diff --git a/INSTALLATION-es.md b/INSTALLATION-es.md index b4365aa3..9403ea2b 100644 --- a/INSTALLATION-es.md +++ b/INSTALLATION-es.md @@ -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 - + 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 diff --git a/client/README.md b/client/README.md index 84eee920..2f7497bd 100644 --- a/client/README.md +++ b/client/README.md @@ -19,6 +19,7 @@ This README contains the following content: #### Install Node using NVM + 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 + - 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. diff --git a/client/src/components/AreaDetail/areaDetail.module.scss b/client/src/components/AreaDetail/areaDetail.module.scss index 9e319643..03f644da 100644 --- a/client/src/components/AreaDetail/areaDetail.module.scss +++ b/client/src/components/AreaDetail/areaDetail.module.scss @@ -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); +} \ No newline at end of file diff --git a/client/src/components/AreaDetail/areaDetail.module.scss.d.ts b/client/src/components/AreaDetail/areaDetail.module.scss.d.ts index ad2fc967..03e161a5 100644 --- a/client/src/components/AreaDetail/areaDetail.module.scss.d.ts +++ b/client/src/components/AreaDetail/areaDetail.module.scss.d.ts @@ -11,6 +11,7 @@ declare namespace MapModuleScssNamespace { versionInfo: string; showThresholdExceed:string; hideThresholdExceed:string; + categoryHeader:string; } } diff --git a/client/src/components/AreaDetail/index.tsx b/client/src/components/AreaDetail/index.tsx index b017b6bd..9a91c02d 100644 --- a/client/src/components/AreaDetail/index.tsx +++ b/client/src/components/AreaDetail/index.tsx @@ -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: , content: ( <> + {/* Category Header */} +
+
{intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.INDICATOR)}
+
{intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.PERCENTILE)}
+
+ + {/* Category Indicators */} {category.indicators.map((indicator:any, index:number) => { return ; })} diff --git a/client/src/components/AreaDetail/tests/__snapshots__/areaDetail.test.tsx.snap b/client/src/components/AreaDetail/tests/__snapshots__/areaDetail.test.tsx.snap index 40d5ac81..55b7b218 100644 --- a/client/src/components/AreaDetail/tests/__snapshots__/areaDetail.test.tsx.snap +++ b/client/src/components/AreaDetail/tests/__snapshots__/areaDetail.test.tsx.snap @@ -87,6 +87,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="climate-change" > +
+
+ Indicator +
+
+ Percentile (0-100) +
+
  • @@ -190,6 +198,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="clean-energy" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -273,6 +289,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="clean-transport" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -356,6 +380,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="sustain-house" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -441,6 +473,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="leg-pollute" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -542,6 +582,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="clean-water" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -607,6 +655,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="health-burdens" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -731,6 +787,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="work-dev" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -822,15 +886,16 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 High school degree achievement rate
    - 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
    N/A - - - + + % +
    • diff --git a/client/src/components/BetaBanner/__snapshots__/BetaBanner.test.tsx.snap b/client/src/components/BetaBanner/__snapshots__/BetaBanner.test.tsx.snap index a688be38..cbc3f79f 100644 --- a/client/src/components/BetaBanner/__snapshots__/BetaBanner.test.tsx.snap +++ b/client/src/components/BetaBanner/__snapshots__/BetaBanner.test.tsx.snap @@ -10,8 +10,8 @@ exports[`rendering of the BetaBanner checks if component renders 1`] = ` This is a beta site. - 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. diff --git a/client/src/components/Categories/__snapshots__/Categories.test.tsx.snap b/client/src/components/Categories/__snapshots__/Categories.test.tsx.snap index 50a9511f..167f5aa7 100644 --- a/client/src/components/Categories/__snapshots__/Categories.test.tsx.snap +++ b/client/src/components/Categories/__snapshots__/Categories.test.tsx.snap @@ -176,7 +176,7 @@ exports[`rendering of the Categories checks if component renders 1`] = ` low median home value 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 diff --git a/client/src/components/DatasetCard/index.tsx b/client/src/components/DatasetCard/index.tsx index f20223a5..52c14dfa 100644 --- a/client/src/components/DatasetCard/index.tsx +++ b/client/src/components/DatasetCard/index.tsx @@ -25,9 +25,7 @@ const DatasetCard = ({datasetCardProps}:IDatasetCardProps) => { {intl.formatMessage(METHODOLOGY_COPY.DATASET_CARD_LABELS.RESP_PARTY)} - - {datasetCardProps.respPartyLabel} - + {datasetCardProps.responsibleParty}
  • diff --git a/client/src/components/DatasetCard/tests/__snapshots__/datasetCard.test.tsx.snap b/client/src/components/DatasetCard/tests/__snapshots__/datasetCard.test.tsx.snap index 2006c358..cc95578d 100644 --- a/client/src/components/DatasetCard/tests/__snapshots__/datasetCard.test.tsx.snap +++ b/client/src/components/DatasetCard/tests/__snapshots__/datasetCard.test.tsx.snap @@ -10,9 +10,9 @@ exports[`rendering of indicator dataset card checks if component renders 1`] = `
    - 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. +
    • @@ -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
    • diff --git a/client/src/components/DatasetContainer/tests/__snapshots__/datasetContainer.test.tsx.snap b/client/src/components/DatasetContainer/tests/__snapshots__/datasetContainer.test.tsx.snap index 0ce9f9f5..d3e85ea9 100644 --- a/client/src/components/DatasetContainer/tests/__snapshots__/datasetContainer.test.tsx.snap +++ b/client/src/components/DatasetContainer/tests/__snapshots__/datasetContainer.test.tsx.snap @@ -40,9 +40,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
      - 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. +
      • @@ -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
      • @@ -79,12 +79,12 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
        - 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. +
