11 datasets found
  1. Low-Income or Disadvantaged Communities Designated by California

    • data.ca.gov
    • data.cnra.ca.gov
    • +5more
    Updated Jun 11, 2025
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    California Energy Commission (2025). Low-Income or Disadvantaged Communities Designated by California [Dataset]. https://data.ca.gov/dataset/low-income-or-disadvantaged-communities-designated-by-california
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    arcgis geoservices rest api, csv, kml, zip, html, geojsonAvailable download formats
    Dataset updated
    Jun 11, 2025
    Dataset authored and provided by
    California Energy Commissionhttp://www.energy.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    California
    Description

    This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.


    Data downloaded in May 2022 from https://webmaps.arb.ca.gov/PriorityPopulations/.

  2. Low and Moderate Income Areas

    • catalog.data.gov
    • s.cnmilf.com
    • +1more
    Updated Mar 1, 2024
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    U.S. Department of Housing and Urban Development (2024). Low and Moderate Income Areas [Dataset]. https://catalog.data.gov/dataset/hud-low-and-moderate-income-areas
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    Dataset updated
    Mar 1, 2024
    Dataset provided by
    United States Department of Housing and Urban Developmenthttp://www.hud.gov/
    Description

    This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

  3. d

    Opportunity Zones Census Tracts Designated by the District of Columbia

    • opendata.dc.gov
    • s.cnmilf.com
    • +1more
    Updated Apr 6, 2018
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    City of Washington, DC (2018). Opportunity Zones Census Tracts Designated by the District of Columbia [Dataset]. https://opendata.dc.gov/datasets/opportunity-zones-census-tracts-designated-by-the-district-of-columbia
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    Dataset updated
    Apr 6, 2018
    Dataset authored and provided by
    City of Washington, DC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    Created in the Tax Cuts and Jobs Act of 2017, Opportunity Zones is a new federal program that provides tax incentives for investments in new businesses and commercial projects in low-income communities. On April 2018, Mayor Bowser nominated 25 census tracts to be Opportunity Zones. The U.S. Department of Treasury certified these tracts on May 18, 2018.

  4. a

    Remote Zip Codes

    • chi-phi-nmcdc.opendata.arcgis.com
    Updated Oct 14, 2019
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    New Mexico Community Data Collaborative (2019). Remote Zip Codes [Dataset]. https://chi-phi-nmcdc.opendata.arcgis.com/datasets/remote-zip-codes
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    Dataset updated
    Oct 14, 2019
    Dataset authored and provided by
    New Mexico Community Data Collaborative
    Area covered
    Description

    Map for question/section 1: "Who lives there?"Containing Rural areas as defined by US Census 2013 urban/rural defined areas, http://nmcdc.maps.arcgis.com/home/item.html?id=fbd1e91ec0a54c58b6fcca8a5138c1fc. Filtered to include: 'RURAL' in 2 category designation, Population of LESS THAN 5001 persons, AND % Low access low-income at 20 miles to AT LEAST 5%. Map displaying by Esri 2019 Age Dependency Ratios.

  5. d

    ACP Households by Zip Code

    • data.detroitmi.gov
    • detroitdata.org
    • +1more
    Updated Jun 13, 2023
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    City of Detroit (2023). ACP Households by Zip Code [Dataset]. https://data.detroitmi.gov/maps/detroitmi::acp-households-by-zip-code
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    Dataset updated
    Jun 13, 2023
    Dataset authored and provided by
    City of Detroit
    Area covered
    Description

