space-between;
text-transform: uppercase;
font-size: small;
@include u-bg('gray-cool-5');
@include u-padding-left(2.5);
@include u-padding-right(2);
}

1
client/src/components/AreaDetail/areaDetail.module.scss.d.ts vendored
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@ -11,6 +11,7 @@ declare namespace MapModuleScssNamespace {
versionInfo: string;
showThresholdExceed:string;
hideThresholdExceed:string;
categoryHeader:string;
}
}

125
client/src/components/AreaDetail/index.tsx
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@ -18,11 +18,20 @@ interface IAreaDetailProps {
properties: constants.J40Properties,
}
/**
* This interface is used as define the various fields for each indicator in the side panel
* label: the indicator label or title
* description: the description of the indicator used in the side panel
* value: the number from the geoJSON tile
* isDisadvagtaged: the flag from the geoJSON tile
* isPercent: is the value a percent or percentile
* */
export interface indicatorInfo {
label: string,
description: string,
value: number,
isDisadvagtaged: boolean,
isPercent?: boolean,
}
const AreaDetail = ({properties}:IAreaDetailProps) => {
@ -45,84 +54,84 @@ const AreaDetail = ({properties}:IAreaDetailProps) => {
label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.EXP_AG_LOSS),
description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.EXP_AG_LOSS),
value: properties[constants.EXP_AGRICULTURE_LOSS_PERCENTILE] ?
properties[constants.EXP_AGRICULTURE_LOSS_PERCENTILE] : null,
properties[constants.EXP_AGRICULTURE_LOSS_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_EXP_AGR_LOSS_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_EXP_AGR_LOSS_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_EXP_AGR_LOSS_AND_IS_LOW_INCOME] : null,
};
const expBldLoss:indicatorInfo = {
label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.EXP_BLD_LOSS),
description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.EXP_BLD_LOSS),
value: properties[constants.EXP_BUILDING_LOSS_PERCENTILE] ?
properties[constants.EXP_BUILDING_LOSS_PERCENTILE] : null,
properties[constants.EXP_BUILDING_LOSS_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_EXP_BLD_LOSS_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_EXP_BLD_LOSS_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_EXP_BLD_LOSS_AND_IS_LOW_INCOME] : null,
};
const expPopLoss:indicatorInfo = {
label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.EXP_POP_LOSS),
description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.EXP_POP_LOSS),
value: properties[constants.EXP_POPULATION_LOSS_PERCENTILE] ?
properties[constants.EXP_POPULATION_LOSS_PERCENTILE] : null,
properties[constants.EXP_POPULATION_LOSS_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_EXP_POP_LOSS_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_EXP_POP_LOSS_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_EXP_POP_LOSS_AND_IS_LOW_INCOME] : null,
};
const lowInc:indicatorInfo = {
label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.LOW_INCOME),
description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.LOW_INCOME),
value: properties[constants.POVERTY_BELOW_200_PERCENTILE] ?
properties[constants.POVERTY_BELOW_200_PERCENTILE] : null,
properties[constants.POVERTY_BELOW_200_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_FEDERAL_POVERTY_LEVEL_200] ?
properties[constants.IS_FEDERAL_POVERTY_LEVEL_200] : null,
properties[constants.IS_FEDERAL_POVERTY_LEVEL_200] : 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] ?
properties[constants.ENERGY_PERCENTILE] : null,
properties[constants.ENERGY_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_ENERGY_BURDEN_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_ENERGY_BURDEN_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_ENERGY_BURDEN_AND_IS_LOW_INCOME] : 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] ?
properties[constants.PM25_PERCENTILE] : null,
properties[constants.PM25_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_PM25_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_PM25_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_PM25_AND_IS_LOW_INCOME] : 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] ?
properties[constants.DIESEL_MATTER_PERCENTILE] : null,
properties[constants.DIESEL_MATTER_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_DIESEL_PM_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_DIESEL_PM_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_DIESEL_PM_AND_IS_LOW_INCOME] : 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] ?
properties[constants.TRAFFIC_PERCENTILE] : null,
properties[constants.TRAFFIC_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_TRAFFIC_PROX_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_TRAFFIC_PROX_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_TRAFFIC_PROX_AND_IS_LOW_INCOME] : 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] ?
properties[constants.HOUSING_BURDEN_PROPERTY_PERCENTILE] : null,
properties[constants.HOUSING_BURDEN_PROPERTY_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_HOUSE_BURDEN_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_HOUSE_BURDEN_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_HOUSE_BURDEN_AND_IS_LOW_INCOME] : 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] ?
properties[constants.LEAD_PAINT_PERCENTILE] : null,
properties[constants.LEAD_PAINT_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_LEAD_PAINT_AND_MEDIAN_HOME_VAL_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_LEAD_PAINT_AND_MEDIAN_HOME_VAL_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_LEAD_PAINT_AND_MEDIAN_HOME_VAL_AND_IS_LOW_INCOME] : null,
};
// const medHomeVal:indicatorInfo = {
// label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.MED_HOME_VAL),
@ -136,108 +145,109 @@ const AreaDetail = ({properties}:IAreaDetailProps) => {
label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.PROX_HAZ),
description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.PROX_HAZ),
value: properties[constants.PROXIMITY_TSDF_SITES_PERCENTILE] ?
properties[constants.PROXIMITY_TSDF_SITES_PERCENTILE] : null,
properties[constants.PROXIMITY_TSDF_SITES_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_HAZARD_WASTE_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_HAZARD_WASTE_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_HAZARD_WASTE_AND_IS_LOW_INCOME] : null,
};
const proxNPL:indicatorInfo = {
label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.PROX_NPL),
description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.PROX_NPL),
value: properties[constants.PROXIMITY_NPL_SITES_PERCENTILE] ?
properties[constants.PROXIMITY_NPL_SITES_PERCENTILE] : null,
properties[constants.PROXIMITY_NPL_SITES_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_SUPERFUND_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_SUPERFUND_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_SUPERFUND_AND_IS_LOW_INCOME] : null,
};
const proxRMP:indicatorInfo = {
label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.PROX_RMP),
description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.PROX_RMP),
value: properties[constants.PROXIMITY_RMP_SITES_PERCENTILE] ?
properties[constants.PROXIMITY_RMP_SITES_PERCENTILE] : null,
properties[constants.PROXIMITY_RMP_SITES_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_RMP_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_RMP_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_RMP_AND_IS_LOW_INCOME] : 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] ?
properties[constants.WASTEWATER_PERCENTILE] : null,
properties[constants.WASTEWATER_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_WASTEWATER_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_WASTEWATER_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_WASTEWATER_AND_IS_LOW_INCOME] : 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] ?
properties[constants.ASTHMA_PERCENTILE] : null,
properties[constants.ASTHMA_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_ASTHMA_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_ASTHMA_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_ASTHMA_AND_IS_LOW_INCOME] : 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] ?
properties[constants.DIABETES_PERCENTILE] : null,
properties[constants.DIABETES_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_DIABETES_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_DIABETES_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_DIABETES_AND_IS_LOW_INCOME] : 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] ?
properties[constants.HEART_PERCENTILE] : null,
properties[constants.HEART_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_HEART_DISEASE_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_HEART_DISEASE_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_HEART_DISEASE_AND_IS_LOW_INCOME] : null,
};
const lifeExpect:indicatorInfo = {
label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.LIFE_EXPECT),
description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.LOW_LIFE_EXPECT),
value: properties[constants.LIFE_PERCENTILE] ?
properties[constants.LIFE_PERCENTILE] : null,
properties[constants.LIFE_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_LOW_LIFE_EXP_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_LOW_LIFE_EXP_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_LOW_LIFE_EXP_AND_IS_LOW_INCOME] : null,
};
const lowMedInc:indicatorInfo = {
label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.LOW_MED_INC),
description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.LOW_MED_INCOME),
value: properties[constants.LOW_MEDIAN_INCOME_PERCENTILE] ?
properties[constants.LOW_MEDIAN_INCOME_PERCENTILE] : null,
properties[constants.LOW_MEDIAN_INCOME_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_LOW_MEDIAN_INCOME_AND_LOW_HIGH_SCHOOL_EDU] ?
properties[constants.IS_GTE_90_LOW_MEDIAN_INCOME_AND_LOW_HIGH_SCHOOL_EDU] : null,
properties[constants.IS_GTE_90_LOW_MEDIAN_INCOME_AND_LOW_HIGH_SCHOOL_EDU] : null,
};
const lingIso:indicatorInfo = {
label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.LING_ISO),
description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.LING_ISO),
value: properties[constants.LINGUISTIC_ISOLATION_PROPERTY_PERCENTILE] ?
properties[constants.LINGUISTIC_ISOLATION_PROPERTY_PERCENTILE] : null,
properties[constants.LINGUISTIC_ISOLATION_PROPERTY_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_LINGUISITIC_ISO_AND_IS_LOW_INCOME] ?
properties[constants.IS_GTE_90_LINGUISITIC_ISO_AND_IS_LOW_INCOME] : null,
properties[constants.IS_GTE_90_LINGUISITIC_ISO_AND_IS_LOW_INCOME] : null,
};
const unemploy:indicatorInfo = {
label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.UNEMPLOY),
description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.UNEMPLOY),
value: properties[constants.UNEMPLOYMENT_PROPERTY_PERCENTILE] ?
properties[constants.UNEMPLOYMENT_PROPERTY_PERCENTILE] : null,
properties[constants.UNEMPLOYMENT_PROPERTY_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_UNEMPLOYMENT_AND_LOW_HIGH_SCHOOL_EDU] ?
properties[constants.IS_GTE_90_UNEMPLOYMENT_AND_LOW_HIGH_SCHOOL_EDU] : null,
properties[constants.IS_GTE_90_UNEMPLOYMENT_AND_LOW_HIGH_SCHOOL_EDU] : 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] ?
properties[constants.POVERTY_PROPERTY_PERCENTILE] : null,
properties[constants.POVERTY_PROPERTY_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_BELOW_100_POVERTY_AND_LOW_HIGH_SCHOOL_EDU] ?
properties[constants.IS_GTE_90_BELOW_100_POVERTY_AND_LOW_HIGH_SCHOOL_EDU] : null,
properties[constants.IS_GTE_90_BELOW_100_POVERTY_AND_LOW_HIGH_SCHOOL_EDU] : null,
};
const highSchool:indicatorInfo = {
label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.HIGH_SCL),
description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.HIGH_SKL),
value: properties[constants.HIGH_SCHOOL_PROPERTY_PERCENTILE] ?
properties[constants.HIGH_SCHOOL_PROPERTY_PERCENTILE] : null,
properties[constants.HIGH_SCHOOL_PROPERTY_PERCENTILE] : null,
isDisadvagtaged: properties[constants.IS_GTE_90_UNEMPLOYMENT_AND_LOW_HIGH_SCHOOL_EDU] ?
properties[constants.IS_GTE_90_UNEMPLOYMENT_AND_LOW_HIGH_SCHOOL_EDU] : null,
properties[constants.IS_GTE_90_UNEMPLOYMENT_AND_LOW_HIGH_SCHOOL_EDU] : null,
isPercent: true,
};
// Aggregate indicators based on categories
@ -247,56 +257,56 @@ const AreaDetail = ({properties}:IAreaDetailProps) => {
titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.CLIMATE),
indicators: [expAgLoss, expBldLoss, expPopLoss, lowInc],
isDisadvagtaged: properties[constants.IS_CLIMATE_FACTOR_DISADVANTAGED_L] ?
properties[constants.IS_CLIMATE_FACTOR_DISADVANTAGED_L] : null,
properties[constants.IS_CLIMATE_FACTOR_DISADVANTAGED_L] : null,
},
{
id: 'clean-energy',
titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.CLEAN_ENERGY),
indicators: [energyBurden, pm25, lowInc],
isDisadvagtaged: properties[constants.IS_ENERGY_FACTOR_DISADVANTAGED_L] ?
properties[constants.IS_ENERGY_FACTOR_DISADVANTAGED_L] : null,
properties[constants.IS_ENERGY_FACTOR_DISADVANTAGED_L] : null,
},
{
id: 'clean-transport',
titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.CLEAN_TRANSPORT),
indicators: [dieselPartMatter, trafficVolume, lowInc],
isDisadvagtaged: properties[constants.IS_TRANSPORT_FACTOR_DISADVANTAGED_L] ?
properties[constants.IS_TRANSPORT_FACTOR_DISADVANTAGED_L] : null,
properties[constants.IS_TRANSPORT_FACTOR_DISADVANTAGED_L] : null,
},
{
id: 'sustain-house',
titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.SUSTAIN_HOUSE),
indicators: [houseBurden, leadPaint, lowInc],
isDisadvagtaged: properties[constants.IS_HOUSING_FACTOR_DISADVANTAGED_L] ?
properties[constants.IS_HOUSING_FACTOR_DISADVANTAGED_L] : null,
properties[constants.IS_HOUSING_FACTOR_DISADVANTAGED_L] : null,
},
{
id: 'leg-pollute',
titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.LEG_POLLUTE),
indicators: [proxHaz, proxNPL, proxRMP, lowInc],
isDisadvagtaged: properties[constants.IS_POLLUTION_FACTOR_DISADVANTAGED_L] ?
properties[constants.IS_POLLUTION_FACTOR_DISADVANTAGED_L] : null,
properties[constants.IS_POLLUTION_FACTOR_DISADVANTAGED_L] : null,
},
{
id: 'clean-water',
titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.CLEAN_WATER),
indicators: [wasteWater, lowInc],
isDisadvagtaged: properties[constants.IS_WATER_FACTOR_DISADVANTAGED_L] ?
properties[constants.IS_WATER_FACTOR_DISADVANTAGED_L] : null,
properties[constants.IS_WATER_FACTOR_DISADVANTAGED_L] : null,
},
{
id: 'health-burdens',
titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.HEALTH_BURDEN),
indicators: [asthma, diabetes, heartDisease, lifeExpect, lowInc],
isDisadvagtaged: properties[constants.IS_HEALTH_FACTOR_DISADVANTAGED_L] ?
properties[constants.IS_HEALTH_FACTOR_DISADVANTAGED_L] : null,
properties[constants.IS_HEALTH_FACTOR_DISADVANTAGED_L] : null,
},
{
id: 'work-dev',
titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.WORK_DEV),
indicators: [lowMedInc, lingIso, unemploy, poverty, highSchool],
isDisadvagtaged: properties[constants.IS_WORKFORCE_FACTOR_DISADVANTAGED_L] ?
properties[constants.IS_WORKFORCE_FACTOR_DISADVANTAGED_L] : null,
properties[constants.IS_WORKFORCE_FACTOR_DISADVANTAGED_L] : null,
},
];
@ -308,6 +318,13 @@ const AreaDetail = ({properties}:IAreaDetailProps) => {
title: <Category name={category.titleText} isDisadvantaged={category.isDisadvagtaged}/>,
content: (
<>
{/* Category Header */}
<div className={styles.categoryHeader}>
<div>{intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.INDICATOR)}</div>
<div>{intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.PERCENTILE)}</div>
</div>
{/* Category Indicators */}
{category.indicators.map((indicator:any, index:number) => {
return <Indicator key={`ind${index}`} indicator={indicator}/>;
})}

