Data on homeowners' demographic characteristics from Homeowner Assistance Fund County-Level Targeting Data published in the Urban Institute's data catalog (https://datacatalog.urban.org/dataset/homeowner-assistance-fund-county-level-targeting-data).

Millions of Americans Are About to Lose Their Homes. Congress Must Help Them. -https://www.nytimes.com/2020/07/23/opinion/coronavirus-evictions-rent.htmlWhere to Prioritize Emergency Rental Assistance to Keep Renters in Their Homes - https://www.urban.org/features/where-prioritize-emergency-rental-assistance-keep-renters-their-homesDATASET - https://datacatalog.urban.org/dataset/rental-assistance-priority-index

Launched in 2021, the National Survey of Nonprofit Trends and Impacts is a multiyear panel study conducted by the Urban Institute in collaboration with American University and George Mason University. Discussions about this initiative began in 2015, with input from the Association for Research on Nonprofit Organizations and Voluntary Action (ARNOVA) and numerous scholars across the nation. This nationally representative study investigates various aspects of nonprofit organizations, including programs and services, staffing and volunteerism, fundraising, donations, financial health, and government engagement. The insights aim to help nonprofit leaders establish sustainable and effective organizations and provide guidance to policymakers, funders, and the public to enhance support for the nonprofit sector. Researchers interested in the arts and cultural sector can identify arts and cultural 501(c)(3) nonprofit organizations using the "nteecc_3code" variable, which is based on the National Taxonomy of Exempt Entities (NTEE) codes found in the June 2024 IRS Exempt Organizations Business Master File. Public-use data files and codebooks are available for download at https://datacatalog.urban.org/dataset/national-survey-nonprofit-trends-and-impacts-public-use-files. For comprehensive information about the project, including reports, analyses, and access to restricted data, please visit the project page: https://www.urban.org/partnering-understand-long-term-trends-nonprofit-organization-activities-and-needs.

HUD's Enterprise Data Listing in JSON machine readable format. (Schema Version 1.1)

Geographic coverage

Urban area

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2006 Urban Health Survey is collected using four questionnaires: - Household Questionnaire - Male Questionnaire - Female Questionnaire - Neighborhood Questionnaire

Planned and Unplanned Settlement information product contains spatial explicit information on the two settlement types within the core urban area. The distinction into the two settlement types is restricted to the residential area (LULC class 11), and to commercial areas (LULC class 1211) which also have a residential component. The Planned and Unplanned Settlements and Change dataset is based on Very High Resolution (VHR) satellite imagery (QuickBird-2: 2007) by means of visual interpretation.

Geographic coverage

Urban

Kind of data

Sample survey data [ssd]

Sampling procedure

Multi-stage stratified random sample 24650 Households

Mode of data collection

Face-to-face

Worldwide cover of subway stations built up to December 2010, ridership and shortest linear path maps of subway lines between stations. Also included are the data and replication files used in `Subways and urban growth: Evidence from Earth', Gonalez-Navarro and Turner, Journal of Urban Economics 2018. The paper is available here: https://www.sciencedirect.com/science/article/pii/S009411901830072X

The urban indicators data available here are analyzed, compiled and published by UN-Habitat’s Global Urban Observatory which supports governments, local authorities and civil society organizations to develop urban indicators, data and statistics. Urban statistics are collected through household surveys and censuses conducted by national statistics authorities. Global Urban Observatory team analyses and compiles urban indicators statistics from surveys and censuses. Additionally, Local urban observatories collect, compile and analyze urban data for national policy development. Population statistics are produced by the United Nations Department of Economic and Social Affairs, World Urbanization Prospects.

Polygons indicate the extent of urban (gridcode 5) and developed (gridcode 6) land cover in the greater Kabul area. Polygons were created from classification of Landsat 8 Operational Land Imagery (OLI) 30m resolution multispectral satellite imagery acquired on June 13, 2018.

The Urban Landsat: Cities from Space data set contains images for 66 urban areas and the raw, underlying data for 28 of these places. Each image shows a Landsat false color composite in UTM projection. The R/G/B layers correspond to TM/ETM+ bands 7/4/2. Each pixel is 30x30 meters in area and most images are 30x30 km in area. A 2% linear stretch has been applied to the images. The Landsat data files contain six reflected bands of calibrated exoatmospheric reflectance stored in ENVI band sequential (BSQ) format. Geographic coordinates are included in the header files. The data files contain 1000x1000x6 4 byte floating point numbers as indicated in the header files.

This dataset contains tabular data at three scales (city, tract, and synoptic site) and related vector shapefiles (for watersheds or buffers around synoptic sites) for areas included in the Carbon in Urban River Biogeochemistry Project (CURB) to assess how social, built, and biophysical factors shape aquatic functions. The city scale included 486 urban areas in the continental United States with greater than 50,000 residents. Tabular data are provided for each urban area (CURB_CensusUrbanArea.csv) and all U.S. Census tracts within seven urban areas (Atlanta, GA, Boston, MA, Miami, FL, Phoenix, AZ, Portland, OR, Salt Lake City, UT, and San Francisco, CA; CURB_CensusTract.csv) to characterize a range of social, built, and biophysical factors. In six focal cities (Baltimore, MD, Boston, MA, Atlanta, GA, Miami, FL, Salt Lake City, UT, and Portland, OR) up to 100 sites were selected for synoptic water quality sampling. For each synoptic site tabular data (CURB_SynopticSite.csv) are pro ...