    Discounts for Internet service through the Affordable Connectivity Program (ACP) ended June 1, 2024 due to lack of additional funding. Whether the program will receive additional funding in the future is uncertain. Please see ACP program information from the FCC for more details.The Affordable Connectivity Program (ACP) households data set summarizes household enrollments and subscriptions by month and zip code for beneficiary households located in Detroit zip codes. The Affordable Connectivity Program (ACP) is a U.S. government program to help low-income households pay for Internet services and connected devices. Households that participate in ACP receive discounts on qualifying broadband Internet services of up to $30 per month and can also receive a one-time discount of up to $100 to purchase a laptop, desktop computer, or tablet. Households can qualify for ACP based on participation in Lifeline or other service provider programs for low-income households, income at or below 200% of the federal poverty guidelines, participation in other Lifeline-qualifying programs such as SNAP or Medicaid, or participation in free and reduced-price school lunch and breakfast programs. Additionally, service providers can ask the FCC to approve an alternative verification process and use that approved process to check consumer eligibility. ACP program discounts first became available to eligible enrolled households on January 1, 2022. The ACP claims process is built on the Lifeline Claims System and this data set is derived from snapshots of all subscribers entered in the National Lifeline Accountability Database (NLAD) as of the first of each month. The ACP was created under the Infrastructure Investment and Jobs Act, also known as the Bipartisan Infrastructure Law, and is administered by the independent not-for-profit Universal Service Access Co. under the direction of the Federal Communications Commission (FCC). Eligible beneficiaries who participated in the Emergency Broadband Benefit (EBB) program that was funded by the Coronavirus Aid, Relief, and Economic Security (CARES) Act, were transitioned to ACP between January 1 and March 1, 2022. EBB was ACP's predecessor program and ran from May 12, 2021 until it was phased out on February 28, 2022. Due to the granularity of available data, households located in communities adjacent to Detroit that share a zip code such as Hamtramck and Highland Park are included in this data set.Fieldsprogram - Associated program for the data (ACP or EBB)data_month - Data month is associated with the subscriber snapshot for each claim month. If data month is listed as '5/1/2022', then the subscriber snapshot was captured on June 1, and the data represents the number of households in ACP as of June 1. This is the universe of subscribers that providers can claim for the May 2022 data month.zipcode - Zip code where the enrolled household is located.net_new_enrollments_alternative_verification_process - Difference between the current month Total Subscribers who qualified using an alternative verification process and prior month Total Subscribers who qualified using an alternative verification process.net_new_enrollments_verified_by_school - Difference between the current month Total Subscribers who qualified using school lunch program verification and prior month Total Subscribers who qualified using school lunch program verification.net_new_enrollments_lifeline - Difference between the current month Total Subscribers who qualified using the Lifeline program and prior month Total Subscribers who qualified using the Lifeline program.net_new_enrollments_national_verifier_application - Difference between the current month Total Subscribers who qualified using a National Verifier application and prior month Total Subscribers who qualified using a National Verifier application.net_new_enrollments_total - Difference between the total number of subscribers in the current and prior months. Calculated based on the sum of net new monthly enrollments verified by the school, lifeline, alternative verification process, and national verifier application programs.total_alternative_verification_process - Number of households in the ACP on the first of the month snapshot whose eligibility was determined via an FCC-approved alternative verification process. total_verified_by_school - Number of households in the ACP on the first of the month snapshot whose eligibility was verified based on participation in a school lunch program.total_lifeline - Number of households in the ACP on the first of the month snapshot whose eligibility was determined based on participation in Lifeline, a federal program that lowers the monthly cost of phone or Internet services.total_national_verifier_application - Number of households in the ACP on of the first of the month snapshot whose eligibility was determined via the National Eligibility Verifier (National Verifier) system.total_subscribers - Number of total households participating in ACP on the first of the month snapshot. If, for example, there were 100 subscribers enrolled as of the June 1, 2022 snapshot, then Total Subscribers for the 05/01/2022 (May 2022) data month would be 100.

  6. d

    ACP Claims by Zip Code

    • data.detroitmi.gov
    • detroitdata.org
    • +1more
    Updated Jun 13, 2023
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    City of Detroit (2023). ACP Claims by Zip Code [Dataset]. https://data.detroitmi.gov/datasets/acp-claims-by-zip-code
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    Dataset updated
    Jun 13, 2023
    Dataset authored and provided by
    City of Detroit
    Area covered
    Description