73
client/src/components/AreaDetail/tests/__snapshots__/areaDetail.test.tsx.snap
View file

@ -87,6 +87,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
hidden=""
id="climate-change"
>
<div>
<div>
Indicator
</div>
<div>
Percentile (0-100)
</div>
</div>
<li
data-cy="indicatorBox"
>
@ -190,6 +198,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
hidden=""
id="clean-energy"
>
<div>
<div>
Indicator
</div>
<div>
Percentile (0-100)
</div>
</div>
<li
data-cy="indicatorBox"
>
@ -273,6 +289,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
hidden=""
id="clean-transport"
>
<div>
<div>
Indicator
</div>
<div>
Percentile (0-100)
</div>
</div>
<li
data-cy="indicatorBox"
>
@ -356,6 +380,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
hidden=""
id="sustain-house"
>
<div>
<div>
Indicator
</div>
<div>
Percentile (0-100)
</div>
</div>
<li
data-cy="indicatorBox"
>
@ -441,6 +473,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
hidden=""
id="leg-pollute"
>
<div>
<div>
Indicator
</div>
<div>
Percentile (0-100)
</div>
</div>
<li
data-cy="indicatorBox"
>
@ -542,6 +582,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
hidden=""
id="clean-water"
>
<div>
<div>
Indicator
</div>
<div>
Percentile (0-100)
</div>
</div>
<li
data-cy="indicatorBox"
>
@ -607,6 +655,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
hidden=""
id="health-burdens"
>
<div>
<div>
Indicator
</div>
<div>
Percentile (0-100)
</div>
</div>
<li
data-cy="indicatorBox"
>
@ -731,6 +787,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
hidden=""
id="work-dev"
>
<div>
<div>
Indicator
</div>
<div>
Percentile (0-100)
</div>
</div>
<li
data-cy="indicatorBox"
>
@ -822,15 +886,16 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1
High school degree achievement rate
<div>
Percent of people ages 25 years or older whose education level is less than a high school diploma
Percent (not a percentile) of people ages 25 years or older whose education level is less than a
high school diploma
</div>
</div>
<div>
N/A
<sup>
<span />
</sup>
<span>
%
</span>
</div>
</div>
</li>

4
client/src/components/BetaBanner/__snapshots__/BetaBanner.test.tsx.snap
View file

@ -10,8 +10,8 @@ exports[`rendering of the BetaBanner checks if component renders 1`] = `
This is a beta site.
</span>
<span>
It is an early, in-progress version of the tool with limited datasets
that will be continuously updated.
It is an early, in-progress version of the tool with limited datasets that will
be regularly updated.
</span>
</div>
</div>

2
client/src/components/Categories/__snapshots__/Categories.test.tsx.snap
View file

@ -176,7 +176,7 @@ exports[`rendering of the Categories checks if component renders 1`] = `
low median home value
</a>
is at or less than
90th percentile OR at or above the 10th percentile for the
90th percentile OR at or above the 90th percentile for the
<a
href="#house-burden"
>

4
client/src/components/DatasetCard/index.tsx
View file

@ -25,9 +25,7 @@ const DatasetCard = ({datasetCardProps}:IDatasetCardProps) => {
<span className={styles.datasetCardLabels}>
{intl.formatMessage(METHODOLOGY_COPY.DATASET_CARD_LABELS.RESP_PARTY)}
</span>
<a href={datasetCardProps.dataSourceURL} target={'_blank'} rel="noreferrer">
{datasetCardProps.respPartyLabel}
</a>
{datasetCardProps.responsibleParty}
</li>
<li className={styles.datasetCardListItem}>
<span className={styles.datasetCardLabels}>

8
client/src/components/DatasetCard/tests/__snapshots__/datasetCard.test.tsx.snap
View file

@ -10,9 +10,9 @@ exports[`rendering of indicator dataset card checks if component renders 1`] = `
</h3>
<div>
Percent of a block group's population in households where household income is at or below
200% of the federal poverty level.
Percent of a census tract's population in households where household income is at or below
200% of the federal poverty level.
</div>
<ul>
<li>
@ -24,7 +24,7 @@ exports[`rendering of indicator dataset card checks if component renders 1`] = `
rel="noreferrer"
target="_blank"
>
Census's American Community Survey.
Census's American Community Survey
</a>
</li>
<li>

253
client/src/components/DatasetContainer/tests/__snapshots__/datasetContainer.test.tsx.snap
View file

@ -40,9 +40,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Percent of a block group's population in households where household income is at or below
200% of the federal poverty level.
Percent of a census tract's population in households where household income is at or below
200% of the federal poverty level.
</div>
<ul>
<li>
@ -54,7 +54,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Census's American Community Survey.
Census's American Community Survey
</a>
</li>
<li>
@ -79,12 +79,12 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Percent of agriculture value at risk from losses due to natural hazards. Calculated by dividing
the agriculture value at risk in a census tract by the total agriculture value in that census
tract. Fourteen natural hazards that have some link to climate change include: avalanche,
coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide,
riverine flooding, strong wind, tornado, wildfire, and winter weather.
Percent of agriculture value at risk from losses due to natural hazards. Calculated by dividing
the agriculture value at risk in a census tract by the total agriculture value in that census
tract. Fourteen natural hazards that have some link to climate change include: avalanche,
coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide,
riverine flooding, strong wind, tornado, wildfire, and winter weather.
</div>
<ul>
<li>
@ -121,12 +121,12 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Percent of building value at risk from losses due to natural hazards. Calculated by dividing the
building value at risk in a census tract by the total building value in that census tract.
Fourteen natural hazards that have some link to climate change include: avalanche, coastal flooding,
cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine flooding, strong
wind, tornado, wildfire, and winter weather.
Percent of building value at risk from losses due to natural hazards. Calculated by dividing the
building value at risk in a census tract by the total building value in that census tract.
Fourteen natural hazards that have some link to climate change include: avalanche,
coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide,
riverine flooding, strong wind, tornado, wildfire, and winter weather.
</div>
<ul>
<li>
@ -163,19 +163,18 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Rate relative to the population in fatalities and injuries due to natural hazards each year.
Fourteen natural hazards that have some link to climate change include: avalanche, coastal
flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine
flooding, strong wind, tornado, wildfire, and winter weather.
Population loss is defined as the Spatial Hazard Events and Losses or National Centers
for Environmental Information’s (NCEI) reported number of fatalities and injuries caused by the
hazard occurrence. To combine fatalities and injuries for the computation of population loss value,
an injury is counted as one-tenth (1/10) of a fatality. The NCEI Storm Events Database
classifies injuries and fatalities as direct or indirect. Both direct and indirect injuries
and fatalities are counted as population loss. This total number of injuries and fatalities
is then divided by the population in the census tract to get a per-capita rate of population risk.
Rate relative to the population in fatalities and injuries due to natural hazards each year.
Fourteen natural hazards that have some link to climate change include: avalanche, coastal
flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine
flooding, strong wind, tornado, wildfire, and winter weather.
Population loss is defined as the Spatial Hazard Events and Losses or National Centers
for Environmental Information’s (NCEI) reported number of fatalities and injuries caused by the
hazard occurrence. To combine fatalities and injuries for the computation of population loss value,
an injury is counted as one-tenth (1/10) of a fatality. The NCEI Storm Events Database
classifies injuries and fatalities as direct or indirect. Both direct and indirect injuries
and fatalities are counted as population loss. This total number of injuries and fatalities
is then divided by the population in the census tract to get a per-capita rate of population risk.
</div>
<ul>
<li>
@ -247,8 +246,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
PM2.5 in the air
</h3>
<div>
Fine inhalable particles, with diameters that are generally
2.5 micrometers and smaller.
Fine inhalable particles, with diameters that are generally 2.5 micrometers and smaller.
</div>
<ul>
<li>
@ -260,8 +260,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Environmental Protection Agency (EPA) Office of Air
and Radiation (OAR) fusion of model and monitor data as compiled by EPA's EJSCREEN
Environmental Protection Agency (EPA) Office of Air and Radiation (OAR) fusion of model and monitor
data as compiled by EPA's EJSCREEN, sourced from EPA National Air Toxics Assessment (NATA), 2017
U.S. Department of Transportation (DOT) traffic data
</a>
</li>
<li>
@ -285,7 +288,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Diesel particulate matter exposure
</h3>
<div>
Mixture of particles that is part of diesel exhaust in the air.
Mixture of particles that is part of diesel exhaust in the air.
</div>
<ul>
<li>
@ -297,8 +302,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA)
as compiled by EPA's EJSCREEN
Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA)
as compiled by EPA's EJSCREEN
</a>
</li>
<li>
@ -322,8 +329,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Traffic proximity and volume
</h3>
<div>
Count of vehicles (average annual daily traffic) at major roads
within 500 meters, divided by distance in meters (not km).
Count of vehicles (average annual daily traffic) at major roads
within 500 meters, divided by distance in meters (not km).
</div>
<ul>
<li>
@ -335,7 +344,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Department of Transportation (DOT) traffic data as compiled by EPA's EJSCREEN
Department of Transportation (DOT) traffic data as compiled by EPA's EJSCREEN
</a>
</li>
<li>
@ -360,9 +371,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
The percent of households in a census tract that are both earning less than 80% of HUD Area Median
Family Income by county and are paying greater than 30% of their income to housing costs.
The percent of households in a census tract that are both earning less than 80% of HUD Area Median
Family Income by county and are paying greater than 30% of their income to housing costs.
</div>
<ul>
<li>
@ -374,8 +385,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Department of Housing & Urban Development’s
(HUD) Comprehensive Housing Affordability Strategy dataset
Department of Housing & Urban Development’s
(HUD) Comprehensive Housing Affordability Strategy dataset
</a>
</li>
<li>
@ -400,8 +413,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in
tracts with median home values less than 90th percentile
Percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in
tracts with median home values less than 90th percentile
</div>
<ul>
<li>
@ -437,7 +451,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Low median home value
</h3>
<div>
Median home value of owner-occupied housing units in the census tract.
Median home value of owner-occupied housing units in the census tract.
</div>
<ul>
<li>
@ -474,9 +490,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Count of hazardous waste facilities (Treatment, Storage, and Disposal Facilities and Large
Quantity Generators) within 5 km (or nearest beyond 5 km), each divided by distance in kilometers.
Count of hazardous waste facilities (Treatment, Storage, and Disposal Facilities and Large
Quantity Generators) within 5 km (or nearest beyond 5 km), each divided by distance in kilometers.
</div>
<ul>
<li>
@ -489,16 +505,16 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
target="_blank"
>
Environmental Protection Agency (EPA) Treatment Storage, and Disposal Facilities
(TSDF) data calculated from EPA RCRA info database as compiled by EPA’s EJSCREEN
Environmental Protection Agency (EPA) Treatment Storage, and Disposal Facilities
(TSDF) data calculated from EPA RCRA info database as compiled by EPA’s EJSCREEN
</a>
</li>
<li>
<span>
Date range:
</span>
2015-2020
2020
</li>
<li>
<span>
@ -516,8 +532,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Count of proposed or listed NPL - also known as superfund - sites within 5 km (or nearest one
beyond 5 km), each divided by distance in kilometers.
Count of proposed or listed NPL - also known as superfund - sites within 5 km (or nearest one
beyond 5 km), each divided by distance in kilometers.
</div>
<ul>
<li>
@ -529,7 +546,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Environmental Protection Agency (EPA) CERCLIS database as compiled by EPA’s EJSCREEN
Environmental Protection Agency (EPA) CERCLIS database as compiled by EPA’s EJSCREEN
</a>
</li>
<li>
@ -554,8 +573,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Count of RMP (potential chemical accident management plan) facilities within 5 km (or nearest
one beyond 5 km), each divided by distance in kilometers.
Count of RMP (potential chemical accident management plan) facilities within 5 km (or nearest
one beyond 5 km), each divided by distance in kilometers.
</div>
<ul>
<li>
@ -567,7 +587,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Environmental Protection Agency (EPA) RMP database as compiled by EPA’s EJSCREEN
Environmental Protection Agency (EPA) RMP database as compiled by EPA’s EJSCREEN
</a>
</li>
<li>
@ -580,7 +602,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
<span>
Used in:
</span>
Affordable and sustainable housing methodology
Reduction and remediation of legacy pollution methodology
</li>
</ul>
</div>
@ -591,8 +613,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Wastewater discharge
</h3>
<div>
Risk-Screening Environmental Indicators (RSEI) modeled Toxic Concentrations at
stream segments within 500 meters, divided by distance in kilometers (km).
Risk-Screening Environmental Indicators (RSEI) modeled Toxic Concentrations at
stream segments within 500 meters, divided by distance in kilometers (km).
</div>
<ul>
<li>
@ -604,8 +628,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Environmental Protection Agency (EPA) Risk-Screening
Environmental Indicators (RSEI) Model as compiled by EPA's EJSCREEN
Environmental Protection Agency (EPA) Risk-Screening
Environmental Indicators (RSEI) Model as compiled by EPA's EJSCREEN
</a>
</li>
<li>
@ -629,10 +655,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Asthma
</h3>
<div>
Weighted percent of people who answer “yes” both
to both of the following questions: “Have you ever been told by a doctor,
nurse, or other health professional that you have asthma?” and the question
“Do you still have asthma?”
Weighted percent of people who answer “yes” to both of the following questions: “Have you ever
been told by a doctor, nurse, or other health professional that you have asthma?” and the question
“Do you still have asthma?”
</div>
<ul>
<li>
@ -644,7 +671,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Centers for Disease Control and Prevention (CDC) PLACES
Centers for Disease Control and Prevention (CDC) PLACES
</a>
</li>
<li>
@ -668,9 +697,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Diabetes
</h3>
<div>
Weighted percent of people ages 18 years and older who report having ever been
told by a doctor, nurse, or other health professionals that they have
diabetes other than diabetes during pregnancy.
Weighted percent of people ages 18 years and older who report having ever been
told by a doctor, nurse, or other health professionals that they have
diabetes other than diabetes during pregnancy.
</div>
<ul>
<li>
@ -682,7 +713,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Centers for Disease Control and Prevention (CDC) PLACES
Centers for Disease Control and Prevention (CDC) PLACES
</a>
</li>
<li>
@ -706,9 +739,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Heart disease
</h3>
<div>
Weighted percent of people ages 18 years and older who report ever having been told
by a doctor, nurse, or other health professionals that they had angina or
coronary heart disease.
Weighted percent of people ages 18 years and older who report ever having been told
by a doctor, nurse, or other health professionals that they had angina or
coronary heart disease.
</div>
<ul>
<li>
@ -720,7 +755,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Centers for Disease Control and Prevention (CDC) PLACES
Centers for Disease Control and Prevention (CDC) PLACES
</a>
</li>
<li>
@ -745,13 +782,22 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Average number of years of life a person who has attained a given age can expect to live.
Note: Unlike most of the other datasets, high values of this indicator indicate low burdens.
For percentile calculations, the percentile is calculated in reverse order, so that the tract with
the highest median income relative to area median income (lowest burden on this measure) is at the
0th percentile, and the tract with the lowest median income relative to area median income
(highest burden on this measure) is at the 100th percentile.
Average number of years of life a person who has attained a given age can expect to live.
<p>
<strong>
Note:
</strong>
Unlike most of the other datasets, high values of this indicator indicate low burdens.
For percentile calculations, the percentile is calculated in reverse order, so that the tract with
the highest median income relative to area median income (lowest burden on this measure) is at the
0th percentile, and the tract with the lowest median income relative to area median income
(highest burden on this measure) is at the 100th percentile.
</p>
</div>
<ul>
<li>
@ -763,7 +809,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
CDC’s U.S. Small-area Life Expectancy Estimates Project (USALEEP)
CDC’s U.S. Small-area Life Expectancy Estimates Project (USALEEP)
</a>
</li>
<li>
@ -787,7 +835,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Low median Income
</h3>
<div>
Median income of the census tract calculated as a percent of the area’s median income.
Median income of the census tract calculated as a percent of the area’s median income.
</div>
<ul>
<li>
@ -812,7 +862,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
<span>
Used in:
</span>
Training and workforce development
Training and workforce development methodology
</li>
</ul>
</div>
@ -824,8 +874,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
The percent of limited speaking households, which are households where no one over age 14 speaks English well.
The percent of limited speaking households, which are households where no one over age 14 speaks English well.
</div>
<ul>
<li>
@ -850,7 +900,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
<span>
Used in:
</span>
Training and workforce development
Training and workforce development methodology
</li>
</ul>
</div>
@ -861,7 +911,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Unemployment
</h3>
<div>
Number of unemployed people as a percentage of the civilian labor force
Number of unemployed people as a percentage of the civilian labor force
</div>
<ul>
<li>
@ -886,7 +938,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
<span>
Used in:
</span>
Training and workforce development
Training and workforce development methodology
</li>
</ul>
</div>
@ -897,7 +949,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Poverty
</h3>
<div>
Percent of a tract's population in households where the household income is at or below 100% of the federal poverty level.
Percent of a tract's population in households where the household income is at or below 100% of
the federal poverty level.
</div>
<ul>
<li>
@ -922,7 +977,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
<span>
Used in:
</span>
Training and workforce development
Training and workforce development methodology
</li>
</ul>
</div>
@ -933,8 +988,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
High school degree achievement rate
</h3>
<div>
Percent of people ages 25 years or older in a census tract whose
education level is less than a high school diploma.
Percent (not percentile) of people ages 25 years or older in a census tract whose
education level is less than a high school diploma.
</div>
<ul>
<li>
@ -959,7 +1016,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
<span>
Used in:
</span>
Training and workforce development
Training and workforce development methodology
</li>
</ul>
</div>

