PDF maps of building ages. Created 3/2018 by Spatial Alternatives for The Ad-Hoc Committee on Auburn's Agriculture and Natural Resource. Building ages from Auburn assessing data.

Block and block area-related allocation of predominant building age classes (decades) of residential buildings at the level of the base map 1: 5,000 (ISU5, spatial reference environmental atlas 2010).

A list of buildings identified by building permit records and/or visual identification as buildings that may require strengthening to increase safety during an earthquake.Inclusion of a building on this list is not confirmation that the building is structurally deficient, hazardous or unsafe.These buildings display characteristics such as age, appearance, construction material, method of design and construction, and structural records that may indicate a need for strengthening in preparation for an earthquake.Buildings appearing on this list will receive notice from the City of Santa Monica to complete a structural analysis. Buildings found to be non-compliant with established standards for earthquake resistance will be ordered to strengthen the building through a seismic retrofit.Questions?Contact the Building and Safety divisionPhone: (310) 458-8355Email: seismic@smgov.netWeb: https://www.smgov.net/Departments/PCD/Programs/Seismic-Retrofit/

Building Age Distribution based on the University of Florida's GeoPlan layer dissolved by year/decade for St Johns County

Colonial Building Footprint Context Map - City of St. Augustine.Building age data (by parcel development) with historical building footprints and current building footprints.

This data respository includes the following datasets:

1. Building stock map 1920 (BSM_1920.shp) and its attribute table (BSM_1920_attribute_table.csv)
2. Areas out of scope 1920 (AOOS_1920.shp)
3. Scope of analog building age map 1920 (SABAM_1920.shp)

NYS Building Footprints - metadata info:The New York State building footprints service contains building footprints with address information. The footprints have address point information folded in from the Streets and Address Matching (SAM - https://gis.ny.gov/streets/) address point file. The building footprints have a field called “Address Range”, this field shows (where available) either a single address or an address range, depending on the address points that fall within the footprint. Ex: 3860 Atlantic Avenue or Ex: 32 - 34 Wheatfield Circle Building footprints in New York State are from four different sources: Microsoft, Open Data, New York State Energy Research and Development Authority (NYSERDA), and Geospatial Services. The majority of the footprints are from NYSERDA, except in NYC where the primary source was Open Data. Microsoft footprints were added where the other 2 sources were missing polygons. Field Descriptions: NYSGeo Source : tells the end user if the source is NYSERDA, Microsoft, NYC Open Data, and could expand from here in the futureAddress Point Count: the number of address points that fall within that building footprintAddress Range : If an address point falls within a footprint it lists the range of those address points. Ex: if a building is on a corner of South Pearl and Beaver Street, 40 points fall on the building, and 35 are South Pearl Street it would give the range of addresses for South Pearl. We also removed sub addresses from this range, primarily apartment related. For example, in above example, it would not list 30 South Pearl, Apartment 5A, it would list 30 South Pearl.Most Common Street : the street name of the largest number of address points. In the above example, it would list “South Pearl” as the most common street since the majority of address points list it as the street. Other Streets: the list of other streets that fall within the building footprint, if any. In the above example, “Beaver Street” would be listed since address points for Beaver Street fall on the footprint but are not in the majority.County Name : County name populated from CIESINs. If not populated from CIESINs, identified by the GSMunicipality Name : Municipality name populated from CIESINs. If not populated from CIESINs, identified by the GSSource: Source where the data came from. If NYSGeo Source = NYSERDA, the data would typically list orthoimagery, LIDAR, county data, etc.Source ID: if NYSGeo Source = NYSERDA, Source ID would typically list an orthoimage or LIDAR tileSource Date: Date the footprint was created. If the source image was from 2016 orthoimagery, 2016 would be the Source Date. Description of each footprint source:NYSERDA Building footprints that were created as part of the New York State Flood Impact Decision Support Systems https://fidss.ciesin.columbia.edu/home Footprints vary in age from county to county.Microsoft Building Footprints released 6/28/2018 - vintage unknown/varies. More info on this dataset can be found at https://blogs.bing.com/maps/2018-06/microsoft-releases-125-million-building-footprints-in-the-us-as-open-data.NYC Open Data - Building Footprints of New York City as a polygon feature class. Last updated 7/30/2018, downloaded on 8/6/2018. Feature Class of footprint outlines of buildings in New York City. Please see the following link for additional documentation- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.mdSpatial Reference of Source Data: UTM Zone 18, meters, NAD 83. Spatial Reference of Web Service: Spatial Reference of Web Service: WGS 1984 Web Mercator Auxiliary Sphere.

