Buildings and infrastructure data

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2016 vintage; Generalized

Table of Public Works ROW permits issued by HRM Public Works Traffic Management from the new Permitting, Planning, Licensing & Compliance (PPL&C) solution. This dataset contains all permits issued under the permit type of Right of Way Permits not related to HRM Building Permits and/or Construction Permits. Metadata

Metro Facility Benchmarking data is included in this data set including 3 elements: 1. EUI (Energy Use Intensity) values for Metro Nashville Government facilities for Calendar Year 2021 and 2022. EUI is an indicator of the amount of energy consumed on a property annually, in relation to the size of the building. It is a metric that is used to indicate if a building is energy efficient. 2. Metro facility energy consumption for Calendar Year 2021 and 2022. 3. Metro facility energy costs for Calendar Year 2021 and 2022. Source Link: https://www.nashville.gov/departments/general-servicesMetadata Document: Metro Energy Use Intensity Benchmarking Metadata.pdfContact Data Owner: opendata@nashville.gov

The purpose of this feature class is to illustrate the location of proposed water points in public water systems in the Commonwealth of Kentucky as collected by Kentucky’s Area Development Districts. The locations of proposed water points along with the basic attribute data concerning those features, are used by the Kentucky Infrastructure Authority, Area Development Districts, public water systems, and other state/local agencies for the purpose of water infrastructure planning, project development, state and federal funding applications, Kentucky State Clearinghouse, capital improvement plans, data analysis, and for any other purpose that will improve the potable water infrastructure and service in the Commonwealth of Kentucky.

Unified School Districts, Secondary School Districts, Elementary School Districts

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows housing units in structure by tenure (owner or renter). This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   This layer is symbolized by the count and percent of housing units that are single-family detached homes. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B25024, B25032 (Not all lines of ACS table B25032 are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).  The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico   Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

This digital geographic dataset contains point features that represent the Building Permits issued by the City of Miami Building Department since the year 2014. The attribute table contains extensive information regarding the building permit itself, including the applications, status, cost, days, review status and many other items of interest.

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2016 vintage; Generalized

The US Bureau of Justice Statistics describes law enforcement agencies as "publicly funded and employ at least one full-time or part-time sworn officer with general arrest powers". This dataset includes point locations for federal, state, local, and special jurisdiction law enforcement agency facilities. This dataset was created to provide a general purpose database of law enforcement facility locations. Law enforcement facility data can been used to asses whether the total police capability of a given area is adequate and to help local governments identify areas of greatest concern. The data are also a helpful resource for situational awareness planning, emergency response, and route analysis. A version of this data is also used to map Firearm Safe Harbor Storage Locations. The points in this dataset indicate the locations of law enforcement facilities (police stations, sheriff's offices, etc.) throughout the state of Utah. Each point includes attributes such as the facility name, street address, contact information, city, zip, county, and coordinates. Points have been placed on or near the main structure of the facility. UGRC originally derived this dataset from the Homeland Infrastructure Foundation-Level Data (HIFLD) database maintained by the US Bureau of Justice Statistics. The primary source for this database was the 2008 Census of State and Local Law Enforcement Agencies. The data are currently maintained by UGRC in collaboration with the Department of Public Safety's Bureau of Criminal Identification (BCI).

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2016 vintage; Generalized

Correctional facility polygons published by the US Census Bureau for the United States. Each feature represents a correctional facility and includes its name, land and water area, and centroid coordinates.

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows tenure (owner or renter) by type of heating fuel. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   This layer is symbolized to show the predominant type of heating fuel mostly used in occupied housing units. Only predominant values are shown in the legend, but the count of all housing units is represented in the popup. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B25040, B25117 (Not all lines of ACS table B25117 are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico   Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2015 vintage; Generalized

From: MS Buildings Microsoft recently released a free set of deep learning generated building footprints covering the United States of America. In support of this great work and to make these building footprints available to the ArcGIS community, Esri has consolidated the buildings into a single layer and shared them in ArcGIS Online. The footprints can be used for visualization using vector tile format or as hosted feature layer to do analysis.  Learn more about the Microsoft Project at the Announcement Blog or the raw data is available at Github.