    Discounts for Internet service through the Affordable Connectivity Program (ACP) ended June 1, 2024 due to lack of additional funding. Whether the program will receive additional funding in the future is uncertain. Please see ACP program information from the FCC for more details.The Affordable Connectivity Program (ACP) claims data set summarizes reimbursement claims submitted by participating Internet service providers for households in Detroit zip codes by month and zip code. The Affordable Connectivity Program (ACP) is a U.S. government program to help low-income households pay for Internet services and connected devices. Households that participate in ACP receive discounts on qualifying broadband Internet services of up to $30 per month and can also receive a one-time discount of up to $100 to purchase a laptop, desktop computer, or tablet. Households can qualify for ACP based on participation in Lifeline or other service provider programs for low-income households, income at or below 200% of the federal poverty guidelines, participation in other Lifeline-qualifying programs such as SNAP or Medicaid, or participation in free and reduced-price school lunch and breakfast programs. Additionally, service providers can also ask the FCC to approve an alternative verification process and use that approved process to check consumer eligibility. ACP program discounts first became available to eligible enrolled households on January 1, 2022. The ACP claims process is built on the Lifeline Claims System and this data set is derived from snapshots of all subscribers entered in the National Lifeline Accountability Database (NLAD) as of the first of each month. The ACP was created under the Infrastructure Investment and Jobs Act, also known as the Bipartisan Infrastructure Law, and is administered by the independent not-for-profit Universal Service Access Co. under the direction of the Federal Communications Commission (FCC). Eligible beneficiaries who participated in the Emergency Broadband Benefit (EBB) program that was funded by the Coronavirus Aid, Relief, and Economic Security (CARES) Act, were transitioned to ACP between January 1 and March 1, 2022. EBB was ACP's predecessor program and ran from May 12, 2021 until it was phased out on February 28, 2022. Due to the granularity of available data, claims for households located in communities adjacent to Detroit that share a zip code such as Hamtramck and Highland Park are included in this data set.Fieldsogc_fid - Zip code id.zipcode - Zip code where the enrolled household is located.postalcity - City associated with the zip code.data_month - Data month is associated with the subscriber snapshot for each claim month. If data month is listed as '5/1/2022', then the subscriber snapshot was captured on June 1, and the data represents the number of households in ACP on June 1. This is the universe of subscribers that providers can claim for the May 2022 data month.total_claimed_subscribers - Total number of enrolled households claimed for reimbursement as of the data month snapshot.total_claimed_devices - Total number of devices (laptops, desktop computers, or tablets) claimed for reimbursement as of the data month snapshot.service_support - Amount program providers claimed for reimbursement under the ACP program in the given month, in dollars. Reimbursement claims are for discounts provided to enrolled households to reduce the standard rate of an eligible broadband service and associated equipment rentals. For households that receive both Lifeline and ACP discounts and apply both benefits to their qualifying broadband service, the Lifeline discount ($9.25) is applied first and the ACP discount is then applied to the remaining amount.device_support - Amount discounted to households for purchasing a device (laptop, desktop computer, or tablet) in the given month, in dollars. Each household is eligible for a one-time reimbursement payment of up to $100 for one connected device.total_support - Sum of service support and device support in the given month, in dollars.

  7. Community Credit survey on trust in consumer financial services

    • data.niaid.nih.gov
    • search.dataone.org
    • +1more
    zip
    Updated Oct 3, 2023
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    Bill Maurer; Taylor Nelms; Melissa Wrapp; Ellen Kladky; Anna Bruzgulis (2023). Community Credit survey on trust in consumer financial services [Dataset]. http://doi.org/10.5061/dryad.sqv9s4n8r
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    zipAvailable download formats
    Dataset updated
    Oct 3, 2023
    Dataset provided by
    Filene Research Institutehttp://filene.org/
    University of California, Irvine
    Authors
    Bill Maurer; Taylor Nelms; Melissa Wrapp; Ellen Kladky; Anna Bruzgulis
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we conducted an online survey investigating people’s everyday financial practices, evolving perceptions of trust and risk, and their unmet financial needs. The general population survey data was collected between April 15 and April 22, 2022. The credit union data was collected between May 3 and July 18, 2022. This data set contains the responses of the survey participants after excluding any personally identifying data. All study materials and procedures were approved by the University of California, Irvine Office of Human Research Protections and the Institutional Review Board (protocol ID 20216839). This material is based upon work supported by the National Science Foundation under Grant No. 2137567. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Methods Survey data was collected via the Qualtrics platform. The survey contains 52 questions. It was distributed to the general population in zip codes within the counties of Los Angeles and Orange. It was also distributed directly to members of a large credit union headquartered in Orange County (“large” according to NCUA asset classes). Participants were eligible to complete the survey if they live in Orange County or Los Angeles County, are older than 18, and have a combined household income of less than $100,000. Incomplete responses have been removed. The survey yielded 1,370 complete responses (1,213 from the general population participants and 157 from members of the large credit union).

  8. d

    Food Access Research Atlas

    • catalog.data.gov
    • datasetcatalog.nlm.nih.gov
    • +4more
    Updated Apr 21, 2025
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    Economic Research Service, Department of Agriculture (2025). Food Access Research Atlas [Dataset]. https://catalog.data.gov/dataset/food-access-research-atlas
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    Dataset updated
    Apr 21, 2025
    Dataset provided by
    Economic Research Service, Department of Agriculture
    Description

    The Food Access Research Atlas presents a spatial overview of food access indicators for low-income and other census tracts using different measures of supermarket accessibility, provides food access data for populations within census tracts, and offers census-tract-level data on food access that can be downloaded for community planning or research purposes.

