DDOT's Urban Forestry Division (UFD) is the primary steward of Washington DC's ~175,000 public trees and has a mission of keeping this resource healthy, safe, & growing. Trees in the city are critical to our well-being. Visit trees.dc.gov for more information.

Table of INEBase Urban Audit Indicators for Cities. Annual. National. Urban Indicators

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

Access to the data of the urban files stored in its database is provided through an API (Application Programming Interface).

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2017 TIGER/Line® Shapefiles: Urban Areas

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The relatively quiet urban oases include public green areas and open spaces with a high quality of living space and an appropriate design as compensation areas within walking distance of residential and work locations. Due to their inner-city location, they do not, or do not completely, meet the above-mentioned criteria of a quiet area, e. g. by showing an increased noise level or being significantly lower. However, the urban planning context of the areas has noise-reducing properties, which lead to the fact that the urban oases in their core areas are considerably calmer than their surroundings. These areas make it possible, for example, to take short walks for those seeking peace and quietness in the immediate vicinity of their homes or workplaces.

Analysis of ‘Urban Audit Indicators for Sub-Municipal Areas. UA (API identifier: 46645)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-405-46645 on 08 January 2022.

--- Dataset description provided by original source is as follows ---

Table of INEBase Urban Audit Indicators for Sub-Municipal Areas. Annual. Municipalities. Urban Indicators

--- Original source retains full ownership of the source dataset ---

This theme delineates Urban Growth Boundaries (UGBs) in the state of Oregon. Oregon land use laws limit development outside of urban growth boundaries. The line work was created by various sources including the Oregon Department of Land Conservation and Development (DLCD), the Oregon Department of Transportation (ODOT), Metro Regional Council of Governments (Metro), county and city GIS departments, and the Oregon Department of Administrative Services - Geospatial Enterprise Office (DAS-GEO).

This scene contains the relative heat severity for every pixel for every city in the United States, from this source layer. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2018 and 2019.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this scene is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource Center: https://www.epa.gov/heat-islands/heat-island-resourcesDr. Ladd Keith, University of Arizona: https://www.laddkeith.com/ Dr. Ben McMahan, University of Arizona: https://www.climas.arizona.edu/about/people/ben-mcmahan Dr. Jeremy Hoffman, Science Museum of Virginia: https://jeremyscotthoffman.com/about-me-shift#about Dr. Hunter Jones, NOAA: https://cpo.noaa.gov/News/News-Article/ArtMID/6226/ArticleID/971/CPOs-Hunter-Jones-delivers-keynote-on-Climate-and-Extreme-Heat-at-Design-for-Risk-Reduction-Symposium-in-NYC Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and Resiliency: https://youtu.be/sAHlqGDU0_4 Disclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Pete.Aniello@tpl.org with feedback.

Analysis of ‘Decrees over Urban Leases by type of lease. (API identifier: 29270)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-95-29270 on 08 January 2022.

--- Dataset description provided by original source is as follows ---

Table of INEBase Decrees over Urban Leases by type of lease. Annual. Autonomous Communities and Cities. Statistics on Lawsuits in Urban Leases

--- Original source retains full ownership of the source dataset ---

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The quiet urban landscape areas include relatively large, continuous open spaces of at least regional importance with a high recreational function and corresponding development for leisure and recreation. Its importance lies in the balancing function to the noisy and densely populated areas of the agglomeration of Luxembourg. The quiet urban landscape close to the residential area allows, for example, extensive walks with only occasional crossing of areas with higher noise levels.

Growth data for housing units and employment for recognized sub-villages of three of the City of Seattle's designated urban centers.Housing unit growth is reported quarterly from the city's permitting system while employment change is reported annually from the State of Washington QCEW data.

Polygons of Urban Tree Canopy in Washington, DC in 2015. These data represent detailed urban tree canopy cover in Washington, D.C. The data were derived using remote sensing technologies on aerial imagery from the National Agriculture Imagery Program (NAIP), flown in July 2015, and aerial imagery and LiDAR data from Sanborn and the DC Office of the Chief Technology Officer, flown in April 2015.

Polygons of Urban Tree Canopy in Washington, DC in 2015. These data represent detailed urban tree canopy cover in Washington, D.C. The data were derived using remote sensing technologies on aerial imagery from the National Agriculture Imagery Program (NAIP), flown in July 2015, and aerial imagery and LiDAR data from Sanborn and the DC Office of the Chief Technology Officer, flown in April 2015.

API to retrieve general information on contracts and grants from the City of Montreal. The API serves as the foundation for the contract visualization tool and is also made available for anyone who wants to explore contracts. Note that the data can also be visualized using the [Contract View] tool (https://ville.montreal.qc.ca/vuesurlescontrats/).

World Urban Areas represents the major urban areas of the world as shaded polygons.

Silencio’s Street Noise-Level Dataset provides unmatched value environmental data industry, delivering highly granular noise data to researchers, developers, and governments. Built from over 35 billion datapoints collected globally via our mobile app and refined through AI-driven interpolation, this dataset offers hyper-local average noise levels (dBA) covering streets, neighborhoods, and venues across more than 200 countries.

Our data helps assess the environmental quality of any location, supporting residential and commercial property valuations, site selection, and urban development. By integrating real-world noise measurements with AI-powered models, we enable real estate professionals to evaluate how noise exposure impacts property value, livability, and buyer perception — factors often overlooked by traditional market analyses.

Silencio also operates the largest global database of noise complaints, providing additional context for understanding neighborhood soundscapes from both objective measurements and subjective community feedback.

We offer on-demand visual delivery for mapped cities, regions, or even specific streets and districts, allowing clients to access exactly the data they need. Data is available both as historical and up-to-date records, ready to be integrated into valuation models, investment reports, and location intelligence platforms. Delivery options include CSV exports, S3 buckets, PDF, PNG, JPEG, and we are currently developing a full-featured API, with flexibility to adapt to client needs. We are open to discussion for API early access, custom projects, or unique delivery formats.

Fully anonymized and fully GDPR-compliant, Silencio’s data ensures ethical sourcing while providing real estate professionals with actionable insights for smarter, more transparent valuations.