9
client/src/components/Indicator/Indicator.tsx
View file

@ -43,9 +43,12 @@ const Indicator = ({indicator}:IIndicator) => {
</div>
<div className={styles.indicatorValue}>
{readablePercentile(indicator.value)}
<sup className={styles.indicatorSuperscript}><span>
{getSuperscriptOrdinal(readablePercentile(indicator.value))}
</span></sup>
{indicator.isPercent ?
<span>{`%`}</span> :
<sup className={styles.indicatorSuperscript}>
<span>{getSuperscriptOrdinal(readablePercentile(indicator.value))}</span>
</sup>
}
</div>
</div>
</li>

4
client/src/components/J40Header/__snapshots__/J40Header.test.tsx.snap
View file

@ -150,8 +150,8 @@ exports[`rendering of the J40Header checks if component renders 1`] = `
This is a beta site.
</span>
<span>
It is an early, in-progress version of the tool with limited datasets
that will be continuously updated.
It is an early, in-progress version of the tool with limited datasets that will
be regularly updated.
</span>
</div>
</div>

4
client/src/data/copy/common.tsx
View file

@ -9,8 +9,8 @@ export const BETA_BANNER = defineMessages({
},
INFO: {
id: 'banner.beta.info',
defaultMessage: `It is an early, in-progress version of the tool with limited datasets
that will be continuously updated.`,
defaultMessage: `It is an early, in-progress version of the tool with limited datasets that will
be regularly updated.`,
description: 'the main info of the beta banner',
},
});

15
client/src/data/copy/explore.tsx
View file

@ -237,6 +237,16 @@ export const COMMUNITY = {
};
export const SIDE_PANEL_CATEGORY = defineMessages({
INDICATOR: {
id: 'areaDetail.category.header.indicator',
defaultMessage: 'Indicator',
description: 'header for each category',
},
PERCENTILE: {
id: 'areaDetail.category.header.percentile',
defaultMessage: 'Percentile (0-100)',
description: 'header for each category',
},
CLIMATE: {
id: 'areaDetail.indicator.title.climate',
defaultMessage: 'Climate change',
@ -550,7 +560,8 @@ export const SIDE_PANEL_INDICATOR_DESCRIPTION = defineMessages({
HIGH_SKL: {
id: 'areaDetail.indicator.description.high.school',
defaultMessage: `
Percent of people ages 25 years or older whose education level is less than a high school diploma
Percent (not a percentile) of people ages 25 years or older whose education level is less than a
high school diploma
`,
description: 'Percent of people ages 25 years or older whose education level is less than a high school diploma',
},
@ -597,7 +608,7 @@ export const NOTE_ON_TERRITORIES = {
PARA_1: <FormattedMessage
id={'explore.page.note.on.territories.para.1'}
defaultMessage={`
The data sources described on the {dataMethLink} are used to
The data sources described on the {dataMethLink} page are used to
identify disadvantaged communities for all fifty states and the District of Columbia. However, not all
of these data sources are currently available for the U.S. territories. The Census ACS data from
2015-2019 was used to identify disadvantaged communities for Puerto Rico. This uses the same methodology

