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3,709 results for “urbanization.”
Global Grid of Probabilities of Urban Expansion to 2030
The Global Grid of Probabilities of Urban Expansion to 2030 presents spatially explicit probabilistic forecasts of global urban land cover change from 2000 to 2030 at a 2.5 arc-minute resolution. For each grid cell that is non-urban in 2000, a Monte-Carlo model assigned a probability of becoming urban by the year 2030. The authors first extracted urban extent circa 2000 from the NASA MODIS Land Cover Type Product Version 5, which provides a conservative estimate of global urban land cover. The authors then used population densities from the Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) to create the population density driver map. They estimated the amount of new urban land in each United Nations region by 2030 in a Monte-Carlo fashion based on present empirical distribution of regional urban population densities and probability density functions of projected regional population and GDP values for 2030. To facilitate integration with other data products, CIESIN reprojected the data from Goode's Homolosine to Geographic WGS84 projection.
Global Monthly and Seasonal Urban and Land Backscatter Time Series, 1993-2020
The Global Monthly and Seasonal Urban and Land Backscatter Time Series, 1993-2020, is a multi-sensor, multi-decadal, data set of global microwave backscatter, for 1993 to 2020. It assembles data from C-band sensors onboard the European Remote Sensing Satellites (ERS-1 and ERS-2) covering 1993-2000, Advanced Scatterometer (ASCAT) onboard EUMETSAT satellites for 2007-2020, and the Ku-band sensor onboard the QuikSCAT satellite for 1999-2009, onto a common spatial grid (0.05 degree latitude /longitude resolution) and time step (both monthly and seasonal). Data are provided for all land (except high latitudes and islands), and for urban grid cells, based on a specific masking that removes grid cells with > 50% open water or < 20% built land. The all-land data allows users to choose and evaluate other urban masks. There is an offset between C-band and Ku-band backscatter from both vegetated and urban surfaces that is not spatially constant. There is a strong linear correlation (overall R-squared value = 0.69) between 2015 ASCAT urban backscatter and a continental-scale gridded product of building volume, across 8,450 urban grid cells (0.05 degree resolution) from large cities in Europe, China, and the United States.
Global High Resolution Daily Extreme Urban Heat Exposure (UHE-Daily), 1983-2016
The Global High Resolution Daily Extreme Urban Heat Exposure (UHE-Daily), 1983-2016 data set contains a high-resolution, longitudinal global record of geolocated urban extreme heat events and urban population exposure estimates for more than 10,000 urban settlements worldwide for 1983-2016. Urban extreme heat events and urban population exposure are identified for each urban settlement in the data record for five combined temperature-humidity thresholds: two-day or longer periods where the daily maximum Heat Index (HImax) > 40.6 �C; one-day or longer periods where HImax > 46.1 �C; and one day or longer periods where the daily maximum Wet Bulb Globe Temperature (WBGTmax) > 28 �C, 30 �C, and 32 �C. The WBGTmax thresholds follow the International Standards Organization (ISO) criteria for risk of occupational heat related heat illness, whereas the HImax thresholds follow the U.S. National Weather Services' definition for an excessive heat warning. For each criteria, across urban settlements worldwide, the data set also contains the duration, intensity, and severity of each urban extreme heat event.
Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): National Administrative Boundaries
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): National Administrative Boundaries are derived from the land area grid to show the outlines of pixels (cells) that contain administrative Units in GRUMPv1 on a per-country/territory basis. They are derived from the pixels as polygons and thus have rectilinear boundaries at a large scale. The polygons that outline the countries and territories are not official representations; rather they represent the area covered by the statistical data as provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).
Global Urban Polygons and Points Dataset (GUPPD), Version 1
The Global Urban Polygons and Points Dataset (GUPPD), Version 1 is a global data set of 123,034 urban settlements with place names and population for the years 1975-2030 in five-year increments. The data set builds on and expands the European Commission, Joint Research Centre's (JRC) 2015 Global Human Settlement (GHS) Urban Centre Database (UCDB). The JRC Settlement Model (GHS-SMOD) data set includes a hierarchy of urban settlements, from urban centre (level 30), to dense urban cluster (level 23), to semi-dense urban cluster (level 22). The UCDB only includes level 30, whereas the GUPPDv1 adds levels 23 and 22, and uses open data sources to both check and validate the names that JRC assigned to its UCDB polygons and to label the newly added settlements. The methodology described in the documentation was able to consistently label a greater percentage of UCDB polygons than were previously labeled by JRC.
Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100
The Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of 1-km (about 30 arc-seconds). An algorithm was developed and validated to downscale the 1/8-degree resolution data set to 1-km resolution. For a given decade, the downscaling algorithm allocates the 1/8-degree decadal amount of urban land expansion to 1-km grid cells in proportion to their total urban land amounts at the beginning of the decade. The algorithm uses an iterative process to collect any overflows from already highly-developed 1-km grid cells, and then allocates them to 1-km grid cells that are not yet fully developed. This iterative process repeats itself until all 1/8-degree amounts of urban land expansion are allocated to 1-km grid cells with no overflow. The downscaling process is applied decade by decade throughout the 21st century for each urban land expansion scenario. The final product is a set of global maps displaying the 1-km fraction of urban land, updated at decadal intervals throughout the 21st century, for five different urban land expansion scenarios consistent with the Shared Socioeconomic Pathways (SSPs).
Low Elevation Coastal Zone (LECZ) Global Delta Urban-Rural Population and Land Area Estimates, Version 1
The Low Elevation Coastal Zone (LECZ) Global Delta Urban-Rural Population and Land Area Estimates, Version 1 data set provides country-level estimates of urban, quasi-urban, rural, and total population (count), land area (square kilometers), and built-up areas in river delta- and non-delta contexts for 246 statistical areas (countries and other UN-recognized territories) for the years 1990, 2000, 2014 and 2015. The population estimates are disaggregated such that compounding risk factors including elevation, settlement patterns, and delta zones can be cross-examined. The Intergovernmental Panel on Climate Change (IPCC) recently concluded that without significant adaptation and mitigation action, risk to coastal commUnities will increase at least one order of magnitude by 2100, placing people, property, and environmental resources at greater risk. Greater-risk zones were then generated: 1) the global extent of two low-elevation zones contiguous to the coast, one bounded by an upper elevation of 10m (LECZ10), and one by an upper elevation of 5m (LECZ05); 2) the extent of the world's major deltas; 3) the distribution of people and built-up area around the world; 4) the extents of urban centers around the world. The data are layered spatially, along with political and land/water boundaries, allowing the densities and quantities of population and built-up area, as well as levels of urbanization (defined as the share of population living in "urban centers") to be estimated for any country or region, both inside and outside the LECZs and deltas, and at two points in time (1990 and 2015). In using such estimates of populations living in 5m and 10m LECZs and outside of LECZs, policymakers can make informed decisions based on perceived exposure and vulnerability to potential damages from sea level rise.
Historical Urban Population: 3700 BC - AD 2000
The Historical Urban Population, 3700 BC - AD 2000, originally developed by the Yale School of Forestry & Environmental Studies, is the first spatially explicit global data set containing location and size of urban populations over the last 6,000 years. The data set was created by digitizing, transcribing, and geocoding historical, archaeological, and census-based urban population data. Each data point consists of a city name, latitude, longitude, year, population, and a reliability ranking to assess the geographic uncertainty of each data point. Despite spatial and temporal gaps, no other geocoded data set at this resolution exists. It can therefore be used to investigate long-term historical urbanization trends and patterns, evaluate the current era of urbanization, and build a richer record of urban population through history.
Yale Center for Earth Observation (YCEO) Surface Urban Heat Islands, Version 4, 2003-2018
The Yale Center for Earth Observation (YCEO) Surface Urban Heat Islands, Version 4, 2003-2018 includes annual, summertime, and wintertime Surface Urban Heat Island (SUHI) intensities for daytime and nighttime for over 10,000 global urban extents. This global SUHI data set was created using the Simplified Urban-Extent (SUE) algorithm and is available at the pixel and urban cluster-levels (i.e. at the level of larger urban agglomerations). Monthly composites are also available as urban cluster means. A summary of older versions, including changes from the data set created and analyzed in the originally published manuscript (Chakraborty and Lee, 2019) can be found on the YCEO Global Surface UHI Explorer website (https://yceo.yale.edu/research/global-surface-uhi-explorer).
Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Density Grid
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Density Grid estimates population per square km for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic Units, is used to assign population values to grid cells. The population count grids are divided by the land area grid to produce population density grids. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).