        • @@ -121,12 +121,12 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
          - 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. +
          • @@ -163,19 +163,18 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
            - 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. +
            • @@ -247,8 +246,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis PM2.5 in the air
              - 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. +
              • @@ -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 +
              • @@ -285,7 +288,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Diesel particulate matter exposure
                - Mixture of particles that is part of diesel exhaust in the air. + + Mixture of particles that is part of diesel exhaust in the air. +
                • @@ -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 +
                • @@ -322,8 +329,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Traffic proximity and volume
                  - 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). +
                  • @@ -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 +
                  • @@ -360,9 +371,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                    - 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. +
                    • @@ -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 +
                    • @@ -400,8 +413,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                      - 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 +
                      • @@ -437,7 +451,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Low median home value
                        - Median home value of owner-occupied housing units in the census tract. + + Median home value of owner-occupied housing units in the census tract. +
                        • @@ -474,9 +490,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                          - 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. +
                          • @@ -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 +
                          • Date range: - 2015-2020 + 2020
                          • @@ -516,8 +532,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                            - 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. +
                            • @@ -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 +
                            • @@ -554,8 +573,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                              - 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. +
                              • @@ -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 +
                              • @@ -580,7 +602,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Affordable and sustainable housing methodology + Reduction and remediation of legacy pollution methodology
                              @@ -591,8 +613,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Wastewater discharge
                              - 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). +
                              • @@ -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 +
                              • @@ -629,10 +655,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Asthma
                                - 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?” +
                                • @@ -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 +
                                • @@ -668,9 +697,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Diabetes
                                  - 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. +
                                  • @@ -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 +
                                  • @@ -706,9 +739,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis 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. + + 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. +
                                    • @@ -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 +
                                    • @@ -745,13 +782,22 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                      - 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. + +

                                      + + 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. + +

                                      + +
                                      • @@ -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) +
                                      • @@ -787,7 +835,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Low median Income
                                        - 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. +
                                        • @@ -812,7 +862,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                        @@ -824,8 +874,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                        - 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. +
                                        • @@ -850,7 +900,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                        @@ -861,7 +911,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Unemployment
                                        - Number of unemployed people as a percentage of the civilian labor force + + Number of unemployed people as a percentage of the civilian labor force +
                                        • @@ -886,7 +938,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                        @@ -897,7 +949,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Poverty
                                        - 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. +
                                        • @@ -922,7 +977,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                        @@ -933,8 +988,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis High school degree achievement rate
                                        - 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. +
                                        • @@ -959,7 +1016,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                        diff --git a/client/src/components/Indicator/Indicator.tsx b/client/src/components/Indicator/Indicator.tsx index e19e6c9c..8d397196 100644 --- a/client/src/components/Indicator/Indicator.tsx +++ b/client/src/components/Indicator/Indicator.tsx @@ -43,9 +43,12 @@ const Indicator = ({indicator}:IIndicator) => {