741
client/src/data/copy/methodology.tsx
View file

@ -148,7 +148,20 @@ export const CATEGORY= {
// Indicator Categories copy constants:
export const CATEGORIES = {
ALL: {
METHODOLOGY:
<FormattedMessage
id= {'methodologies.all.used.in.text'}
defaultMessage= {`All methodologies except for training and workforce development`}
description= {'used in text for all methodologies'}
/>,
},
CLIMATE_CHANGE: {
METHODOLOGY: <FormattedMessage
id= {'indicator.categories.climate.change.methodology'}
defaultMessage= {`Climate change methodology`}
description= {'climate change methodology'}
/>,
TITLE: <FormattedMessage
id={'indicator.categories.climate.change.title'}
defaultMessage={'Climate change'}
@ -187,6 +200,11 @@ export const CATEGORIES = {
/>,
},
CLEAN_ENERGY: {
METHODOLOGY: <FormattedMessage
id= {'indicator.categories.climate.change.methodology'}
defaultMessage= {`Clean energy and energy efficiency methodology`}
description= {`Clean energy and energy efficiency methodology`}
/>,
TITLE: <FormattedMessage
id={'indicator.categories.clean.energy.title'}
defaultMessage={'Clean energy and energy efficiency'}
@ -224,6 +242,11 @@ export const CATEGORIES = {
/>,
},
CLEAN_TRANSPORT: {
METHODOLOGY: <FormattedMessage
id= {'indicator.categories.clean.transport.methodology'}
defaultMessage= {`Clean transportation methodology`}
description= {`Clean transportation methodology`}
/>,
TITLE: <FormattedMessage
id={'indicator.categories.clean.transport.title'}
defaultMessage={'Clean transportation'}
@ -261,6 +284,11 @@ export const CATEGORIES = {
/>,
},
AFFORDABLE_HOUSING: {
METHODOLOGY: <FormattedMessage
id= {'indicator.categories.afford.housing.methodology'}
defaultMessage= {`Affordable and sustainable housing methodology`}
description= {`Affordable and sustainable housing methodology`}
/>,
TITLE: <FormattedMessage
id={'indicator.categories.afford.house.title'}
defaultMessage={'Affordable and sustainable housing'}
@ -270,7 +298,7 @@ export const CATEGORIES = {
id= {'indicator.categories.afford.house.if'}
defaultMessage= {`
{if} at or above 90th percentile for {lead} AND {medianHomeVal} is at or less than
90th percentile OR at or above the 10th percentile for the {houseBur}
90th percentile OR at or above the 90th percentile for the {houseBur}
`}
description= {'if portion of the formula'}
values= {{
@ -300,6 +328,11 @@ export const CATEGORIES = {
/>,
},
LEGACY_POLLUTION: {
METHODOLOGY: <FormattedMessage
id= {'indicator.categories.legacy.pollute.methodology'}
defaultMessage= {`Reduction and remediation of legacy pollution methodology`}
description= {`Reduction and remediation of legacy pollution methodology`}
/>,
TITLE: <FormattedMessage
id={'indicator.categories.legacy.pollution.title'}
defaultMessage={'Reduction and remediation of legacy pollution'}
@ -338,6 +371,11 @@ export const CATEGORIES = {
/>,
},
CLEAN_WATER: {
METHODOLOGY: <FormattedMessage
id= {'indicator.categories.clean.water.methodology'}
defaultMessage= {`Critical clean water and waste infrastructure`}
description= {`Critical clean water and waste infrastructure methodology`}
/>,
TITLE: <FormattedMessage
id={'indicator.categories.clean.water.title'}
defaultMessage={'Critical clean water and waste infrastructure'}
@ -374,6 +412,11 @@ export const CATEGORIES = {
/>,
},
HEALTH_BURDENS: {
METHODOLOGY: <FormattedMessage
id= {'indicator.categories.health.burdens.methodology'}
defaultMessage= {`Health burdens methodology`}
description= {`Health burdens methodology`}
/>,
TITLE: <FormattedMessage
id={'indicator.categories.health.burdens.title'}
defaultMessage={'Health burdens'}
@ -413,6 +456,11 @@ export const CATEGORIES = {
/>,
},
WORKFORCE_DEV: {
METHODOLOGY: <FormattedMessage
id= {'indicator.categories.workforce.dev.methodology'}
defaultMessage= {`Training and workforce development methodology`}
description= {`Training and workforce development`}
/>,
TITLE: <FormattedMessage
id={'indicator.categories.work.dev.title'}
defaultMessage={'Training and workforce development'}
@ -503,303 +551,592 @@ export const DATASET_CARD_LABELS = defineMessages({
},
});
export const RESPONSIBLE_PARTIES = {
CENSUS_ACS: <FormattedMessage
id= {'category.resp.party.census.link'}
defaultMessage= {'{resPartyCensusLink}'}
description= {'responsible party link for Census ACS'}
values= {{
resPartyCensusLink:
<a href={`https://www.census.gov/programs-surveys/acs`} target={'_blank'} rel="noreferrer">
{`Census's American Community Survey`}
</a>,
}}
/>,
FEMA: <FormattedMessage
id= {'category.resp.party.fema.link'}
defaultMessage= {`{resPartyFemaLink}`}
description= {'responsible party link for FEMA'}
values={{
resPartyFemaLink:
<a href={`https://hazards.fema.gov/nri/expected-annual-loss`} target={'_blank'} rel="noreferrer">
{`Federal Emergency Management Agency (FEMA)`}
</a>,
}}
/>,
DOE: <FormattedMessage
id= {'category.resp.party.doe.link'}
defaultMessage= {`{resPartyDoeLink}`}
description= {'responsible party link for FEMA'}
values={{
resPartyDoeLink:
<a href={`https://www.energy.gov/eere/slsc/low-income-energy-affordability-data-lead-tool`}
target={'_blank'}
rel="noreferrer">
{`Department of Energy (DOE) LEAD Score`}
</a>,
}}
/>,
EPA_OAR: <FormattedMessage
id= {'category.resp.party.epa.oar.link'}
defaultMessage= {`{resPartyEpaOarLink}`}
description= {'responsible party link for EPA OAR'}
values={{
resPartyEpaOarLink:
<a href={`https://www.epa.gov/ejscreen/technical-documentation-ejscreen`}
target={'_blank'}
rel="noreferrer">
{`
Environmental Protection Agency (EPA) Office of Air and Radiation (OAR) fusion of model and monitor
data as compiled by EPA's EJSCREEN, sourced from EPA National Air Toxics Assessment (NATA), 2017
U.S. Department of Transportation (DOT) traffic data
`}
</a>,
}}
/>,
EPA_NATA: <FormattedMessage
id= {'category.resp.party.epa.nata.link'}
defaultMessage= {`{resPartyEpaOarLink}`}
description= {'responsible party link for EPA NATA'}
values={{
resPartyEpaOarLink:
<a href={`https://www.epa.gov/ejscreen/technical-documentation-ejscreen`}
target={'_blank'}
rel="noreferrer">
{`
Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA)
as compiled by EPA's EJSCREEN
`}
</a>,
}}
/>,
DOT_EPA: <FormattedMessage
id= {'category.resp.party.dot.epa.link'}
defaultMessage= {`{resPartyDotEpaLink}`}
description= {'responsible party link for DOT EPA'}
values={{
resPartyDotEpaLink:
<a href={`https://www.epa.gov/ejscreen/technical-documentation-ejscreen`}
target={'_blank'}
rel="noreferrer">
{`
Department of Transportation (DOT) traffic data as compiled by EPA's EJSCREEN
`}
</a>,
}}
/>,
HUD: <FormattedMessage
id= {'category.resp.party.hud.link'}
defaultMessage= {`{resPartyHudLink}`}
description= {'responsible party link for HUD'}
values={{
resPartyHudLink:
<a href={`https://www.huduser.gov/portal/datasets/cp.html`}
target={'_blank'}
rel="noreferrer">
{`
Department of Housing & Urban Development’s
(HUD) Comprehensive Housing Affordability Strategy dataset
`}
</a>,
}}
/>,
EPA_TSDF: <FormattedMessage
id= {'category.resp.party.epa.tsdf.link'}
defaultMessage= {`{resPartyEpaTsdfLink}`}
description= {'responsible party link for EPA TSDF'}
values={{
resPartyEpaTsdfLink:
<a href={`https://enviro.epa.gov/facts/rcrainfo/search.html`}
target={'_blank'}
rel="noreferrer">
{`
Environmental Protection Agency (EPA) Treatment Storage, and Disposal Facilities
(TSDF) data calculated from EPA RCRA info database as compiled by EPA’s EJSCREEN
`}
</a>,
}}
/>,
EPA_CERCLIS: <FormattedMessage
id= {'category.resp.party.epa.cerclis.link'}
defaultMessage= {`{resPartyEpaCerclisLink}`}
description= {'responsible party link for EPA CERCLIS'}
values={{
resPartyEpaCerclisLink:
<a href={`https://enviro.epa.gov/facts/rcrainfo/search.html`}
target={'_blank'}
rel="noreferrer">
{`
Environmental Protection Agency (EPA) CERCLIS database as compiled by EPA’s EJSCREEN
`}
</a>,
}}
/>,
EPA_RMP: <FormattedMessage
id= {'category.resp.party.epa.rmp.link'}
defaultMessage= {`{resPartyEpaRmpLink}`}
description= {'responsible party link for EPA RMP'}
values={{
resPartyEpaRmpLink:
<a href={`https://www.epa.gov/ejscreen/technical-documentation-ejscreen`}
target={'_blank'}
rel="noreferrer">
{`
Environmental Protection Agency (EPA) RMP database as compiled by EPA’s EJSCREEN
`}
</a>,
}}
/>,
EPA_RSEI: <FormattedMessage
id= {'category.resp.party.epa.rsei.link'}
defaultMessage= {`{resPartyEpaRseiLink}`}
description= {'responsible party link for EPA RSEI'}
values={{
resPartyEpaRseiLink:
<a href={`https://www.epa.gov/ejscreen/technical-documentation-ejscreen`}
target={'_blank'}
rel="noreferrer">
{`
Environmental Protection Agency (EPA) Risk-Screening
Environmental Indicators (RSEI) Model as compiled by EPA's EJSCREEN
`}
</a>,
}}
/>,
CDC_PLACES: <FormattedMessage
id= {'category.resp.party.cdc.places.link'}
defaultMessage= {`{resPartyCdcPlacesLink}`}
description= {'responsible party link for CDC Places'}
values={{
resPartyCdcPlacesLink:
<a href={`https://www.cdc.gov/places/index.html`}
target={'_blank'}
rel="noreferrer">
{`
Centers for Disease Control and Prevention (CDC) PLACES
`}
</a>,
}}
/>,
CDC_SLEEP: <FormattedMessage
id= {'category.resp.party.cdc.sleep.link'}
defaultMessage= {`{resPartyCdcSleepLink}`}
description= {'responsible party link for CDC Sleep'}
values={{
resPartyCdcSleepLink:
<a href={`https://www.cdc.gov/nchs/nvss/usaleep/usaleep.html#data`}
target={'_blank'}
rel="noreferrer">
{`
CDC’s U.S. Small-area Life Expectancy Estimates Project (USALEEP)
`}
</a>,
}}
/>,
};
export const DATE_RANGE = {
TEN_PLUS_5: '2010-2015',
FOURTEEN: '2014',
FOURTEEN_PLUS_4: '2014-2018',
FOURTEEN_PLUS_7: '2014-2021',
FIFETEEN_PLUS_4: '2015-2019',
SIXTEEN_PLUS_3: '2016-2019',
SIXTEEN_PLUS_4: '2016-2020',
SEVENTEEN: '2017',
EIGHTEEN: '2018',
TWENTY: '2020',
};
export const INDICATORS = [
{
domID: 'low-income',
indicator: 'Low income',
description: `
Percent of a block group's population in households where household income is at or below
200% of the federal poverty level.
`,
usedIn:
<FormattedMessage
id= {'category.low.income.used.in'}
defaultMessage= {`All methodologies except for training and workforce development`}
description= {'used in text for low income'}
/>,
respPartyLabel:
<FormattedMessage
id= {'category.low.income.resp.party.label'}
defaultMessage= {'{lowIncResPartyLinkText}.'}
description= {'responsible party label for low.income'}
values= {{
lowIncResPartyLinkText: `Census's American Community Survey`,
}}
/>,
dataSourceURL: `https://www.census.gov/programs-surveys/acs`,
dateRange: `2015-2019`,
description: <FormattedMessage
id= {'category.low.income.description.text'}
defaultMessage= {`
Percent of a census tract's population in households where household income is at or below
200% of the federal poverty level.
`}
description= {'description text for low income'}
/>,
usedIn: CATEGORIES.ALL.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS,
dateRange: DATE_RANGE.FIFETEEN_PLUS_4,
},
{
domID: 'exp-agr-loss-rate',
indicator: 'Expected agriculture loss rate',
description: `
Percent of agriculture value at risk from losses due to natural hazards. Calculated by dividing
the agriculture value at risk in a census tract by the total agriculture value in that census
tract. Fourteen natural hazards that have some link to climate change include: avalanche,
coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide,
riverine flooding, strong wind, tornado, wildfire, and winter weather.
`,
usedIn: `Climate change methodology`,
respPartyLabel: `Federal Emergency Management Agency (FEMA)`,
dataSourceURL: `https://hazards.fema.gov/nri/expected-annual-loss`,
dateRange: `2014-2021`,
description: <FormattedMessage
id= {'category.exp.agr.loss.rate.description.text'}
defaultMessage= {`
Percent of agriculture value at risk from losses due to natural hazards. Calculated by dividing
the agriculture value at risk in a census tract by the total agriculture value in that census
tract. Fourteen natural hazards that have some link to climate change include: avalanche,
coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide,
riverine flooding, strong wind, tornado, wildfire, and winter weather.
`}
description= {'description text for exp agr loss rate'}
/>,
usedIn: CATEGORIES.CLIMATE_CHANGE.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.FEMA,
dateRange: DATE_RANGE.FOURTEEN_PLUS_7,
},
{
domID: 'exp-bld-loss-rate',
indicator: 'Expected building loss rate',
description: `
Percent of building value at risk from losses due to natural hazards. Calculated by dividing the
building value at risk in a census tract by the total building value in that census tract.
Fourteen natural hazards that have some link to climate change include: avalanche, coastal flooding,
cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine flooding, strong
wind, tornado, wildfire, and winter weather.
`,
usedIn: `Climate change methodology`,
respPartyLabel: `Federal Emergency Management Agency (FEMA)`,
dataSourceURL: `https://hazards.fema.gov/nri/expected-annual-loss`,
dateRange: `2014-2021`,
description: <FormattedMessage
id= {'category.exp.bld.loss.rate.description.text'}
defaultMessage= {`
Percent of building value at risk from losses due to natural hazards. Calculated by dividing the
building value at risk in a census tract by the total building value in that census tract.
Fourteen natural hazards that have some link to climate change include: avalanche,
coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide,
riverine flooding, strong wind, tornado, wildfire, and winter weather.
`}
description= {'description text for exp bld loss rate'}
/>,
usedIn: CATEGORIES.CLIMATE_CHANGE.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.FEMA,
dateRange: DATE_RANGE.FOURTEEN_PLUS_7,
},
{
domID: 'exp-pop-loss-rate',
indicator: 'Expected population loss rate',
description: `
Rate relative to the population in fatalities and injuries due to natural hazards each year.
Fourteen natural hazards that have some link to climate change include: avalanche, coastal
flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine
flooding, strong wind, tornado, wildfire, and winter weather.
Population loss is defined as the Spatial Hazard Events and Losses or National Centers
for Environmental Information’s (NCEI) reported number of fatalities and injuries caused by the
hazard occurrence. To combine fatalities and injuries for the computation of population loss value,
an injury is counted as one-tenth (1/10) of a fatality. The NCEI Storm Events Database
classifies injuries and fatalities as direct or indirect. Both direct and indirect injuries
and fatalities are counted as population loss. This total number of injuries and fatalities
is then divided by the population in the census tract to get a per-capita rate of population risk.
`,
usedIn: `Climate change methodology`,
respPartyLabel: `Federal Emergency Management Agency (FEMA)`,
dataSourceURL: `https://hazards.fema.gov/nri/expected-annual-loss`,
dateRange: `2014-2021`,
description: <FormattedMessage
id= {'category.exp.pop.loss.rate.description.text'}
defaultMessage= {`
Rate relative to the population in fatalities and injuries due to natural hazards each year.
Fourteen natural hazards that have some link to climate change include: avalanche, coastal
flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine
flooding, strong wind, tornado, wildfire, and winter weather.
Population loss is defined as the Spatial Hazard Events and Losses or National Centers
for Environmental Information’s (NCEI) reported number of fatalities and injuries caused by the
hazard occurrence. To combine fatalities and injuries for the computation of population loss value,
an injury is counted as one-tenth (1/10) of a fatality. The NCEI Storm Events Database
classifies injuries and fatalities as direct or indirect. Both direct and indirect injuries
and fatalities are counted as population loss. This total number of injuries and fatalities
is then divided by the population in the census tract to get a per-capita rate of population risk.
`}
description= {'description text for exp pop loss rate'}
/>,
usedIn: CATEGORIES.CLIMATE_CHANGE.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.FEMA,
dateRange: DATE_RANGE.FOURTEEN_PLUS_7,
},
{
domID: 'energy-burden',
indicator: 'Energy cost burden',
description: `Average annual energy cost ($) divided by household income.`,
usedIn: `Clean energy and energy efficiency methodology`,
respPartyLabel: `Department of Energy (DOE) LEAD Score`,
dataSourceURL: `https://www.energy.gov/eere/slsc/low-income-energy-affordability-data-lead-tool`,
dateRange: `2018`,
description: <FormattedMessage
id= {'category.energy.burden.description.text'}
defaultMessage= {`Average annual energy cost ($) divided by household income.`}
description= {'description text for energy burden'}
/>,
usedIn: CATEGORIES.CLEAN_ENERGY.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.DOE,
dateRange: DATE_RANGE.EIGHTEEN,
},
{
domID: 'pm-25',
indicator: 'PM2.5 in the air',
description: `Fine inhalable particles, with diameters that are generally
2.5 micrometers and smaller.`,
usedIn: `Clean energy and energy efficiency methodology`,