November 2024

The Chicago Historic Resources Survey (CHRS), completed in 1995, was a decade-long research effort by the City of Chicago to analyze the historic and architectural importance of all buildings, objects, structures, and sites constructed in the city prior to 1940. During 12 years of field work and follow-up research that started in 1983, CHRS surveyors identified approximately 9,900 properties which were considered to have some historic or architectural importance. Please note that this CHRS dataset is limited and does not include the entire survey: A color-coded ranking system was used to identify historic and architectural significance relative to age, degree of external physical integrity, and level of possible significance. This dataset only includes buildings identified with the two highest color codes: "Red" and "Orange." Buildings and structures coded "Red" or "Orange" (unless designated as a Chicago Landmark or located within a Chicago Landmark District) are subject to the City of Chicago’s Demolition-Delay Ordinance (link to: http://www.cityofchicago.org/city/en/depts/dcd/supp_info/demolition_delay.html), adopted by City Council in 2003. Only buildings are included in this dataset; structures and objects such as bridges, park structures, monuments and mausoleums, generally are not represented. Likewise, garages, coach houses, and other secondary structures associated with a building may not be consistently depicted or color-coded. If an “Orange”- or “Red”-rated building was demolished after 2008, it may still appear in the map. The CHRS occasionally rated only part of a building or part of a group of joined buildings as “Orange” or “Red;” however the entire building or group of joined buildings may be incorrectly identified as “Orange” or “Red.” Additional information about the CHRS is available at www.cityofchicago.org/Landmarks/ or by contacting the Historic Preservation Division at (312) 744-3200. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS or QGIS, is required. To download this file, right-click the "Download" link above and choose "Save link as."

This web map shows the locations and information and age records of private buildings including address, building type, building usage, block ID, occupation permit date and occupation permit number in Hong Kong. It is created by Buildings Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of CSDI Portal at https://portal.csdi.gov.hk.

View item in Open Data portalThis dataset contains polygons representing building footprints for the City of Moncton. The BUILDINGS dataset contains building outlines, building types and elevation values. The dataset was collected originally from 2004 orthophotography and updated using orthophotography from subsequent years.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Zimbabwe: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49). Methodology These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click here. For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/ Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found here

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Mali: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49). Methodology These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click here. For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/ Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found here

Scan of the map "Building age 1992/93" from the publication: "Urban development of Berlin since 1650 in maps"

A list of buildings identified by building permit records and/or visual identification as buildings that may require strengthening to increase safety during an earthquake.Inclusion of a building on this list is not confirmation that the building is structurally deficient, hazardous or unsafe.These buildings display characteristics such as age, appearance, construction material, method of design and construction, and structural records that may indicate a need for strengthening in preparation for an earthquake.Buildings appearing on this list will receive notice from the City of Santa Monica to complete a structural analysis. Buildings found to be non-compliant with established standards for earthquake resistance will be ordered to strengthen the building through a seismic retrofit.Questions?Contact the Building and Safety divisionPhone: (310) 458-8355Email: seismic@smgov.netWeb: https://www.smgov.net/Departments/PCD/Programs/Seismic-Retrofit/

Building Footprint is a Polygon FeatureClass representing the building footprints for the City of Cupertino, California. The mapped geographic area includes 11.3 square miles of western Santa Clara County in California. The building footprints data layer was originally based on aerial photographs from 2011. Continual updates are made as needed. Most updates come from digitized plat/plan approvals or from completed City project plans. Mapping accuracy meets National Map Accuracy Standards for +/-2.5 US feet. Spatial coordinate system is California State Plane West, zone III Fipszone 0403 Adszone 3326, NAD83. Scale of true display is 1:1200 (100' scale). Building Footprints has the following fields: OBJECTID: Unique identifier automatically generated by Esri type: OID, length: 4, domain: none

LEVEL_DESC: A general description of what type of structure the polygon represents type: String, length: 18, domain: none