                                        {readablePercentile(indicator.value)} - - {getSuperscriptOrdinal(readablePercentile(indicator.value))} - + {indicator.isPercent ? + {`%`} : + + {getSuperscriptOrdinal(readablePercentile(indicator.value))} + + }
                                        • diff --git a/client/src/components/J40Header/__snapshots__/J40Header.test.tsx.snap b/client/src/components/J40Header/__snapshots__/J40Header.test.tsx.snap index 3c9912ca..7e8d5369 100644 --- a/client/src/components/J40Header/__snapshots__/J40Header.test.tsx.snap +++ b/client/src/components/J40Header/__snapshots__/J40Header.test.tsx.snap @@ -150,8 +150,8 @@ exports[`rendering of the J40Header checks if component renders 1`] = ` This is a beta site. - 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. diff --git a/client/src/data/copy/common.tsx b/client/src/data/copy/common.tsx index 689c8584..48d3fa2d 100644 --- a/client/src/data/copy/common.tsx +++ b/client/src/data/copy/common.tsx @@ -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', }, }); diff --git a/client/src/data/copy/explore.tsx b/client/src/data/copy/explore.tsx index ccb380de..e55b1ff4 100644 --- a/client/src/data/copy/explore.tsx +++ b/client/src/data/copy/explore.tsx @@ -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: , + }, CLIMATE_CHANGE: { + METHODOLOGY: , TITLE: , }, CLEAN_ENERGY: { + METHODOLOGY: , TITLE: , }, CLEAN_TRANSPORT: { + METHODOLOGY: , TITLE: , }, AFFORDABLE_HOUSING: { + METHODOLOGY: , TITLE: , }, LEGACY_POLLUTION: { + METHODOLOGY: , TITLE: , }, CLEAN_WATER: { + METHODOLOGY: , TITLE: , }, HEALTH_BURDENS: { + METHODOLOGY: , TITLE: , }, WORKFORCE_DEV: { + METHODOLOGY: , TITLE: + {`Census's American Community Survey`} + , + }} + />, + FEMA: + {`Federal Emergency Management Agency (FEMA)`} + , + }} + />, + DOE: + {`Department of Energy (DOE) LEAD Score`} + , + }} + />, + EPA_OAR: + {` + 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 + `} + , + }} + />, + EPA_NATA: + {` + Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA) + as compiled by EPA's EJSCREEN + `} + , + }} + />, + DOT_EPA: + {` + Department of Transportation (DOT) traffic data as compiled by EPA's EJSCREEN + `} + , + }} + />, + HUD: + {` + Department of Housing & Urban Development’s + (HUD) Comprehensive Housing Affordability Strategy dataset + `} + , + }} + />, + EPA_TSDF: + {` + Environmental Protection Agency (EPA) Treatment Storage, and Disposal Facilities + (TSDF) data calculated from EPA RCRA info database as compiled by EPA’s EJSCREEN + `} + , + }} + />, + EPA_CERCLIS: + {` + Environmental Protection Agency (EPA) CERCLIS database as compiled by EPA’s EJSCREEN + `} + , + }} + />, + EPA_RMP: + {` + Environmental Protection Agency (EPA) RMP database as compiled by EPA’s EJSCREEN + `} + , + }} + />, + EPA_RSEI: + {` + Environmental Protection Agency (EPA) Risk-Screening + Environmental Indicators (RSEI) Model as compiled by EPA's EJSCREEN + `} + , + }} + />, + CDC_PLACES: + {` + Centers for Disease Control and Prevention (CDC) PLACES + `} + , + }} + />, + CDC_SLEEP: + {` + CDC’s U.S. Small-area Life Expectancy Estimates Project (USALEEP) + `} + , + }} + />, +}; + +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: - , - respPartyLabel: - , - dataSourceURL: `https://www.census.gov/programs-surveys/acs`, - dateRange: `2015-2019`, + description: , + 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: , + 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: , + 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: , + 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: , + 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: , + 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: , + 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: , + 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: , + 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: , + 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: , + 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: , + 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: , + 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: , + 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: , + 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: , + 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: , + 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: , + 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: 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: 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: , + 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: , + 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: , + 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: , + 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: , + usedIn: CATEGORIES.WORKFORCE_DEV.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS, + dateRange: DATE_RANGE.FIFETEEN_PLUS_4, + isPercent: true, }, ]; diff --git a/client/src/pages/__snapshots__/contact.test.tsx.snap b/client/src/pages/__snapshots__/contact.test.tsx.snap index ca06f9bb..0150d5ea 100644 --- a/client/src/pages/__snapshots__/contact.test.tsx.snap +++ b/client/src/pages/__snapshots__/contact.test.tsx.snap @@ -150,8 +150,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis This is a beta site. - 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. diff --git a/client/src/pages/__snapshots__/index.test.tsx.snap b/client/src/pages/__snapshots__/index.test.tsx.snap index b1fc8bf6..1e1368c7 100644 --- a/client/src/pages/__snapshots__/index.test.tsx.snap +++ b/client/src/pages/__snapshots__/index.test.tsx.snap @@ -150,8 +150,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis This is a beta site. - 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. diff --git a/client/src/pages/__snapshots__/methodology.test.tsx.snap b/client/src/pages/__snapshots__/methodology.test.tsx.snap index 44fb8798..5a098225 100644 --- a/client/src/pages/__snapshots__/methodology.test.tsx.snap +++ b/client/src/pages/__snapshots__/methodology.test.tsx.snap @@ -150,8 +150,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis This is a beta site. - 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. @@ -575,7 +575,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis low median home value 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 @@ -836,9 +836,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                        - 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. +