respPartyLabel: `Environmental Protection Agency (EPA) Office of Air
and Radiation (OAR) fusion of model and monitor data as compiled by EPA's EJSCREEN`,
dataSourceURL: `https://www.epa.gov/ejscreen/technical-documentation-ejscreen`,
dateRange: `2017`,
description: <FormattedMessage
id= {'category.pm2.5.description.text'}
defaultMessage= {`
Fine inhalable particles, with diameters that are generally 2.5 micrometers and smaller.
`}
description= {'description text for pm 2.5'}
/>,
usedIn: CATEGORIES.CLEAN_ENERGY.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.EPA_OAR,
dateRange: DATE_RANGE.SEVENTEEN,
},
{
domID: 'diesel-pm',
indicator: 'Diesel particulate matter exposure',
description: `Mixture of particles that is part of diesel exhaust in the air.`,
usedIn: `Clean transportation methodology`,
respPartyLabel: `Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA)
as compiled by EPA's EJSCREEN`,
dataSourceURL: `https://www.epa.gov/ejscreen/technical-documentation-ejscreen`,
dateRange: `2014`,
description: <FormattedMessage
id= {'category.diesel.pm.description.text'}
defaultMessage= {`
Mixture of particles that is part of diesel exhaust in the air.
`}
description= {'description text for diesel pm'}
/>,
usedIn: CATEGORIES.CLEAN_TRANSPORT.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.EPA_NATA,
dateRange: DATE_RANGE.FOURTEEN,
},
{
domID: 'traffic-vol',
indicator: 'Traffic proximity and volume',
description: `Count of vehicles (average annual daily traffic) at major roads
within 500 meters, divided by distance in meters (not km).`,
usedIn: `Clean transportation methodology`,
respPartyLabel: `Department of Transportation (DOT) traffic data as compiled by EPA's EJSCREEN`,
dataSourceURL: `https://www.epa.gov/ejscreen/technical-documentation-ejscreen`,
dateRange: `2017`,
description: <FormattedMessage
id= {'category.traffic.vol.description.text'}
defaultMessage= {`
Count of vehicles (average annual daily traffic) at major roads
within 500 meters, divided by distance in meters (not km).
`}
description= {'description text for traffic volume'}
/>,
usedIn: CATEGORIES.CLEAN_TRANSPORT.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.DOT_EPA,
dateRange: DATE_RANGE.SEVENTEEN,
},
{
domID: 'house-burden',
indicator: 'Housing cost burden',
description: `
The percent of households in a census tract that are both earning less than 80% of HUD Area Median
Family Income by county and are paying greater than 30% of their income to housing costs.
`,
usedIn: `Affordable and sustainable housing methodology`,
respPartyLabel: `Department of Housing & Urban Development’s
(HUD) Comprehensive Housing Affordability Strategy dataset`,
dataSourceURL: `https://www.huduser.gov/portal/datasets/cp.html`,
dateRange: `2014-2018`,
description: <FormattedMessage
id= {'category.house.burden.description.text'}
defaultMessage= {`
The percent of households in a census tract that are both earning less than 80% of HUD Area Median
Family Income by county and are paying greater than 30% of their income to housing costs.
`}
description= {'description text for housing burden'}
/>,
usedIn: CATEGORIES.AFFORDABLE_HOUSING.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.HUD,
dateRange: DATE_RANGE.FOURTEEN_PLUS_4,
},
{
domID: 'lead-paint',
indicator: 'Lead paint',
description: `
Percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in
tracts with median home values less than 90th percentile `,
usedIn: `Affordable and sustainable housing methodology`,
respPartyLabel: `Census's American Community Survey`,
dataSourceURL: `https://www.census.gov/programs-surveys/acs`,
dateRange: `2015-2019`,
description: <FormattedMessage
id= {'category.lead.paint.description.text'}
defaultMessage= {`
Percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in
tracts with median home values less than 90th percentile
`}
description= {'description text for lead paint'}
/>,
usedIn: CATEGORIES.AFFORDABLE_HOUSING.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS,
dateRange: DATE_RANGE.FIFETEEN_PLUS_4,
},
{
domID: 'median-home',
indicator: 'Low median home value',
description: `Median home value of owner-occupied housing units in the census tract.`,
usedIn: `Affordable and sustainable housing methodology`,
respPartyLabel: `Census's American Community Survey`,
dataSourceURL: `https://www.census.gov/programs-surveys/acs`,
dateRange: `2015-2019`,
description: <FormattedMessage
id= {'category.lead.paint.description.text'}
defaultMessage= {`
Median home value of owner-occupied housing units in the census tract.
`}
description= {'description text for lead paint'}
/>,
usedIn: CATEGORIES.AFFORDABLE_HOUSING.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS,
dateRange: DATE_RANGE.FIFETEEN_PLUS_4,
},
{
domID: 'prox-haz',
indicator: 'Proximity to hazardous waste facilities',
description: `
Count of hazardous waste facilities (Treatment, Storage, and Disposal Facilities and Large
Quantity Generators) within 5 km (or nearest beyond 5 km), each divided by distance in kilometers.
`,
usedIn: `Reduction and remediation of legacy pollution methodology`,
respPartyLabel: `
Environmental Protection Agency (EPA) Treatment Storage, and Disposal Facilities
(TSDF) data calculated from EPA RCRA info database as compiled by EPA’s EJSCREEN
`,
dataSourceURL: `https://enviro.epa.gov/facts/rcrainfo/search.html`,
dateRange: `2015-2020`,
description: <FormattedMessage
id= {'category.prox.haz.description.text'}
defaultMessage= {`
Count of hazardous waste facilities (Treatment, Storage, and Disposal Facilities and Large
Quantity Generators) within 5 km (or nearest beyond 5 km), each divided by distance in kilometers.
`}
description= {'description text for proximity to hazards'}
/>,
usedIn: CATEGORIES.LEGACY_POLLUTION.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.EPA_TSDF,
dateRange: DATE_RANGE.TWENTY,
},
{
domID: 'prox-npl',
indicator: 'Proximity to National Priorities List (NPL) sites',
description: `
Count of proposed or listed NPL - also known as superfund - sites within 5 km (or nearest one
beyond 5 km), each divided by distance in kilometers.`,
usedIn: `Reduction and remediation of legacy pollution methodology`,
respPartyLabel: `Environmental Protection Agency (EPA) CERCLIS database as compiled by EPA’s EJSCREEN`,
dataSourceURL: `https://enviro.epa.gov/facts/rcrainfo/search.html`,
dateRange: `2020`,
description: <FormattedMessage
id= {'category.prox.npl.description.text'}
defaultMessage= {`
Count of proposed or listed NPL - also known as superfund - sites within 5 km (or nearest one
beyond 5 km), each divided by distance in kilometers.
`}
description= {'description text for proximity to npl'}
/>,
usedIn: CATEGORIES.LEGACY_POLLUTION.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.EPA_CERCLIS,
dateRange: DATE_RANGE.TWENTY,
},
{
domID: 'prox-rmp',
indicator: 'Proximity to Risk Management Plan (RMP) facilities',
description: `
Count of RMP (potential chemical accident management plan) facilities within 5 km (or nearest
one beyond 5 km), each divided by distance in kilometers.`,
usedIn: `Affordable and sustainable housing methodology`,
respPartyLabel: `Environmental Protection Agency (EPA) RMP database as compiled by EPA’s EJSCREEN`,
dataSourceURL: `https://www.epa.gov/ejscreen/technical-documentation-ejscreen`,
dateRange: `2020`,
description: <FormattedMessage
id= {'category.prox.rmp.description.text'}
defaultMessage= {`
Count of RMP (potential chemical accident management plan) facilities within 5 km (or nearest
one beyond 5 km), each divided by distance in kilometers.
`}
description= {'description text for proximity to rmp'}
/>,
usedIn: CATEGORIES.LEGACY_POLLUTION.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.EPA_RMP,
dateRange: DATE_RANGE.TWENTY,
},
{
domID: 'waste-water',
indicator: 'Wastewater discharge',
description: `Risk-Screening Environmental Indicators (RSEI) modeled Toxic Concentrations at
stream segments within 500 meters, divided by distance in kilometers (km).`,
usedIn: `Critical clean water and waste infrastructure`,
respPartyLabel: `Environmental Protection Agency (EPA) Risk-Screening
Environmental Indicators (RSEI) Model as compiled by EPA's EJSCREEN`,
dataSourceURL: `https://www.epa.gov/ejscreen/technical-documentation-ejscreen`,
dateRange: `2020`,
description: <FormattedMessage
id= {'category.waste.water.description.text'}
defaultMessage= {`
Risk-Screening Environmental Indicators (RSEI) modeled Toxic Concentrations at
stream segments within 500 meters, divided by distance in kilometers (km).
`}
description= {'description text for waste water'}
/>,
usedIn: CATEGORIES.CLEAN_WATER.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.EPA_RSEI,
dateRange: DATE_RANGE.TWENTY,
},
{
domID: 'asthma',
indicator: 'Asthma',
description: `Weighted percent of people who answer “yes” both
to both of the following questions: “Have you ever been told by a doctor,
nurse, or other health professional that you have asthma?” and the question
“Do you still have asthma?”`,
usedIn: `Health burdens methodology`,
respPartyLabel: `Centers for Disease Control and Prevention (CDC) PLACES`,
dataSourceURL: `https://www.cdc.gov/places/index.html`,
dateRange: `2016-2019`,
description: <FormattedMessage
id= {'category.asthma.description.text'}
defaultMessage= {`
Weighted percent of people who answer “yes” to both of the following questions: “Have you ever
been told by a doctor, nurse, or other health professional that you have asthma?” and the question
“Do you still have asthma?”
`}
description= {'description text for asthma'}
/>,
usedIn: CATEGORIES.HEALTH_BURDENS.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.CDC_PLACES,
dateRange: DATE_RANGE.SIXTEEN_PLUS_3,
},
{
domID: 'diabetes',
indicator: 'Diabetes',
description: `Weighted percent of people ages 18 years and older who report having ever been
told by a doctor, nurse, or other health professionals that they have
diabetes other than diabetes during pregnancy.`,
usedIn: `Health burdens methodology`,
respPartyLabel: `Centers for Disease Control and Prevention (CDC) PLACES`,
dataSourceURL: `https://www.cdc.gov/places/index.html`,
dateRange: `2016-2019`,
description: <FormattedMessage
id= {'category.diabetes.description.text'}
defaultMessage= {`
Weighted percent of people ages 18 years and older who report having ever been
told by a doctor, nurse, or other health professionals that they have
diabetes other than diabetes during pregnancy.
`}
description= {'description text for diabetes'}
/>,
usedIn: CATEGORIES.HEALTH_BURDENS.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.CDC_PLACES,
dateRange: DATE_RANGE.SIXTEEN_PLUS_3,
},
{
domID: 'heart-disease',
indicator: 'Heart disease',
description: `Weighted percent of people ages 18 years and older who report ever having been told
by a doctor, nurse, or other health professionals that they had angina or
coronary heart disease.`,
usedIn: `Health burdens methodology`,
respPartyLabel: `Centers for Disease Control and Prevention (CDC) PLACES`,
dataSourceURL: `https://www.cdc.gov/places/index.html`,
dateRange: `2016-2019`,
description: <FormattedMessage
id= {'category.diabetes.description.text'}
defaultMessage= {`
Weighted percent of people ages 18 years and older who report ever having been told
by a doctor, nurse, or other health professionals that they had angina or
coronary heart disease.
`}
description= {'description text for diabetes'}
/>,
usedIn: CATEGORIES.HEALTH_BURDENS.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.CDC_PLACES,
dateRange: DATE_RANGE.SIXTEEN_PLUS_3,
},
{
domID: 'life-exp',
indicator: 'Low life expectancy',
description: `
Average number of years of life a person who has attained a given age can expect to live.
Note: Unlike most of the other datasets, high values of this indicator indicate low burdens.
For percentile calculations, the percentile is calculated in reverse order, so that the tract with
the highest median income relative to area median income (lowest burden on this measure) is at the
0th percentile, and the tract with the lowest median income relative to area median income
(highest burden on this measure) is at the 100th percentile.
`,
usedIn: `Health burdens methodology`,
respPartyLabel: `CDC’s U.S. Small-area Life Expectancy Estimates Project (USALEEP)`,
dataSourceURL: `https://www.cdc.gov/nchs/nvss/usaleep/usaleep.html#data`,
dateRange: `2010-2015`,
description: <FormattedMessage
id= {'category.low.life.expectancy.description.text'}
defaultMessage= {`
Average number of years of life a person who has attained a given age can expect to live.
{note}
`}
description= {'description text for low life expectancy'}
values= {{
note: <p><strong>Note:</strong>{`
Unlike most of the other datasets, high values of this indicator indicate low burdens.
For percentile calculations, the percentile is calculated in reverse order, so that the tract with
the highest median income relative to area median income (lowest burden on this measure) is at the
0th percentile, and the tract with the lowest median income relative to area median income
(highest burden on this measure) is at the 100th percentile.
`}</p>,
}}
/>,
usedIn: CATEGORIES.HEALTH_BURDENS.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.CDC_SLEEP,
dateRange: DATE_RANGE.TEN_PLUS_5,
},
{
domID: 'low-med-inc',
indicator: 'Low median Income',
description: `Median income of the census tract calculated as a percent of the area’s median income.`,
usedIn: `Training and workforce development`,
respPartyLabel: `Census's American Community Survey`,
dataSourceURL: `https://www.census.gov/programs-surveys/acs`,
dateRange: `2015-2019`,
description: <FormattedMessage
id= {'category.workforce.dev.description.text'}
defaultMessage= {`
Median income of the census tract calculated as a percent of the area’s median income.
`}
description= {'description text for workforce dev'}
/>,
usedIn: CATEGORIES.WORKFORCE_DEV.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS,
dateRange: DATE_RANGE.FIFETEEN_PLUS_4,
},
{
domID: 'ling-iso',
indicator: 'Linguistic Isolation',
description: `
The percent of limited speaking households, which are households where no one over age 14 speaks English well.
`,
usedIn: `Training and workforce development`,
respPartyLabel: `Census's American Community Survey`,
dataSourceURL: `https://www.census.gov/programs-surveys/acs`,
dateRange: `2015-2019`,
description: <FormattedMessage
id= {'category.linguistic.iso.description.text'}
defaultMessage= {`
The percent of limited speaking households, which are households where no one over age 14 speaks English well.
`}
description= {'description text for linguistic isolation'}
/>,
usedIn: CATEGORIES.WORKFORCE_DEV.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS,
dateRange: DATE_RANGE.FIFETEEN_PLUS_4,
},
{
domID: 'unemploy',
indicator: 'Unemployment',
description: `Number of unemployed people as a percentage of the civilian labor force`,
usedIn: `Training and workforce development`,
respPartyLabel: `Census's American Community Survey`,
dataSourceURL: `https://www.census.gov/programs-surveys/acs`,
dateRange: `2015-2019`,
description: <FormattedMessage
id= {'category.unemploy.description.text'}
defaultMessage= {`
Number of unemployed people as a percentage of the civilian labor force
`}
description= {'description text for unemployment'}
/>,
usedIn: CATEGORIES.WORKFORCE_DEV.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS,
dateRange: DATE_RANGE.FIFETEEN_PLUS_4,
},
{
domID: 'poverty',
indicator: 'Poverty',
description: `Percent of a tract's population in households where the household income` +
` is at or below 100% of the federal poverty level.`,
usedIn: `Training and workforce development`,
respPartyLabel: `Census's American Community Survey`,
dataSourceURL: `https://www.census.gov/programs-surveys/acs`,
dateRange: `2015-2019`,
description: <FormattedMessage
id= {'category.poverty.description.text'}
defaultMessage= {`
Percent of a tract's population in households where the household income is at or below 100% of
the federal poverty level.
`}
description= {'description text for poverty'}
/>,
usedIn: CATEGORIES.WORKFORCE_DEV.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS,
dateRange: DATE_RANGE.FIFETEEN_PLUS_4,
},
{
domID: 'high-school',
indicator: 'High school degree achievement rate',
description: `Percent of people ages 25 years or older in a census tract whose
education level is less than a high school diploma.`,
usedIn: `Training and workforce development`,
respPartyLabel: `Census's American Community Survey`,
dataSourceURL: `https://www.census.gov/programs-surveys/acs`,
dateRange: `2015-2019`,
description: <FormattedMessage
id= {'category.highschool.description.text'}
defaultMessage= {`
Percent (not percentile) of people ages 25 years or older in a census tract whose
education level is less than a high school diploma.
`}
description= {'description text for highschool'}
/>,
usedIn: CATEGORIES.WORKFORCE_DEV.METHODOLOGY,
responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS,
dateRange: DATE_RANGE.FIFETEEN_PLUS_4,
isPercent: true,
},
];

