HUD furnishes technical and professional assistance in planning, developing and managing these developments. Public Housing Developments are depicted as a distinct address chosen to represent the general location of an entire Public Housing Development, which may be comprised of several buildings scattered across a community. The building with the largest number of units is selected to represent the location of the development. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Developments Date Updated: Q2 2025

CPD Maps includes data on the locations of existing CDBG, HOME, public housing and other HUD-funded community assets, so that users can view past investments geographically when considering various strategies for future funding. CPD Maps offers a large amount of data in a way that is easy to access. The website allows grantees and the general public to easily search, query, and display information to identify trends and analyze the needs of their community.

The feature set indicates the locations, and tenant characteristics of public housing development buildings for the San Francisco Bay Region. This feature set, extracted by the Metropolitan Transportation Commission, is from the statewide public housing buildings feature layer provided by the California Department of Housing and Community Development (HCD). HCD itself extracted the California data from the United States Department of Housing and Urban Development (HUD) feature service depicting the location of individual buildings within public housing units throughout the United States.

According to HUD's Public Housing Program, "Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by some 3,300 housing agencies. HUD administers federal aid to local housing agencies that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments.

HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This feature set provides the location, and resident characteristics of public housing development buildings.

Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:

‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) 
‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) 
‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) 
‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) 
‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) 
 ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) 
‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) 
Null - Could not be geocoded (does not appear on the map) 

For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information, the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10.

HCD downloaded the HUD data in April 2021. They sourced the data from https://hub.arcgis.com/datasets/fedmaps::public-housing-buildings.

To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/.

HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This dataset provides the location and resident characteristics of public housing development buildings. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/ Development FAQs - IMS/PIC | HUD.gov / U.S. Department of Housing and Urban Development (HUD), for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Buildings Date Updated: Q2 2025

This dataset denotes the Pathways to Removing Obstacles to Housing (PRO Housing) Priority Geography Map. Under the Need rating factor, applicants will be awarded ten (10) points if their application primarily serves a ‘priority geography’. Priority geography means a geography that has an affordable housing need greater than a threshold calculation for one of three measures. The threshold calculation is determined by the need of the 90th-percentile jurisdiction (top 10%) for each factor as computed comparing only jurisdictions with greater than 50,000 population. Threshold calculations are done at the county and place level and applied respectively to county and place applicants. An application can also quality as a priority geography if it serves a geography that scores in the top 5% of its State for the same three measures.

Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by over 3,300 housing agencies (HAs). HUD administers Federal aid to local housing agencies (HAs) that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Authorities Date Updated: Q2 2025

This layer is intended for researchers, students, policy makers, and the general public for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production. This layer will provide a basemap for layers related to socio-political analysis, statistical enumeration and analysis, or to support graphical overlays and analysis with other spatial data. More advanced user applications may focus on demographics, urban and rural land use planning, socio-economic analysis and related areas (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output.)

A list of complaints received and associated data. Prior monthly reports are archived at DOB and are not available on NYC Open Data.

The official tax maps for the City of New York are maintained by the DEPARTMENT OF FINANCE Tax Map Office. Tax maps show the lot lines, the block and lot numbers, the street names, lot dimensions, and easements.

The NYC Department of City Planning’s (DCP) Housing Database contains all NYC Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. It includes the three primary construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. Records in the Housing Database Project-Level Files are geocoded to the greatest level of precision possible, subject to numerous quality assurance and control checks, recoded for usability, and joined to other housing data sources relevant to city planners and analysts.

Data are updated semiannually, at the end of the second and fourth quarters of each year.

Please see DCP’s annual Housing Production Snapshot summarizing findings from the 21Q4 data release here. Additional Housing and Economic analyses are also available.