                                        • @@ -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
                                        • @@ -875,12 +875,12 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                          - 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. +
                                          • @@ -917,12 +917,12 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                            - 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. +
                                            • @@ -959,19 +959,18 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                              - 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. +
                                              • @@ -1043,8 +1042,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis PM2.5 in the air
                                                - 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. +
                                                • @@ -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 +
                                                • @@ -1081,7 +1084,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Diesel particulate matter exposure
                                                  - Mixture of particles that is part of diesel exhaust in the air. + + Mixture of particles that is part of diesel exhaust in the air. +
                                                  • @@ -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 +
                                                  • @@ -1118,8 +1125,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Traffic proximity and volume
                                                    - 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). +
                                                    • @@ -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 +
                                                    • @@ -1156,9 +1167,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                      - 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. +
                                                      • @@ -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 +
                                                      • @@ -1196,8 +1209,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                        - 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 +
                                                        • @@ -1233,7 +1247,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Low median home value
                                                          - Median home value of owner-occupied housing units in the census tract. + + Median home value of owner-occupied housing units in the census tract. +
                                                          • @@ -1270,9 +1286,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                            - 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. +
                                                            • @@ -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 +
                                                            • Date range: - 2015-2020 + 2020
                                                            • @@ -1312,8 +1328,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                              - 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. +
                                                              • @@ -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 +
                                                              • @@ -1350,8 +1369,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                                - 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. +
                                                                • @@ -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 +
                                                                • @@ -1376,7 +1398,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Affordable and sustainable housing methodology + Reduction and remediation of legacy pollution methodology
                                                                @@ -1387,8 +1409,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Wastewater discharge
                                                                - 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). +
                                                                • @@ -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 +
                                                                • @@ -1425,10 +1451,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Asthma
                                                                  - 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?” +
                                                                  • @@ -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 +
                                                                  • @@ -1464,9 +1493,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Diabetes
                                                                    - 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. +
                                                                    • @@ -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 +
                                                                    • @@ -1502,9 +1535,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis 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. + + 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. +
                                                                      • @@ -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 +
                                                                      • @@ -1541,13 +1578,22 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                                        - 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. + +

                                                                        + + 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. + +

                                                                        + +
                                                                        • @@ -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) +
                                                                        • @@ -1583,7 +1631,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Low median Income