The Transport Infrastructure information product shows the classification of three road types: Arterial Roads, Collector Roads and Local Roads in 2006 and 2015. The Transport Infrastructure dataset is based on Very High Resolution (VHR) satellite imagery (QuickBird-2 (2006, 2008) and Pleiades (2015)) by means of manual classification processing techniques.

This dataset is a categorical mapping of estimated mean building heights, by Census block group, in shapefile format for the conterminous United States. The data were derived from the NASA Shuttle Radar Topography Mission, which collected “first return” (top of canopy and buildings) radar data at 30-m resolution in February, 2000 aboard the Space Shuttle Endeavor. These data were processed here to estimate building heights nationally, and then aggregated to block group boundaries. The block groups were then categorized into six classes, ranging from “Low” to “Very High”, based on the mean and standard deviation breakpoints of the data. The data were evaluated in several ways, to include comparing them to a reference dataset of 85,000 buildings for the city of San Francisco for accuracy assessment and to provide contextual definitions for the categories.

This 30-meter resolution raster data set of land cover for the conterminous United States ("NLCDep0905") was designed to describe conditions representative of the year 2000 and is the result of overlaying enhanced 1992 National Land Cover Data with 2000 population data at the block group geographic level. Any area (excluding water, developed land, or wetlands) with population density of at least 1,000 people per square mile was reclassified as "newly urbanized" land in the derivative product. Areas of water, developed land, or wetlands existing in the original national land-cover data set were preserved.

This data set has been superseded by the one called "Enhanced National Land Cover Data 1992 revised with 1990 and 2000population data to indicate urban development between 1992 and 2000" ("NLCDep0306") dated March 2006. The approach used in developing NLCDep0905 was determined to have misclassified lands that already were urban in 1990 as newly urbanized and therefore great ...

The wildland-urban interface (WUI) is the area where urban development occurs in close proximity to wildland vegetation. We generated WUI maps for the conterminous U.S. using building point locations (Carlson et al. 2022), offering higher spatial resolution compared to previously developed WUI maps based on U.S. Census Bureau housing density data (Radeloff et al., 2017). Building point locations were obtained from a Microsoft product released in 2018, which classified building footprints based on high-resolution satellite imagery. Maps were also based on wildland vegetation mapped by the 2016 National Land Cover Dataset (Yang et al., 2018). The mapping algorithm utilized definitions of the WUI from the U.S. Federal Register (USDA & USDI, 2001) and Radeloff et al. (2005). According to these definitions, two classes of WUI were identified: 1) the intermix, where there is at least 50% vegetation cover surrounding buildings, and 2) the interface, where buildings are within 2.4 km ...

Researchers collected data in the South Bronx to determine the impact of government-subsidized supermarkets in high-need areas on food availability and dietary habits. Data was collected in Morrisania, a community in the South Bronx where the government-subsidized New York City FRESH Program supermarket was opened and in Highbridge, a comparison community in the South Bronx that does not have a similar program.

Data was collected via street-intercept survey and follow-up 24 hour recall over the telephone on the habits of children through their parents/caregivers. Data collection occurred in 2011 in three waves: in Morrisania before the supermarket opened, in Morrisania after the supermarket opened, and in Highbridge. Data collected included demographic information (e.g., gender, age, race/ethnicity, household income, education, marital status, household size, and employment), where participants typically shopped for food, how far they travelled to their usual store, whether participants had heard of a new store opening in their neighborhood, household food availability of selected healthful and unhealthful foods, and food consumption habits. A total of 2230 surveys were collected.

Geographic coverage

Urban only (Elbasan Fier Lezha)

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

This data set presents ecosystem geomorphic and soil attributes, sediment and nutrient loading rates, and rates of nutrient biogeochemistry processes, including denitrification and N and P mineralization, in floodplains of urban restored streams. The restored streams were located in the Charlotte, North Carolina, metropolitan area and were studied from 2012-2013.

Semi-arid urban environments are undergoing an increase in air temperatures, both in average temperatures and in the frequency and intensity of extreme heat events. Within cities, different varieties of urban landcovers (ULC) and their densities influence local air temperatures, either mitigating or increasing heat. Currently, understanding how various combinations of ULCs influence air temperature at the block to neighborhood scale is limited due to the complexities of urban energy balances at small scales. We quantified how ULC influences air temperature at 60 m resolution for day and nighttime climate normals and heatwaves, by integrating data from microclimate temperature sensor networks and high-resolution (1 m2) ULC for Denver Colorado’s urban core. We derived ULC drivers of air temperature using a structural equation model, and projected urban heat scenarios of climate normals and heatwaves throughout the extent of urban Denver. We found that, in conjunction with other ULCs ...