The NYC Department of City Planning’s (DCP) Housing Database Unit Change Summary Files provide the net change in Class A housing units since 2010, and the count of units pending completion for commonly used political and statistical boundaries (Census Block, Census Tract, City Council district, Community District, Community District Tabulation Area (CDTA), Neighborhood Tabulation Area (NTA). These tables are aggregated from the DCP Housing Database Project-Level Files, which is derived from Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions. These files can be used to determine the change in legal housing units across time and space.

The United States Department of Housing and Urban Development (HUD) is organized into 10 Regions where each Region is managed by a Regional Administrator, who also oversees the Regional Office. Each Field Office within a Region is managed by a Field Office Director, who reports to the Regional Administrator. There is at least one HUD Field Office in every State and a total of 10 Regional Offices. Staff who answer the main office telephone will be able to respond to or direct your calls to the appropriate person. Data Current As Of: 11/1/2012

City map highlighting 2023 qualified census tracts (QCT) in Mesa. Low-Income Housing Tax Credit Qualified Census Tracts must have 50 percent of households with incomes below 60 percent of the Area Median Gross Income (AMGI) or have a poverty rate of 25 percent or more. Maps of Qualified Census Tracts are available at: https://www.huduser.gov/portal/datasets/qct.html

The FHA insured Multifamily Housing portfolio consists primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also be nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. Please note that this dataset overlaps the Multifamily Properties Assisted layer. The Multifamily property locations represent the approximate location of the property. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about HUD Insured Multifamily Properties visit: https://www.hud.gov/program_offices/housing/mfh Data Dictionary: DD_HUD Insured Multifamilly Properties Date of Coverage: 02/2025

This dataset allows users to map United States Department of Agriculture's (USDA's) rural development multi family housing assets. The USDA, Rural Development (RD) Agency operates a broad range of programs that were formally administered by the Farmers Home Administration to support affordable housing and community development in rural areas. RD helps rural communities and individuals by providing loans and grants for housing and community facilities. RD provides funding for single family homes, apartments for low-income persons or the elderly, housing for farm laborers, childcare centers, fire and police stations, hospitals, libraries, nursing homes and schools.

This dataset contains grouped information about affordable housing projects funded through the American Rescue Plan Act (ARPA), Community Development Block Grant (CDBG), HOME Investment Partnerships Program (HOME), HOME Investment Partnerships American Rescue Plan Program (HOME-ARP), Housing Development Authority (HRA) levy, General Obligation Bonds (GO Bonds) and Statewide Affordable Housing Aid (SAHA) in 2021-2023. It includes project name, funding source, funded amount, and housing unit counts.

This dataset compiles a comprehensive database containing 90,327 street segments in New York City, covering their street design features, streetscape design, Vision Zero treatments, and neighborhood land use. It has two scales-street and street segment group (aggregation of same type of street at neighborhood). This dataset is derived based on all publicly available data, most from NYC Open Data. The detailed methods can be found in the published paper, Pedestrian and Car Occupant Crash Casualties Over a 9-Year Span of Vision Zero in New York City. To use it, please refer to the metadata file for more information and cite our work. A full list of raw data source can be found below:

Motor Vehicle Collisions – NYC Open Data: https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95

Citywide Street Centerline (CSCL) – NYC Open Data: https://data.cityofnewyork.us/City-Government/NYC-Street-Centerline-CSCL-/exjm-f27b

NYC Building Footprints – NYC Open Data: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh

Practical Canopy for New York City: https://zenodo.org/record/6547492

New York City Bike Routes – NYC Open Data: https://data.cityofnewyork.us/Transportation/New-York-City-Bike-Routes/7vsa-caz7

Sidewalk Widths NYC (originally from Sidewalk – NYC Open Data): https://www.sidewalkwidths.nyc/

LION Single Line Street Base Map - The NYC Department of City Planning (DCP): https://www.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page

NYC Planimetric Database Median – NYC Open Data: https://data.cityofnewyork.us/Transportation/NYC-Planimetrics/wt4d-p43d

NYC Vision Zero Open Data (including multiple datasets including all the implementations): https://www.nyc.gov/content/visionzero/pages/open-data