BLDG_HIGH: The height of the highest point on the polygon - feet above sea level type: String, length: 50, domain: none

BLDG_LOW: The height of the lowest point on the polygon - feet above see level type: String, length: 50, domain: none

FloorNumbe: The number of floors the building has type: Integer, length: 4, domain: none

AssetID: Cupertino maintained GIS primary key type: String, length: 50, domain: none

Year_Built: The year the building was built type: Date, length: 8, domain: none

Bldg_Age: The age of the building type: Single, length: 4, domain: none

LegacyID: Old identifiers used to track asset migration type: Integer, length: 4, domain: none

Shape: Field that stores geographic coordinates associated with feature type: Geometry, length: 4, domain: none

GlobalID: Unique identifier automatically generated for features in enterprise database type: GlobalID, length: 38, domain: noneShape.STArea():The area of the building footprinttype: double, length: none, domain: none Shape.STLength(): The length of the perimeter of the building footprinttype: double, length: none, domain: none BLDG_HEIGHT: The height of the building, calculated by subtracting the highest and lowest points type: double, length: none, domain: none

last_edited_date: The date the database row was last updated type: Date, length: 8, domain: none

created_date: The date the database row was initially created type: Date, length: 8, domain: none

VERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Nigeria: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Methodology

These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click [here](https://dataforgood.fb.com/docs/methodology-high-resolution-population-density-maps-demographic-estimates/

For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/

Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found here

VERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Niger: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49). Methodology These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click here. For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/ Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found here

Abstract

The building attribute data was collected by citizens as part of the citizen science project “Colouring Dresden”. Main goal was to collect information about buildings (like building age, usage, number of storeys, roof shape etc.) in an interactive online map. Different action formats (like dialogue series, presentations, mapathons, hackathons, and monthly meetings) were organised to bring citizens into action and discuss building-related topics. Focus was the sustainable construction of buildings and the question how good current buildings of the city are prepared for natural catastrophes like floods, heavy rain incidents or heat stress. The project period was from October 2022 to September 2023 and the mapping platform was launched in March 2023.

Project page: https://colouring.dresden.ioer.info/

The project is part of the international research network “Colouring Cities Research Programme” (CCRP) and the first local project in Germany. Colouring Dresden is currently coordinating the European Hub of CCRP.

The project was led by Leibniz Institute of Ecological Urban and Regional Development Dresden, Germany in cooperation with:

•  Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden (SLUB) - Regionalportal Saxorum
  

•  Bund Deutscher Architektinnen und Architekten (BDA)
  

•  Zentrum für Baukultur Sachsen (ZfBK)
  

•  Technische Sammlungen Dresden (TSD) / DLR_School_Lab TU Dresden
  

•  Zentralbibliothek der Städtische Bibliotheken Dresden (SBD)
  

Data description

The data contains several files:

•  building_atributes_geometry_20231001.gpkg: building geometries and building attributes in Geopackage file format
  

•  building_attributes.csv: building attributes
  

•  building_verification.csv: building verification data (crowd-sourced)
  

•  edit_history.csv: edit history of collected building_attributes
  

•  README.md
  

Building geometries:

Data source: 3D-Stadtmodell (3D city model) LoD2, Open Data Dresden

URL: https://daten.dresden.de/SONST/Datenbeschreibung_3D_Gebaeude_Dresden_OpenData.pdf

Contact: opendata@dresden.de

Data license: Data license Germany – Attribution – Version 2.0 https://www.govdata.de/dl-de/by-2-0., Attribution: “Datenquelle: Landeshauptstadt Dresden, dl-de/by-2-0, opendata.dresden.de”

Spatial reference: WGS 84 / Web Mercator (EPSG: 3857)

Spatial extent: City of Dresden, Germany

Number of buildings: 135598

Completeness: Almost complete. Recently built buildings are not included.

Positional accuracy: Building footprints based on official cadastre data (ALKIS)

Up-to-dateness: Data is from 4th of May, 2023. Recently built buildings are not included.

Building attributes:

(Including building verification and edit history)

Data source: crowd-sourced collected building attributes from citizen science project “Colouring Dresden”, led by Leibniz Institute of Ecological Urban and Regional Development Dresden, Germany

Data collection: most attributes were collected by citizens using Citizen Science platform “Colouring Dresden”. Some attributes (address, centroid or identifier of external datasets like OpenStreetMap, Wikipedia or Wikidata) were linked automated.