4
client/src/pages/__snapshots__/contact.test.tsx.snap
View file

@ -150,8 +150,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
This is a beta site.
</span>
<span>
It is an early, in-progress version of the tool with limited datasets
that will be continuously updated.
It is an early, in-progress version of the tool with limited datasets that will
be regularly updated.
</span>
</div>
</div>

4
client/src/pages/__snapshots__/index.test.tsx.snap
View file

@ -150,8 +150,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
This is a beta site.
</span>
<span>
It is an early, in-progress version of the tool with limited datasets
that will be continuously updated.
It is an early, in-progress version of the tool with limited datasets that will
be regularly updated.
</span>
</div>
</div>

259
client/src/pages/__snapshots__/methodology.test.tsx.snap
View file

@ -150,8 +150,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
This is a beta site.
</span>
<span>
It is an early, in-progress version of the tool with limited datasets
that will be continuously updated.
It is an early, in-progress version of the tool with limited datasets that will
be regularly updated.
</span>
</div>
</div>
@ -575,7 +575,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
low median home value
</a>
is at or less than
90th percentile OR at or above the 10th percentile for the
90th percentile OR at or above the 90th percentile for the
<a
href="#house-burden"
>
@ -836,9 +836,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Percent of a block group's population in households where household income is at or below
200% of the federal poverty level.
Percent of a census tract's population in households where household income is at or below
200% of the federal poverty level.
</div>
<ul>
<li>
@ -850,7 +850,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Census's American Community Survey.
Census's American Community Survey
</a>
</li>
<li>
@ -875,12 +875,12 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Percent of agriculture value at risk from losses due to natural hazards. Calculated by dividing
the agriculture value at risk in a census tract by the total agriculture value in that census
tract. Fourteen natural hazards that have some link to climate change include: avalanche,
coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide,
riverine flooding, strong wind, tornado, wildfire, and winter weather.
Percent of agriculture value at risk from losses due to natural hazards. Calculated by dividing
the agriculture value at risk in a census tract by the total agriculture value in that census
tract. Fourteen natural hazards that have some link to climate change include: avalanche,
coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide,
riverine flooding, strong wind, tornado, wildfire, and winter weather.
</div>
<ul>
<li>
@ -917,12 +917,12 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Percent of building value at risk from losses due to natural hazards. Calculated by dividing the
building value at risk in a census tract by the total building value in that census tract.
Fourteen natural hazards that have some link to climate change include: avalanche, coastal flooding,
cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine flooding, strong
wind, tornado, wildfire, and winter weather.
Percent of building value at risk from losses due to natural hazards. Calculated by dividing the
building value at risk in a census tract by the total building value in that census tract.
Fourteen natural hazards that have some link to climate change include: avalanche,
coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide,
riverine flooding, strong wind, tornado, wildfire, and winter weather.
</div>
<ul>
<li>
@ -959,19 +959,18 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Rate relative to the population in fatalities and injuries due to natural hazards each year.
Fourteen natural hazards that have some link to climate change include: avalanche, coastal
flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine
flooding, strong wind, tornado, wildfire, and winter weather.
Population loss is defined as the Spatial Hazard Events and Losses or National Centers
for Environmental Information’s (NCEI) reported number of fatalities and injuries caused by the
hazard occurrence. To combine fatalities and injuries for the computation of population loss value,
an injury is counted as one-tenth (1/10) of a fatality. The NCEI Storm Events Database
classifies injuries and fatalities as direct or indirect. Both direct and indirect injuries
and fatalities are counted as population loss. This total number of injuries and fatalities
is then divided by the population in the census tract to get a per-capita rate of population risk.
Rate relative to the population in fatalities and injuries due to natural hazards each year.
Fourteen natural hazards that have some link to climate change include: avalanche, coastal
flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine
flooding, strong wind, tornado, wildfire, and winter weather.
Population loss is defined as the Spatial Hazard Events and Losses or National Centers
for Environmental Information’s (NCEI) reported number of fatalities and injuries caused by the
hazard occurrence. To combine fatalities and injuries for the computation of population loss value,
an injury is counted as one-tenth (1/10) of a fatality. The NCEI Storm Events Database
classifies injuries and fatalities as direct or indirect. Both direct and indirect injuries
and fatalities are counted as population loss. This total number of injuries and fatalities
is then divided by the population in the census tract to get a per-capita rate of population risk.
</div>
<ul>
<li>
@ -1043,8 +1042,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
PM2.5 in the air
</h3>
<div>
Fine inhalable particles, with diameters that are generally
2.5 micrometers and smaller.
Fine inhalable particles, with diameters that are generally 2.5 micrometers and smaller.
</div>
<ul>
<li>
@ -1056,8 +1056,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Environmental Protection Agency (EPA) Office of Air
and Radiation (OAR) fusion of model and monitor data as compiled by EPA's EJSCREEN
Environmental Protection Agency (EPA) Office of Air and Radiation (OAR) fusion of model and monitor
data as compiled by EPA's EJSCREEN, sourced from EPA National Air Toxics Assessment (NATA), 2017
U.S. Department of Transportation (DOT) traffic data
</a>
</li>
<li>
@ -1081,7 +1084,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Diesel particulate matter exposure
</h3>
<div>
Mixture of particles that is part of diesel exhaust in the air.
Mixture of particles that is part of diesel exhaust in the air.
</div>
<ul>
<li>
@ -1093,8 +1098,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA)
as compiled by EPA's EJSCREEN
Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA)
as compiled by EPA's EJSCREEN
</a>
</li>
<li>
@ -1118,8 +1125,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Traffic proximity and volume
</h3>
<div>
Count of vehicles (average annual daily traffic) at major roads
within 500 meters, divided by distance in meters (not km).
Count of vehicles (average annual daily traffic) at major roads
within 500 meters, divided by distance in meters (not km).
</div>
<ul>
<li>
@ -1131,7 +1140,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Department of Transportation (DOT) traffic data as compiled by EPA's EJSCREEN
Department of Transportation (DOT) traffic data as compiled by EPA's EJSCREEN
</a>
</li>
<li>
@ -1156,9 +1167,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
The percent of households in a census tract that are both earning less than 80% of HUD Area Median
Family Income by county and are paying greater than 30% of their income to housing costs.
The percent of households in a census tract that are both earning less than 80% of HUD Area Median
Family Income by county and are paying greater than 30% of their income to housing costs.
</div>
<ul>
<li>
@ -1170,8 +1181,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Department of Housing & Urban Development’s
(HUD) Comprehensive Housing Affordability Strategy dataset
Department of Housing & Urban Development’s
(HUD) Comprehensive Housing Affordability Strategy dataset
</a>
</li>
<li>
@ -1196,8 +1209,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in
tracts with median home values less than 90th percentile
Percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in
tracts with median home values less than 90th percentile
</div>
<ul>
<li>
@ -1233,7 +1247,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Low median home value
</h3>
<div>
Median home value of owner-occupied housing units in the census tract.
Median home value of owner-occupied housing units in the census tract.
</div>
<ul>
<li>
@ -1270,9 +1286,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Count of hazardous waste facilities (Treatment, Storage, and Disposal Facilities and Large
Quantity Generators) within 5 km (or nearest beyond 5 km), each divided by distance in kilometers.
Count of hazardous waste facilities (Treatment, Storage, and Disposal Facilities and Large
Quantity Generators) within 5 km (or nearest beyond 5 km), each divided by distance in kilometers.
</div>
<ul>
<li>
@ -1285,16 +1301,16 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
target="_blank"
>
Environmental Protection Agency (EPA) Treatment Storage, and Disposal Facilities
(TSDF) data calculated from EPA RCRA info database as compiled by EPA’s EJSCREEN
Environmental Protection Agency (EPA) Treatment Storage, and Disposal Facilities
(TSDF) data calculated from EPA RCRA info database as compiled by EPA’s EJSCREEN
</a>
</li>
<li>
<span>
Date range:
</span>
2015-2020
2020
</li>
<li>
<span>
@ -1312,8 +1328,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Count of proposed or listed NPL - also known as superfund - sites within 5 km (or nearest one
beyond 5 km), each divided by distance in kilometers.
Count of proposed or listed NPL - also known as superfund - sites within 5 km (or nearest one
beyond 5 km), each divided by distance in kilometers.
</div>
<ul>
<li>
@ -1325,7 +1342,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Environmental Protection Agency (EPA) CERCLIS database as compiled by EPA’s EJSCREEN
Environmental Protection Agency (EPA) CERCLIS database as compiled by EPA’s EJSCREEN
</a>
</li>
<li>
@ -1350,8 +1369,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Count of RMP (potential chemical accident management plan) facilities within 5 km (or nearest
one beyond 5 km), each divided by distance in kilometers.
Count of RMP (potential chemical accident management plan) facilities within 5 km (or nearest
one beyond 5 km), each divided by distance in kilometers.
</div>
<ul>
<li>
@ -1363,7 +1383,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Environmental Protection Agency (EPA) RMP database as compiled by EPA’s EJSCREEN
Environmental Protection Agency (EPA) RMP database as compiled by EPA’s EJSCREEN
</a>
</li>
<li>
@ -1376,7 +1398,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
<span>
Used in:
</span>
Affordable and sustainable housing methodology
Reduction and remediation of legacy pollution methodology
</li>
</ul>
</div>
@ -1387,8 +1409,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Wastewater discharge
</h3>
<div>
Risk-Screening Environmental Indicators (RSEI) modeled Toxic Concentrations at
stream segments within 500 meters, divided by distance in kilometers (km).
Risk-Screening Environmental Indicators (RSEI) modeled Toxic Concentrations at
stream segments within 500 meters, divided by distance in kilometers (km).
</div>
<ul>
<li>
@ -1400,8 +1424,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Environmental Protection Agency (EPA) Risk-Screening
Environmental Indicators (RSEI) Model as compiled by EPA's EJSCREEN
Environmental Protection Agency (EPA) Risk-Screening
Environmental Indicators (RSEI) Model as compiled by EPA's EJSCREEN
</a>
</li>
<li>
@ -1425,10 +1451,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Asthma
</h3>
<div>
Weighted percent of people who answer “yes” both
to both of the following questions: “Have you ever been told by a doctor,
nurse, or other health professional that you have asthma?” and the question
“Do you still have asthma?”
Weighted percent of people who answer “yes” to both of the following questions: “Have you ever
been told by a doctor, nurse, or other health professional that you have asthma?” and the question
“Do you still have asthma?”
</div>
<ul>
<li>
@ -1440,7 +1467,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Centers for Disease Control and Prevention (CDC) PLACES
Centers for Disease Control and Prevention (CDC) PLACES
</a>
</li>
<li>
@ -1464,9 +1493,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Diabetes
</h3>
<div>
Weighted percent of people ages 18 years and older who report having ever been
told by a doctor, nurse, or other health professionals that they have
diabetes other than diabetes during pregnancy.
Weighted percent of people ages 18 years and older who report having ever been
told by a doctor, nurse, or other health professionals that they have
diabetes other than diabetes during pregnancy.
</div>
<ul>
<li>
@ -1478,7 +1509,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Centers for Disease Control and Prevention (CDC) PLACES
Centers for Disease Control and Prevention (CDC) PLACES
</a>
</li>
<li>
@ -1502,9 +1535,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Heart disease
</h3>
<div>
Weighted percent of people ages 18 years and older who report ever having been told
by a doctor, nurse, or other health professionals that they had angina or
coronary heart disease.
Weighted percent of people ages 18 years and older who report ever having been told
by a doctor, nurse, or other health professionals that they had angina or
coronary heart disease.
</div>
<ul>
<li>
@ -1516,7 +1551,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
Centers for Disease Control and Prevention (CDC) PLACES
Centers for Disease Control and Prevention (CDC) PLACES
</a>
</li>
<li>
@ -1541,13 +1578,22 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
Average number of years of life a person who has attained a given age can expect to live.
Note: Unlike most of the other datasets, high values of this indicator indicate low burdens.
For percentile calculations, the percentile is calculated in reverse order, so that the tract with
the highest median income relative to area median income (lowest burden on this measure) is at the
0th percentile, and the tract with the lowest median income relative to area median income
(highest burden on this measure) is at the 100th percentile.
Average number of years of life a person who has attained a given age can expect to live.
<p>
<strong>
Note:
</strong>
Unlike most of the other datasets, high values of this indicator indicate low burdens.
For percentile calculations, the percentile is calculated in reverse order, so that the tract with
the highest median income relative to area median income (lowest burden on this measure) is at the
0th percentile, and the tract with the lowest median income relative to area median income
(highest burden on this measure) is at the 100th percentile.
</p>
</div>
<ul>
<li>
@ -1559,7 +1605,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
rel="noreferrer"
target="_blank"
>
CDC’s U.S. Small-area Life Expectancy Estimates Project (USALEEP)
CDC’s U.S. Small-area Life Expectancy Estimates Project (USALEEP)
</a>
</li>
<li>
@ -1583,7 +1631,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Low median Income
</h3>
<div>
Median income of the census tract calculated as a percent of the area’s median income.
Median income of the census tract calculated as a percent of the area’s median income.
</div>
<ul>
<li>
@ -1608,7 +1658,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
<span>
Used in:
</span>
Training and workforce development
Training and workforce development methodology
</li>
</ul>
</div>
@ -1620,8 +1670,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
</h3>
<div>
The percent of limited speaking households, which are households where no one over age 14 speaks English well.
The percent of limited speaking households, which are households where no one over age 14 speaks English well.
</div>
<ul>
<li>
@ -1646,7 +1696,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
<span>
Used in:
</span>
Training and workforce development
Training and workforce development methodology
</li>
</ul>
</div>
@ -1657,7 +1707,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Unemployment
</h3>
<div>
Number of unemployed people as a percentage of the civilian labor force
Number of unemployed people as a percentage of the civilian labor force
</div>
<ul>
<li>
@ -1682,7 +1734,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
<span>
Used in:
</span>
Training and workforce development
Training and workforce development methodology
</li>
</ul>
</div>
@ -1693,7 +1745,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
Poverty
</h3>
<div>
Percent of a tract's population in households where the household income is at or below 100% of the federal poverty level.
Percent of a tract's population in households where the household income is at or below 100% of
the federal poverty level.
</div>
<ul>
<li>
@ -1718,7 +1773,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
<span>
Used in:
</span>
Training and workforce development
Training and workforce development methodology
</li>
</ul>
</div>
@ -1729,8 +1784,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
High school degree achievement rate
</h3>
<div>
Percent of people ages 25 years or older in a census tract whose
education level is less than a high school diploma.
Percent (not percentile) of people ages 25 years or older in a census tract whose
education level is less than a high school diploma.
</div>
<ul>
<li>
@ -1755,7 +1812,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
<span>
Used in:
</span>
Training and workforce development
Training and workforce development methodology
</li>
</ul>
</div>