  9. S

    Final Disadvantaged Communities (DAC) 2023

    • data.ny.gov
    • gimi9.com
    • +2more
    Updated Oct 11, 2023
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    New York State Energy Research and Development Authority (NYSERDA) (2023). Final Disadvantaged Communities (DAC) 2023 [Dataset]. https://data.ny.gov/Energy-Environment/Final-Disadvantaged-Communities-DAC-2023/2e6c-s6fp
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    csv, xml, xlsx, application/geo+json, kmz, kmlAvailable download formats
    Dataset updated
    Oct 11, 2023
    Dataset authored and provided by
    New York State Energy Research and Development Authority (NYSERDA)
    Description

    The Climate Leadership and Community Protection Act (CLCPA) directs the Climate Justice Working Group (CJWG) to establish criteria for defining disadvantaged communities. This dataset identifies areas throughout the State that meet the final disadvantaged community definition as voted on by the Climate Justice Working Group.

    The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, accelerate economic growth, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

  10. C

    Climate Ready Boston Social Vulnerability

    • cloudcity.ogopendata.com
    • data.boston.gov
    • +3more
    Updated Sep 21, 2017
    + more versions
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    Geographic Information Systems (2017). Climate Ready Boston Social Vulnerability [Dataset]. https://cloudcity.ogopendata.com/dataset/climate-ready-boston-social-vulnerability
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    arcgis geoservices rest api, kml, csv, zip, geojson, htmlAvailable download formats
    Dataset updated
    Sep 21, 2017
    Dataset provided by
    BostonMaps
    Authors
    Geographic Information Systems
    Area covered
    Boston
    Description
    Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses.

    Source:

    The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.

    Population Definitions:

    Older Adults:
    Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.
    Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.
    Attribute label: OlderAdult

    Children:
    Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.
    Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.
    Attribute label: TotChild

    People of Color:
    People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups as
    well. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.
    Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.
    Attribute label: POC2

    Limited English Proficiency:
    Without adequate English skills, residents can miss crucial information on how to prepare
    for hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more socially
    isolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.
    Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.
    Attribute label: LEP

    Low to no Income:
    A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.
    Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.
    Attribute label: Low_to_No

    People with Disabilities:
    People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.
    Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty.
    Attribute label: TotDis

    Medical Illness:
    Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.
    Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.
    Attribute label: MedIllnes

    Other attribute definitions:
    GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census Tract
    AREA_SQFT: Tract area (in square feet)
    AREA_ACRES: Tract area (in acres)
    POP100_RE: Tract population count
    HU100_RE: Tract housing unit count
    Name: Boston Neighborhood
  11. Difficult Development Areas

    • hub.arcgis.com
    Updated Sep 13, 2022
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    Esri U.S. Federal Datasets (2022). Difficult Development Areas [Dataset]. https://hub.arcgis.com/maps/fedmaps::difficult-development-areas
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    Dataset updated
    Sep 13, 2022
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri U.S. Federal Datasets
    Area covered
    Description

    Difficult Development Areas This U.S. Department of Housing and Urban Development feature layer depicts Difficult Development Areas in the United States. Per HUD, “Difficult Development Areas (DDA) for the Low Income Housing Tax Credit program are designated by U.S. Department of Housing and Urban Development (HUD) and defined in statute as areas with high construction, land, and utility costs relative to its Area Median Gross Income (AMGI). DDAs in metropolitan areas are designated along Census ZIP Code Tabulation Area (ZCTA) boundaries. DDAs in non-metropolitan areas are designated along county boundaries. DDAs may not contain more than 20% of the aggregate population of metropolitan and non-metropolitan areas, which are designated separately."Baltimore/Columbia/Towson Small Area DDA Data currency: Current Federal Service (Difficult Development Areas 2025)Data modification: NoneFor more information: Housing and Urban Development; Difficult Development Area TablesFor feedback, please contact: ArcGIScomNationalMaps@esri.com Department of Housing and Urban Development Per HUD, “The Department of Housing and Urban Development administers programs that provide housing and community development assistance. The Department also works to ensure fair and equal housing opportunity for all.”

  12. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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California Energy Commission (2025). Low-Income or Disadvantaged Communities Designated by California [Dataset]. https://data.ca.gov/dataset/low-income-or-disadvantaged-communities-designated-by-california
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Low-Income or Disadvantaged Communities Designated by California

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2 scholarly articles cite this dataset (View in Google Scholar)
arcgis geoservices rest api, csv, kml, zip, html, geojsonAvailable download formats
Dataset updated
Jun 11, 2025
Dataset authored and provided by
California Energy Commissionhttp://www.energy.ca.gov/
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
California
Description

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.


Data downloaded in May 2022 from https://webmaps.arb.ca.gov/PriorityPopulations/.

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