NYS Traffic Data - New York State Department of Transportation Open Data: https://data.ny.gov/Transportation/NYS-Traffic-Data-Viewer/7wmy-q6mb

Smart Location Database - US Environmental Protection Agency: https://www.epa.gov/smartgrowth/smart-location-mapping

Race and ethnicity in area - American Community Survey (ACS): https://www.census.gov/programs-surveys/acs

FY2024 full and partial census tracts that qualify as Low-Moderate Income Areas (LMA) where 51% or more of the population are considered as having Low-Moderate Income. The low- and moderate-income summary data (LMISD) is based on the 2016-2020 American Community Survey (ACS). As of August 1, 2024, to qualify any new low- and moderate-income area (LMA) activities, Community Development Block Grant (CDBG) grantees should use this map and data.

For more information about LMA/LMI click the following link to open in new browser tab https://www.hudexchange.info/programs/cdbg/cdbg-low-moderate-income-data/

The data provided here denotes the authors’ revised service areas for a subset of 377 Public Housing Authorities (PHAs) for which HUD previously estimated service areas. Using HUD administrative data on the location of Housing Choice Voucher holders, HUD’s estimated service areas were revised to better capture voucher activity. Specifically, the authors developed two different tests and correction procedures. The first assesses if the estimated service area omits a sizable share of voucher holder locations (so is “too small”), and if so, adjusts to include census designated places or counties containing at least 5 percent of a PHA’s voucher holders. The second test checks whether the estimated service boundary includes areas the PHA does not appear to serve and that are clearly served by another PHA (so is “too large”), in this case adjusting by removing those areas. 148 of the 377 PHA estimated service areas were found to be too small, too large, or both, and so have revised service areas that differ from HUD’s estimated service areas. The detailed methodology is provided below. Additionally, a spreadsheet is supplied that identifies geographies that were added to and dropped from HUD’s estimated services to create the revised service areas for affected PHAs.

This is an experimental dataset that is designed to aid researchers in studying the HCV program. The methodology and the service areas themselves have not been validated by HUD’s Office of Public and Indian Housing (PIH) or the Public Housing Agencies. For additional discussion of the approach, see Tauber et al. (2024); please contact the authors with any questions or comments.Data Dictionary: DD_Extensions to Estimated Housing Authority Service Areas MethodologyMethodology: Extensions to Estimated Housing Authority Service Areas Methodology

Reference:Tauber, Kristen, Ingrid Gould Ellen, and Katherine O’Regan. 2024. “Whom Do We Serve? Refining Public Housing Agency Service Areas.” Cityscape 26(1) (2024): 395-400.

Net change in housing units arising from new buildings, demolitions, or alterations for NYC Census Blocks since 2010. The NYC Department of City Planning's (DCP) Housing Database provide the 2010 census count of housing units, the net change in Class A housing units since the census, and the count of units pending completion for commonly used political and statistical boundaries. These tables are aggregated from the DCP Housing Database, which is derived from Department of Buildings (DOB)-approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. All previously released versions of this data are available at BYTES of the BIG APPLE - Archive.

This map layer displays the planning areas of the winners of the Sustainable Communities Regional Planning Grant competition for FY2010 and FY2011. Program applicants were required to designate their planning area according to a set of criteria given in the Notice of Funding Availability, which in most circumstances ensured that applicant geographies would be composed of counties, MSAs, or the planning areas of Metropolitan Planning Organizations. The majority of geographies in this file were assembled from county, MSA, and MPO shapefiles available on servers or publicly elsewhere. The remaining geographies used publicly available geospatial data such as municipal line files and tribal boundaries.

To learn more about the Sustainable Communities Regional Planning Grants Program visit: https://www.hud.gov/program_offices/economic_development/sustainable_communities_regional_planning_grants, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Sustainable Communities Regional Planning Grantees

Date of Coverage: 12/2014