6
client/src/styles/global.scss
View file

@ -293,6 +293,12 @@ This section will outline styles that are component specific
}
}
// This is to allow the first child in the accordions which is the header showing INDICATOR and
// PERCENTILE (0-100) to push into the container's margin to match mock
.usa-accordion__content > *:first-child {
margin: -10px -20px 10px -20px;
}
/*
***************************************
* TIMELINE / PROCESS LIST STYLES

15
data/data-pipeline/data_pipeline/etl/score/etl_score_post.py
View file

@ -3,6 +3,7 @@ import json
import pandas as pd
from data_pipeline.etl.base import ExtractTransformLoad
from data_pipeline.etl.score.etl_utils import floor_series
from data_pipeline.utils import get_module_logger, zip_files
from data_pipeline.score import field_names
@ -207,13 +208,15 @@ class PostScoreETL(ExtractTransformLoad):
# filter the columns on full score
score_tiles = score_county_state_merged_df[tiles_score_column_titles]
# round decimals
decimals = pd.Series(
[constants.TILES_ROUND_NUM_DECIMALS]
* len(constants.TILES_SCORE_FLOAT_COLUMNS),
index=constants.TILES_SCORE_FLOAT_COLUMNS,
score_tiles[constants.TILES_SCORE_FLOAT_COLUMNS] = score_tiles[
constants.TILES_SCORE_FLOAT_COLUMNS
].apply(
func=lambda series: floor_series(
series=series,
number_of_decimals=constants.TILES_ROUND_NUM_DECIMALS,
),
axis=0,
)
score_tiles = score_tiles.round(decimals)
# create indexes
score_tiles = score_tiles.rename(

60
data/data-pipeline/data_pipeline/etl/score/etl_utils.py
View file

@ -1,6 +1,9 @@
import os
import sys
from pathlib import Path
import numpy as np
import pandas as pd
from data_pipeline.config import settings
from data_pipeline.utils import (
@ -48,3 +51,60 @@ def check_score_data_source(
"No local score tiles data found. Please use '-d aws` to fetch from AWS"
)
sys.exit()
def floor_series(series: pd.Series, number_of_decimals: int) -> pd.Series:
"""Floors all non-null numerical values to a specific number of decimal points
Args:
series (pd.Series): Input pandas series
number_of_decimals (int): Number of decimal points to floor all numerical values to
Returns:
floored_series (pd.Series): A Pandas Series of numerical values with appropriate number of decimal points
"""
# we perform many operations using the division operator
# as well as elementwise multiplication. The result of such
# operations can introduce such values, below, due to numerical
# instability. This results in unsafe type inference for numpy
# float types - exacerbated by panda's type inference engine.
# Hence, to handle such offending values we default to None
# Please see the reference, below, on nullable integer types for more details
unacceptable_values = [-np.inf, np.inf, "None", np.nan]
mapping = {
unacceptable_value: None for unacceptable_value in unacceptable_values
}
# ensure we are working with a numpy array (which is really what a pandas series is)
if not isinstance(series, pd.Series):
raise TypeError(
f"Argument series must be of type pandas series, not of type {type(series).__name__}."
)
# raise exception for handling empty series
if series.empty:
raise ValueError("Empty series provided.")
# if we have any values, just replace them with None
if series.isin(unacceptable_values).any():
series.replace(mapping, regex=False, inplace=True)
multiplication_factor = 10 ** number_of_decimals
# In order to safely cast NaNs
# First coerce series to float type: series.astype(float)
# Please see here:
# https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html#nullable-integer-data-type
product_for_numerator = np.floor(
series.astype(float) * multiplication_factor
)
floored_series = np.where(
series.isnull(),
# For all null values default to null
None,
# The other default condition - floor non-null values
product_for_numerator / multiplication_factor,
)
return floored_series

BIN
data/data-pipeline/data_pipeline/etl/score/tests/snapshots/tile_data_expected.pkl
View file

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72
data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py Normal file
View file

@ -0,0 +1,72 @@
import pandas as pd
import numpy as np
import pytest
from data_pipeline.etl.score.etl_utils import floor_series
def test_floor_series():
# test examples
series = pd.Series(data=[None, 1, 0.324534, 1.2341], dtype="float64")
series_exponentiated = pd.Series(
data=[
-np.inf,
np.inf,
"None",
-0.131321313123123,
5.62322441e-15,
1.2341123131313131312e12,
]
)
series_of_nan_values = pd.Series(data=[None, None, None, None, None])
series_empty = pd.Series(data=[], dtype="float64")
# list of randomly generated values
invalid_type = list(np.random.uniform(1, 1000000, size=15))
floored_series_1 = floor_series(series, number_of_decimals=2)
floored_series_2 = floor_series(series, number_of_decimals=3)
floored_series_3 = floor_series(series, number_of_decimals=1)
floored_series_4 = floor_series(series_of_nan_values, number_of_decimals=10)
floored_series_5 = floor_series(series_exponentiated, number_of_decimals=1)
# expected fixtures
expected_1 = np.array([None, 1.0, 0.32, 1.23])
expected_2 = np.array([None, 1.00, 0.324, 1.234])
expected_3 = np.array([None, 1.0, 0.3, 1.2])
expected_4 = np.array([None, None, None, None, None])
expected_5 = np.array([None, None, None, -0.2, 0.0, 1234112313131.3])
# Test for expected value with 2 decimal places
# Elewentwise comparison to ensure all values are equal
all_elements_are_equal_one = np.equal(expected_1, floored_series_1)
assert all_elements_are_equal_one.all()
# Test for expected value with 3 decimal places
# Elewentwise comparison to ensure all values are equal
all_elements_are_equal_two = np.equal(expected_2, floored_series_2)
assert all_elements_are_equal_two.all()
# Test for expected value with 1 decimal place
# Elewentwise comparison to ensure all values are equal
all_elements_are_equal_three = np.equal(expected_3, floored_series_3)
assert all_elements_are_equal_three.all()
# Test for expected value for some arbitrary decimal place
# Elewentwise comparison to ensure all values are equal for NaN
all_elements_are_equal_four = np.equal(expected_4, floored_series_4)
assert all_elements_are_equal_four.all()
# Test for expected value for some arbitrary decimal place
# Elewentwise comparison to ensure all floating point imprecision
# is clamped to a certain number of decimal points
all_elements_are_equal_five = np.equal(expected_5, floored_series_5)
assert all_elements_are_equal_five.all()
# Test for empty series - should raise a ValueError exception
with pytest.raises(ValueError, match="Empty series provided."):
floor_series(series_empty, number_of_decimals=2)
# Test for invalid type - should raise a TypeError exception
with pytest.raises(
TypeError,
match="Argument series must be of type pandas series, not of type list.",
):
floor_series(invalid_type, number_of_decimals=3)

10
data/data-pipeline/data_pipeline/etl/sources/energy_definition_alternative_draft/etl.py
View file

@ -95,9 +95,13 @@ class EnergyDefinitionAlternativeDraft(ExtractTransformLoad):
)
# Convert to boolean:
self.df[field_names.ENERGY_RELATED_COMMUNITIES_DEFINITION_ALTERNATIVE] = \
self.df[field_names.ENERGY_RELATED_COMMUNITIES_DEFINITION_ALTERNATIVE
].astype('bool')
self.df[
field_names.ENERGY_RELATED_COMMUNITIES_DEFINITION_ALTERNATIVE
] = self.df[
field_names.ENERGY_RELATED_COMMUNITIES_DEFINITION_ALTERNATIVE
].astype(
"bool"
)
def validate(self) -> None:
logger.info("Validating data")

104
data/data-pipeline/requirements.txt
View file

@ -1,48 +1,39 @@
appnope==0.1.2; sys_platform == "darwin" and python_version >= "3.7" and platform_system == "Darwin"
argcomplete==1.12.3; python_version < "3.8.0" and python_version >= "3.7"
argon2-cffi==21.1.0; python_version >= "3.6"
astroid==2.8.0; python_version >= "3.6" and python_version < "4.0"
atomicwrites==1.4.0; python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.4.0"
attrs==21.2.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
astroid==2.8.3; python_version >= "3.6" and python_version < "4.0"
attrs==21.2.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
backcall==0.2.0; python_version >= "3.7"
backports.entry-points-selectable==1.1.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "2.7"
black==21.9b0; python_full_version >= "3.6.2"
bleach==4.1.0; python_version >= "3.7"
censusdata==1.15; python_version >= "2.7"
certifi==2021.5.30; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
cffi==1.14.6; implementation_name == "pypy" and python_version >= "3.6"
charset-normalizer==2.0.6; python_full_version >= "3.6.0" and python_version >= "3"
certifi==2021.10.8; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
cffi==1.15.0; implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.6.1"
charset-normalizer==2.0.7; python_full_version >= "3.6.0" and python_version >= "3"
click-plugins==1.1.1; python_version >= "3.6"
click==8.0.1; python_version >= "3.6"
click==8.0.3; python_version >= "3.6"
cligj==0.7.2; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version < "4" and python_version >= "3.6"
colorama==0.4.4; platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.6.2" and sys_platform == "win32" and python_version < "4.0" and (python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.5.0") and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0")
configparser==5.0.2; python_version >= "3.6"
colorama==0.4.4; python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" and sys_platform == "win32" and python_version < "4.0" or platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.5.0" and sys_platform == "win32" and python_version < "4.0"
cycler==0.10.0; python_version >= "3.7"
debugpy==1.4.3; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
debugpy==1.5.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
decorator==5.1.0; python_version >= "3.7"
defusedxml==0.7.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
distlib==0.3.2; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
dparse==0.5.1; python_version >= "3.5"
dynaconf==3.1.7; python_version >= "3.7"
entrypoints==0.3; python_full_version >= "3.6.1" and python_version >= "3.7"
et-xmlfile==1.1.0; python_version >= "3.6"
filelock==3.0.12; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
fiona==1.8.20; python_version >= "3.6"
flake8==3.9.2; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0")
geopandas==0.9.0; python_version >= "3.6"
idna==3.2; python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5"
importlib-metadata==4.8.1; python_version == "3.7" and (python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "3.8" or python_full_version >= "3.5.0" and python_version < "3.8" and python_version >= "3.6") and python_full_version >= "3.6.2"
iniconfig==1.1.1; python_version >= "3.6"
ipykernel==6.4.1; python_version >= "3.7"
idna==3.3; python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5"
importlib-metadata==4.8.1; python_version == "3.7"
ipdb==0.13.9; python_version >= "2.7"
ipykernel==6.4.2; python_version >= "3.7"
ipython-genutils==0.2.0; python_version >= "3.7"
ipython==7.27.0; python_version >= "3.7"
ipython==7.28.0; python_version >= "3.7"
ipywidgets==7.6.5
isort==5.9.3; python_full_version >= "3.6.1" and python_version < "4.0" and python_version >= "3.6"
jedi==0.18.0; python_version >= "3.7"
jellyfish==0.6.1
jinja2==3.0.1; python_version >= "3.7"
jsonschema==3.2.0; python_version >= "3.5"
jupyter-client==7.0.3; python_full_version >= "3.6.1" and python_version >= "3.7"
jinja2==3.0.2; python_version >= "3.7"
jsonschema==4.1.2; python_version >= "3.7"
jupyter-client==7.0.6; python_full_version >= "3.6.1" and python_version >= "3.7"
jupyter-console==6.4.0; python_version >= "3.6"
jupyter-contrib-core==0.3.3
jupyter-contrib-nbextensions==0.5.1
@ -55,79 +46,64 @@ jupyterlab-pygments==0.1.2; python_version >= "3.7"
jupyterlab-widgets==1.0.2; python_version >= "3.6"
kiwisolver==1.3.2; python_version >= "3.7"
lazy-object-proxy==1.6.0; python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.6" and python_version < "4.0" and python_full_version >= "3.6.0"
liccheck==0.6.2; python_version >= "2.7"
lxml==4.6.3; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
lxml==4.6.5; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
markupsafe==2.0.1; python_version >= "3.7"
matplotlib-inline==0.1.3; python_version >= "3.7"
matplotlib==3.4.3; python_version >= "3.7"
mccabe==0.6.1; python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.6" and python_version < "4.0" and python_full_version >= "3.5.0"
mccabe==0.6.1; python_version >= "3.6" and python_version < "4.0"
mistune==0.8.4; python_version >= "3.7"
munch==2.5.0; python_version >= "3.6"
mypy-extensions==0.4.3; python_full_version >= "3.6.2" and python_version >= "3.5"
mypy==0.910; python_version >= "3.5"
nbclient==0.5.4; python_full_version >= "3.6.1" and python_version >= "3.7"
nbconvert==6.1.0; python_version >= "3.7"
nbconvert==6.2.0; python_version >= "3.7"
nbformat==5.1.3; python_full_version >= "3.6.1" and python_version >= "3.7"
nest-asyncio==1.5.1; python_full_version >= "3.6.1" and python_version >= "3.7"
notebook==6.4.4; python_version >= "3.6"
notebook==6.4.5; python_version >= "3.6"
numpy==1.21.1; python_version >= "3.7"
openpyxl==3.0.7; python_version >= "3.6"
packaging==21.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
pandas==1.3.3; python_full_version >= "3.7.1"
packaging==21.0; python_version >= "3.7"
pandas==1.3.4; python_full_version >= "3.7.1"
pandocfilters==1.5.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
parso==0.8.2; python_version >= "3.7"
pathspec==0.9.0; python_full_version >= "3.6.2"
pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.7"
pickleshare==0.7.5; python_version >= "3.7"
pillow==8.3.2; python_version >= "3.7"
platformdirs==2.3.0; python_version >= "3.6" and python_full_version >= "3.6.2" and python_version < "4.0" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6")
pluggy==1.0.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
pillow==8.4.0; python_version >= "3.7"
platformdirs==2.4.0; python_version >= "3.6" and python_version < "4.0"
prometheus-client==0.11.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
prompt-toolkit==3.0.20; python_full_version >= "3.6.2" and python_version >= "3.7"
prompt-toolkit==3.0.21; python_full_version >= "3.6.2" and python_version >= "3.7"
ptyprocess==0.7.0; sys_platform != "win32" and python_version >= "3.7" and os_name != "nt"
py==1.10.0; python_version >= "3.6" and python_full_version < "3.0.0" and implementation_name == "pypy" or python_full_version >= "3.5.0" and python_version >= "3.6" and implementation_name == "pypy"
pycodestyle==2.7.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
pycparser==2.20; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
pyflakes==2.3.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
py==1.10.0; implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.6.1"
pycparser==2.20; implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.6.1"
pygments==2.10.0; python_version >= "3.7"
pylint==2.11.1; python_version >= "3.6" and python_version < "4.0"
pypandoc==1.6.4
pyparsing==2.4.7; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7"
pyproj==3.2.1; python_version >= "3.7"
pyrsistent==0.18.0; python_version >= "3.6"
pytest-mock==3.6.1; python_version >= "3.6"
pytest==6.2.5; python_version >= "3.6"
pyrsistent==0.18.0; python_version >= "3.7"
python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.7"
pytz==2021.1; python_full_version >= "3.7.1" and python_version >= "2.7"
pywin32==301; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.6"
pytz==2021.3; python_full_version >= "3.7.1" and python_version >= "3.6"
pywin32==302; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.7"
pywinpty==1.1.4; os_name == "nt" and python_version >= "3.6"
pyyaml==5.4.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0"
pyyaml==6.0; python_version >= "3.6"
pyzmq==22.3.0; python_full_version >= "3.6.1" and python_version >= "3.7"
qtconsole==5.1.1; python_version >= "3.6"
qtpy==1.11.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6"
regex==2021.8.28; python_full_version >= "3.6.2"
qtpy==1.11.2; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6"
requests==2.26.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0")
safety==1.10.3; python_version >= "3.5"
semantic-version==2.8.5; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "2.7"
send2trash==1.8.0; python_version >= "3.6"
shapely==1.7.1; python_version >= "3.6"
six==1.16.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.7" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7")
terminado==0.12.1; python_version >= "3.6"
testpath==0.5.0; python_version >= "3.7"
toml==0.10.2; python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.5.0" and python_version >= "3.6" and python_version < "4.0"
tomli==1.2.1; python_version >= "3.6" and python_full_version >= "3.6.2"
toml==0.10.2; python_version > "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.3.0" and python_version > "3.6" and python_version < "4.0"
tornado==6.1; python_full_version >= "3.6.1" and python_version >= "3.7"
tox==3.24.4; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0")
tqdm==4.62.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0")
traitlets==5.1.0; python_full_version >= "3.6.1" and python_version >= "3.7"
types-requests==2.25.8
typing-extensions==3.10.0.2; python_version < "3.8" and python_version >= "3.6"
urllib3==1.26.6; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "2.7"
typed-ast==1.4.3; implementation_name == "cpython" and python_version < "3.8" and python_version >= "3.6"
types-requests==2.25.11
typing-extensions==3.10.0.2; python_version >= "3.6" and python_version < "3.8"
urllib3==1.26.7; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "2.7"
us==2.0.2
virtualenv==20.8.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
wcwidth==0.2.5; python_full_version >= "3.6.2" and python_version >= "3.7"
webencodings==0.5.1; python_version >= "3.7"
widgetsnbextension==3.5.1
wrapt==1.12.1; python_version >= "3.6" and python_version < "4.0"
wrapt==1.13.2; python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.6" and python_version < "4.0" and python_full_version >= "3.5.0"
xlsxwriter==2.0.0
zipp==3.5.0; python_version < "3.8" and python_version >= "3.6"
zipp==3.6.0; python_version < "3.8" and python_version >= "3.6"