                                                                          - 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. +
                                                                          • @@ -1608,7 +1658,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                                                          @@ -1620,8 +1670,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                                          - 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. +
                                                                          • @@ -1646,7 +1696,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                                                          @@ -1657,7 +1707,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Unemployment
                                                                          - Number of unemployed people as a percentage of the civilian labor force + + Number of unemployed people as a percentage of the civilian labor force +
                                                                          • @@ -1682,7 +1734,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                                                          @@ -1693,7 +1745,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Poverty
                                                                          - 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. +
                                                                          • @@ -1718,7 +1773,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                                                          @@ -1729,8 +1784,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis High school degree achievement rate
                                                                          - 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. +
                                                                          • @@ -1755,7 +1812,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                                                          diff --git a/client/src/styles/global.scss b/client/src/styles/global.scss index 26f9da82..210d40c3 100644 --- a/client/src/styles/global.scss +++ b/client/src/styles/global.scss @@ -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 diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score_post.py b/data/data-pipeline/data_pipeline/etl/score/etl_score_post.py index 048b0bce..89f22601 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_score_post.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_score_post.py @@ -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( diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_utils.py b/data/data-pipeline/data_pipeline/etl/score/etl_utils.py index c3fec035..2219ab18 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_utils.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_utils.py @@ -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 diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/tile_data_expected.pkl b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/tile_data_expected.pkl index 8c47d653..d066a170 100644 Binary files a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/tile_data_expected.pkl and b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/tile_data_expected.pkl differ diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py b/data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py new file mode 100644 index 00000000..594f4856 --- /dev/null +++ b/data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py @@ -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) diff --git a/data/data-pipeline/data_pipeline/etl/sources/energy_definition_alternative_draft/etl.py b/data/data-pipeline/data_pipeline/etl/sources/energy_definition_alternative_draft/etl.py index c8a95fe1..6023f71d 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/energy_definition_alternative_draft/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/energy_definition_alternative_draft/etl.py @@ -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") diff --git a/data/data-pipeline/requirements.txt b/data/data-pipeline/requirements.txt index a12d0449..3eacb5cf 100644 --- a/data/data-pipeline/requirements.txt +++ b/data/data-pipeline/requirements.txt @@ -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" diff --git a/data/data-pipeline/tox.ini b/data/data-pipeline/tox.ini index 381139bb..059520a4 100644 --- a/data/data-pipeline/tox.ini +++ b/data/data-pipeline/tox.ini @@ -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 \ No newline at end of file +commands = pytest diff --git a/docs/decisions/0002-mapping-visualization-library.md b/docs/decisions/0002-mapping-visualization-library.md index 47e87a6e..14c010b4 100644 --- a/docs/decisions/0002-mapping-visualization-library.md +++ b/docs/decisions/0002-mapping-visualization-library.md @@ -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) + #### 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. diff --git a/docs/decisions/0004-client-side-framework.md b/docs/decisions/0004-client-side-framework.md index 0c2f9dd5..1491e851 100644 --- a/docs/decisions/0004-client-side-framework.md +++ b/docs/decisions/0004-client-side-framework.md @@ -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 - + - 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 - + ## 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` diff --git a/docs/operations/README.md b/docs/operations/README.md new file mode 100644 index 00000000..b79d0fce --- /dev/null +++ b/docs/operations/README.md @@ -0,0 +1,3 @@ +# Operations Guides + +This directory should have guides for operating any services that are being supported by the Justice40 team. diff --git a/docs/operations/rollback-plan.md b/docs/operations/rollback-plan.md new file mode 100644 index 00000000..ad260b52 --- /dev/null +++ b/docs/operations/rollback-plan.md @@ -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": "", + "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 `` 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 \ + 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 \ + 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). diff --git a/mlc_config.json b/mlc_config.json index ebc7c01e..53f78d89 100644 --- a/mlc_config.json +++ b/mlc_config.json @@ -14,6 +14,9 @@ }, { "pattern": "^https://github.com/usds/environmental-justice/" + }, + { + "pattern": "^https://.*medium.com" } ], "replacementPatterns": [