URL: https://colouring.dresden.ioer.info/

Contact: Dr Robert Hecht, https://www.ioer.de/institut/beschaeftigte/hecht

Data license: Open Data Commons Open Database License (ODbL), Attribution: “Colouring Dresden contributors”, https://opendatacommons.org/licenses/odbl/

Spatial extent: City of Dresden, Germany

Number of buildings: 135598

Completeness:

Completeness of collected building attributes differs and depends on mapping activities of the Citizen Scientists from March to September in 2023. Completeness of automated linked attributes is 47.6% or more. Completeness of crowd-sourced attributes is up to 5.3%.

100.0% building_id; ref_toid; location_latitude; location_longitude; revision_id; size_height_apex

47.6% location_number; location_street; location_postcode; location_town

5.3% building_attachment_form

2.0% is_domestic

1.1% size_storeys_core

1.0% architectural_style; architectural_style_source; use_building_current

0.6% size_storeys_attic; size_roof_shape

0.3% use_building_origin

And more attributes

Up-to-dateness: the data extract is from 1st of October, 2023 and includes the collecting period from March to September in 2023.

These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom's Foreign, Commonwealth & Development Office (INV-009579, formerly OPP1182425), and GRID3 COVID-19 Support Scale-up (INV-018067). Project partners included the United Nations Population Fund, Center for International Earth Science Information Network in the Columbia Climate School at Columbia University, and the Flowminder Foundation. The new age-structured population estimates are based on the existing Census-based gridded population estimates for Burkina Faso (2019), version 1.0 (WorldPop and Institut National de la Statistique et de la Demographie du Burkina Faso, 2020). Duygu Cihan, Heather Chamberlain and Thomas Abbott led the data processing, with advice from Édith Darin.RELEASE CONTENT Aggregated_BFA_under18_population_100m.tif Aggregated_BFA_18_45_population_100m.tif Aggregated_BFA_over45_population_100m.tifFILE DESCRIPTIONS The coordinate system for all GIS files is the geographic coordinate system WGS84 (World Geodetic System 1984, EPSG: 4326). Aggregated_BFA_ under18_population _100m.tifThis geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total population of persons aged under 18 (0-17) per grid cell across Burkina Faso. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.Aggregated_BFA_18_45_population_100m.tif This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total population of persons aged 18 to 45 (18-45) per grid cell across Burkina Faso. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas. Aggregated_BFA_over45_population_100m.tif This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total population of persons aged over 45 (46+) per grid cell across Burkina Faso. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.METHODS OVERVIEW Processing: The existing 2019 gridded population estimates (WorldPop and Institut National de la Statistique et de la Demographie du Burkina Faso, 2020) include age- and sex- structured population estimates for 5 year age classes, based on the age and sex breakdown of population totals at the national level, from the preliminary census results. A Sprague multiplier approach was used to further disaggregate the 5-year age classes at the national level, to create three custom age-classes (under 18, 18-45 and over 45). The population for each of these custom age classes, was calculated as the proportion of the total population at the national level. This proportion was applied to the count of total population at the grid cell level.ASSUMPTIONS AND LIMITATIONS The custom age classes are estimated using a Sprague multiplier approach to interpolate the 5-year age classes and provide the population for a single year age class, which is then summed to provide the custom age classes. Interpolation introduces uncertainty in the estimates.The population estimates for the custom age classes were calculated from national level totals for 5-year age classes. A constant age-structure across all grid cells was assumed in applying the national proportions for the custom age classes to the grid cell level.RELEASE HISTORYVersion 1.0 (25/05/2022) - Original release of this data set.WORKS CITEDDooley, C. A. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.0. University of Southampton: Southampton, UK. Source of building Footprints “Ecopia Vector Maps Powered by Maxar Satellite Imagery”© 2020. https://dx.doi.org/10.5258/SOTON/WP00666.WorldPop and Institut National de la Statistique et de la Demographie du Burkina Faso. 2020. Census-based gridded population estimates for Burkina Faso (2019), version 1.0. WorldPop, University of Southampton. https://dx.doi.org/10.5258/SOTON/WP00687

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by the Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646