19
data/data-pipeline/tox.ini
View file

@ -7,19 +7,32 @@ skip_missing_interpreters = true
[testenv:lint]
# lints python code in src and tests
basepython = python3.9
deps = -rrequirements.txt
# These are "external" because they are dev dependencies that not in
# "requirements.txt" and therefore not installed in this env, but they will be
# available where this is being run. See:
# https://stackoverflow.com/questions/47642747/tox-warningtest-command-found-but-not-installed-in-testenv
allowlist_externals = black
flake8
pylint
commands = black data_pipeline
flake8 data_pipeline
pylint data_pipeline
# Ignore tests this lint check because test dependencies are not installed here.
pylint data_pipeline --ignore tests
[testenv:checkdeps]
# checks the dependencies for security vulnerabilities and open source licenses
deps = -rrequirements.txt
# These are "external" because they are dev dependencies that not in
# "requirements.txt" and therefore not installed in this env, but they will be
# available where this is being run. See:
# https://stackoverflow.com/questions/47642747/tox-warningtest-command-found-but-not-installed-in-testenv
allowlist_externals = safety
liccheck
commands = safety check
liccheck
[testenv:pytest]
# Run tests
deps = pytest
commands = pytest
commands = pytest

1
docs/decisions/0002-mapping-visualization-library.md
View file

@ -108,6 +108,7 @@ We provide more detail on these factors below.
- **Popularity** : According to [NPM Trends](https://www.npmtrends.com/mapbox-gl-vs-leaflet-vs-ol-vs-arcgis-js-api-vs-maplibre-gl) Mapbox-GL is the most downloaded package among those considered. More info [here](https://www.geoapify.com/map-libraries-comparison-leaflet-vs-mapbox-gl-vs-openlayers-trends-and-statistics)
![Download Stats](./0002-files/MapDownloadCount.png)
<!-- markdown-link-check-disable -->
#### Mapbox-GL JS Cons
- **Licensing** : Mapbox's December 2020 [announcement](https://github.com/mapbox/mapbox-gl-js/releases/tag/v2.0.0) of version 2.0 of their software changed ther license to proprietary and changed their pricing scheme to cover tiles loaded from outside of their service. This decision was met with [some criticism](https://joemorrison.medium.com/death-of-an-open-source-business-model-62bc227a7e9b) in the open-source mapping community.

6
docs/decisions/0004-client-side-framework.md
View file

@ -49,11 +49,11 @@ Chosen option: Gatsby, because it seems to hit the balance between being simple
- Fairly good [documentation](https://www.gatsbyjs.com/docs)
### Negative Consequences <!-- optional -->
<!-- markdown-link-check-disable -->
- Test development environment ran out of memory. We debugged this further and could not replicate the problem on a new machine, but the original experience was annoying to work around and this [article](https://support.gatsbyjs.com/hc/en-us/articles/360053096273-Why-did-I-hit-Out-of-Memory-errors-) suggests it could be a more widespread problem.
- Local builds and refreshes feel slow compared to other frameworks on this list
- Seems a little more geared toward the blog usecase
<!-- markdown-link-check-enable -->
## Pros and Cons of the Options
### Jekyll
@ -75,7 +75,7 @@ Cons:
Pros:
- By far the most commonly framework in this list - 1.4MM [total downloads](https://www.npmtrends.com/gatsby-vs-next-vs-nuxt-vs-vuepress-vs-create-react-app-vs-gridsome) as of May 2, 2021 . Gatsby, the second most-downloaded, has ~470,000
- By far the most commonly framework in this list - 1.4MM total downloads as of May 2, 2021 . Gatsby, the second most-downloaded, has ~470,000
- Used by a number of well-known [companies and brands](https://nextjs.org/showcase)
- Flexible
- Mature tooling like `create-next-app`

3
docs/operations/README.md Normal file
View file

@ -0,0 +1,3 @@
# Operations Guides
This directory should have guides for operating any services that are being supported by the Justice40 team.

194
docs/operations/rollback-plan.md Normal file
View file

@ -0,0 +1,194 @@
# Rollback Plan
> Note: This guide is up to date as of [this commit](https://github.com/usds/justice40-tool/commit/0d57dd572be027a2fc8b1625958ed68c4b900653), 12/16/2021. If you want to sanity check that this guide is still relevant, go to [Rollback Details](#rollback-details).
## When To Rollback?
If for some reason the final map data that has been generated by the pipeline
has become incorrect or is missing, this page documents the emergency steps to
get the data back to a known good state.
## Rollback Theory
The theory of rollback depends on two things:
1. The s3 bucket containing our data uses [s3 bucket versioning](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Versioning.html), allowing us to revert specific files back to a previous version.
2. The [Combine and Tileify step](https://github.com/usds/justice40-tool/blob/main/.github/workflows/combine-tilefy.yml) consumes only two files in s3 as input, making the strategy of reverting those files to a previous version a feasible way to run this job against a previous version.
If you feel confident in this and want to do the rollback now, proceed to [Rollback Steps](#rollback-steps).
If you want to understand more deeply what's going on, and sanity check that the
code hasn't changed since this guide was written, go to [Rollback Details](#rollback-details).
## Rollback Steps
### 1. List Object Versions
```shell
aws s3api list-object-versions \
--bucket justice40-data \
--prefix data-sources/census.zip \
--query "Versions[*].{Key:Key,VersionId:VersionId,LastModified:LastModified}"
```
You should get something like:
```
[
...
{
"Key": "data-sources/census.zip",
"VersionId": "<version_id>",
"LastModified": "2021-11-29T18:57:40+00:00"
}
...
]
```
Do the same thing with the score file:
```shell
aws s3api list-object-versions \
--bucket justice40-data \
--prefix data-pipeline/data/score/csv/tiles/usa.csv \
--query "Versions[*].{Key:Key,VersionId:VersionId,LastModified:LastModified}"
```
### 2. Download Previous Version
Based on the output from the commands above, select the `<version_id>` of the known good version of that file, and run the following command to download it:
```shell
aws s3api get-object \
--bucket justice40-data \
--key data-sources/census.zip \
--version-id <version_id> \
census.zip
```
Do the same for the score file:
```shell
aws s3api get-object \
--bucket justice40-data \
--key data-pipeline/data/score/csv/tiles/usa.csv \
--version-id <version_id> \
usa.csv
```
> Note: This command doesn't give you feedback like `curl` does on download progress, it just sits there. To verify the download is happening, open another terminal and check the output file size.
### 3. Upload New Version
After you've verified that the local files are correct, you can overwrite the
live versions by running a normal s3 copy:
```shell
aws s3 cp census.zip s3://justice40-data/data-sources/census.zip
aws s3 cp usa.csv s3://justice40-data/data-pipeline/data/score/csv/tiles/usa.csv
```
### 4. Rerun Combine and Tileify
Run the [Combine and Tileify Github Action](https://github.com/usds/justice40-tool/actions/workflows/combine-tilefy.yml) to regenerate the map tiles from the data you just rolled back.
## Rollback Details
> Note: The links to the relevant code are included alongside the relevant snippets as a defense against this page becoming outdated. Make sure the code matches what the links point to, and verify that things still work as this guide assumes.
The relevant step that consumes the files from s3 should be in the [Combine and Tileify Job](https://github.com/usds/justice40-tool/blob/main/.github/workflows/combine-tilefy.yml#L56-L59):
```yaml
- name: Run Scripts
run: |
poetry run python3 data_pipeline/application.py geo-score -s aws
poetry run python3 data_pipeline/application.py generate-map-tiles
```
This runs the [`score_geo` task](https://github.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/application.py#L166):
```python
score_geo(data_source=data_source)
```
This uses the [`GeoScoreETL` object to run the etl](https://github.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/etl/runner.py#L121-L124):
```python
score_geo = GeoScoreETL(data_source=data_source)
score_geo.extract()
score_geo.transform()
score_geo.load()
```
In the extract step, `GeoScoreETL` calls [`check_census_data` and `check_score_data`](https://github.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/etl/score/etl_score_geo.py#L57-L67):
```python
# check census data
check_census_data_source(
census_data_path=self.DATA_PATH / "census",
census_data_source=self.DATA_SOURCE,
)
# check score data
check_score_data_source(
score_csv_data_path=self.SCORE_CSV_PATH,
score_data_source=self.DATA_SOURCE,
)
```
The `check_score_data_source` function downloads [one file from s3](https://github.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/etl/score/etl_utils.py#L32-L43):
```python
TILE_SCORE_CSV_S3_URL = (
settings.AWS_JUSTICE40_DATAPIPELINE_URL
+ "/data/score/csv/tiles/usa.csv"
)
TILE_SCORE_CSV = score_csv_data_path / "tiles" / "usa.csv"
# download from s3 if census_data_source is aws
if score_data_source == "aws":
logger.info("Fetching Score Tile data from AWS S3")
download_file_from_url(
file_url=TILE_SCORE_CSV_S3_URL, download_file_name=TILE_SCORE_CSV
)
```
This can be found here:
```
% aws s3 ls s3://justice40-data/data-pipeline/data/score/csv/tiles/usa.csv
2021-12-13 15:23:49 27845542 usa.csv
% curl --head https://justice40-data.s3.amazonaws.com/data-pipeline/data/score/csv/tiles/usa.csv
HTTP/1.1 200 OK
...
```
The `check_census_data_source` function downloads [one file from s3](https://github.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/etl/sources/census/etl_utils.py#L99-L109):
```python
CENSUS_DATA_S3_URL = settings.AWS_JUSTICE40_DATASOURCES_URL + "/census.zip"
DATA_PATH = settings.APP_ROOT / "data"
# download from s3 if census_data_source is aws
if census_data_source == "aws":
logger.info("Fetching Census data from AWS S3")
unzip_file_from_url(
CENSUS_DATA_S3_URL,
DATA_PATH / "tmp",
DATA_PATH,
)
```
This can be found here:
```
% aws s3 ls s3://justice40-data/data-sources/census.zip
2021-11-29 13:57:40 845390373 census.zip
% curl --head https://justice40-data.s3.amazonaws.com/data-sources/census.zip
HTTP/1.1 200 OK
...
```
So this is how to see that the pipeline needs those two files. You can also confirm this in the [Combine and Tileify Github Action logs](https://github.com/usds/justice40-tool/actions/workflows/combine-tilefy.yml).
If you feel confident in this and want to do the rollback now, proceed to [Rollback Steps](#rollback-steps).

3
mlc_config.json
View file

@ -14,6 +14,9 @@
},
{
"pattern": "^https://github.com/usds/environmental-justice/"
},
{
"pattern": "^https://.*medium.com"
}
],
"replacementPatterns": [