​​Students served reports are collected from all funded grantees annually​.  This dataset is no longer updated Source Link: https://www.nashville.gov/departments/artsMetadata Document: Schools Served by Metro Arts Grantees Metadata.pdfContact Data Owner: opendata@nashville.gov

Construction sites in the Hanseatic and University City of Rostock

Census Current (2026) Physical Features Web Map Service; January 1, 2026 vintage

Unified School Districts, Secondary School Districts, Elementary School Districts

Overview This feature service provides building footprint information for Westbank First Nation, supporting a current view of built structures within the community. Feature Layers WFN Buildings — Polygon features representing individual building footprints. The layer includes building classification and detail attributes, along with basic measurements such as height, area, and perimeter-related information, which support planning, asset inventory, and spatial analysis. Geographic Coverage The service covers the Westbank First Nation area in British Columbia, Canada. Use Cases Community and land-use planning Building inventory and asset management Map display and reference for local infrastructure Spatial analysis of building distribution and development License and Attribution This item is owned and maintained by Westbank First Nation (WFN) and made available under the Regional District of Central Okanagan Open Data License and related terms of use and disclaimer.

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2015 vintage; Generalized

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2020 vintage

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2020 vintage

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2023 vintage; Generalized

Military Installations; National Park Service Areas; Correctional Facilities; Colleges and Universities

A comprehensive surveys of existing schools in the Fond-des-Blancs commune and surrounding areas.

First resource: Airstrip Locations and Status by Mission Aviation Fellowship (MAF) in Papua New Guinea. As of September 2022. Second resource: Airport geocoded dataset compiled during the 2018 PNG Earthquake response.

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: building - Part of the country exports on HDX by HOT

A dataset of building footprints of the Reunion Island, in the area of the January 2023 Belal Cyclone. Building footprints are useful for a range of important applications, from population estimation, urban planning and humanitarian response, to environmental and climate science. This large-scale open dataset contains the outlines of buildings derived from high-resolution satellite imagery in order to support these types of uses. For more info visit open buildings FAQ https://sites.research.google/open-buildings/#faq

Nigeria country-wide primary, secondary, and tertiary health care facility points and names and functional status

Thinking Machines Data Science is releasing TM Open Buildings, a dataset of manually-drawn building outlines covering 12 Philippine cities with detailed annotations on building and roof attributes as seen over satellite imagery. We contribute the buildings in OpenStreetMap and also made available for download in Kaggle. This is made possible with the support from the Lacuna Fund.

List of health facilities in Yemen by health facility type - Updated September 2020

MapSwipe results focusing on areas with at least one building. This data is collected through the MapSwipe platform, whereby humans visually check for the presence of buildings using satellite imagery (Esri World Imagery for Myanmar). It combines results from several MapSwipe projects listed below. In the download files below the 'results_yes_maybe' are the results and the original input areas are 'aois'. Source MapSwipe Projects - Myanmar: Find - Buildings - Earthquake Response (2) - Myanmar: Find - Buildings - Earthquake Response (3) - Myanmar: Find - Buildings - Earthquake Response (4) - Myanmar: Find - Buildings - Earthquake Response (5) - Myanmar: Find - Buildings - Earthquake Response (6) - Myanmar: Find - Buildings - Earthquake Response (7) - Myanmar: Find - Buildings - Earthquake Response (8)

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['aeroway'] IS NOT NULL OR tags['building'] = 'aerodrome' OR tags['emergency:helipad'] IS NOT NULL OR tags['emergency'] = 'landing_site' Features may have these attributes: - name - name:en - aeroway - building - emergency - emergency:helipad - operator:type - capacity:persons - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['amenity'] IN ('kindergarten', 'school', 'college', 'university') OR tags['building'] IN ('kindergarten', 'school', 'college', 'university') Features may have these attributes: - name - name:en - amenity - building - operator:type - capacity:persons - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Humanitarian OpenStreetMap Team (HOT) conducted a pilot of Crowdsourced Damage Assessment (CDA) with local mappers from the Myanmar OpenStreetMap Community (myOSM) to identify and map damaged buildings in priority affected townships from the March 2025 Myanmar earthquake. Through the CDA, mappers will review damaged buildings by comparing pre- and post-disaster satellite imageries (from Maxar and Planet) to identify signs of damages. These were reviewed, mapped, and validated with open-source tools MapSwipe, HOT Tasking Manager, and MapRoulette. Damage assessments were conducted in 20 townships, including: Bago, Magway, Aungmyaythazan, Chanayethazan, Mahaaungmyay, Chanmyathazi, Pyigyitagon, Amarapura, Sintgaing, Kyaukse, Meiktila, Thazi, Patheingyi, Za Bu Thi Ri, Det Khi Na Thi Ri, Pyinmana, Poke Ba Thi Ri, Oke Ta Ra Thi Ri, Lewe, and Sagaing. This dataset includes all OpenStreetMap features (nodes, ways, and relations) within Myanmar that contain any tag key related to “damage”.) Features may have these attributes: - name - building - damage - damage:event - source:damage - source:damage:date - addr:street - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Listado de centros educativos de Honduras, desagregado por municipios y haciendo diferenciación entre centros educativos rurales y urbanos.