Global Urban Heat Island (UHI) Data Set, 2013
The Urban Heat Island (UHI) effect represents the relatively higher temperatures found in urban areas compared to surrounding rural areas owing to higher proportions of impervious surfaces and the release of waste heat from vehicles and heating and cooling systems. Paved surfaces and built structures tend to absorb shortwave radiation from the sun and release long-wave radiation after a lag of a few hours. The Global Urban Heat Island (UHI) Data Set, 2013, estimates the land surface temperature within urban areas in degrees Celsius (average summer daytime maximum and average summer nighttime minimum) as well as the difference between those temperatures and the temperatures in surrounding rural areas, defined as a 10km buffer around the urban extent. Urban extents are from SEDAC�s Global Rural-Urban Mapping Project, Version 1 (GRUMPv1), and land surface temperatures are from SEDAC�s Global Summer Land Surface Temperature (LST) Grids, 2013, which are derived from the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime Land Surface Temperature (LST) 8-day composite data (MYD11A2). For most regions, the UHI data set provides the average daytime maximum (1:30 p.m. overpass) and average nighttime minimum (1:30 a.m. overpass) temperatures in urban and rural areas, and the urban-rural temperature differences, derived from LST data representing a 40-day time-span during July-August (Julian days 185-224) in the northern hemisphere and January-February (Julian days 001-040) in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).
Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid
The Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid data set provides gridded data on human population (GHS-POP), built-up area (GHS-BUILT), and degree of urbanization (GHS-SMOD) across four time periods: 1975, 1990, 2000, and 2014 (BUILT) or 2015 (POP, SMOD). GHS-BUILT describes the percent built-up area for each 30 arc-second grid cell (approximately 1 km at the equator) based on Landsat imagery from each of the four time periods. GHS-POP consists of census data from the 2010 round of global census from Gridded Population of the World, Version 4, Revision 10 (GPWv4.10) spatially-allocated within census Units based on the percent built-up areas from GHS-BUILT. GHS-SMOD uses GHS-BUILT and GHS-POP in order to develop a standardized classification of degree of urbanization grid. The original data from the Joint Research Centre of the European Commission (JRC-EC) has been combined into a single data package in GeoTIFF format and reprojected from Mollweide Equal Area into WGS84 at 9 arc-second and 30 arc-second horizontal resolutions in order to support integration with a variety of global raster data sets.
Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points contains geospatial location for selected settlements. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).
Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Count Grid
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Count Grid estimates human population for the years 1990, 1995, and 2000 by 30 arc-second (1 km) grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic Units, is used to assign population values (counts, in persons) to grid cells. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).
SDG Indicator 11.7.1: Urban Public Space, Availability and Access, 2023 Release
The SDG Indicator 11.7.1: Urban Public Space, Availability and Access, 2023 Release, part of the SDGI collection, measures the average share of the built-up area of a city that is open space for public use for all. UN SDG 11 is "make cities and human settlements inclusive, safe, resilient and sustainable". Aside from environmental benefits, public space can also help improve public health, bolster commUnity, and encourage economic exchange. As one measure of progress towards SDG 11, the UN has established SDG indicator 11.7.1. The indicator was computed by measuring both the proportion of OpenStreetMap (OSM) public space within a given urban center and the proportion of WorldPop gridded population within 400 meters to Open Public Space (OPS). Cities were delineated using the European Commission Joint Research Centre (JRC) Urban Center Database (GHS-UCDB). The SDG indicator 11.7.1 data set provides estimates of the average share of the built-up area of cities that is open space for public use for all for 8,873 urban centers across 180 countries.
Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points, Revision 01
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points, Revision 01 is an updated version of the Settlement Points, Version 1 (v1) used in the original population reallocation. Revision 01 includes improved geospatial location for selected settlements, as well as new georeferenced settlements. Revision 01 was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the CUNY Institute for Demographic Research (CIDR).
Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Coastlines
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Coastlines data are derived from the land area grids to show the outlines of pixels (cells) that contain administrative Units in GRUMPv1 that fall along waterbodies. The coastlines are designed for cartographic use with the GRUMPv1 population raster data sets. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).
Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2
The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2 data set consists of country-level estimates of urban population, rural population, total population and land area country-wide and in LECZs for years 1990, 2000, 2010, and 2100. The LECZs were derived from Shuttle Radar Topography Mission (SRTM), 3 arc-second (~90m) data which were post processed by ISciences LLC to include only elevations less than 20m contiguous to coastlines; and to supplement SRTM data in northern and southern latitudes. The population and land area statistics presented herein are summarized at the low coastal elevations of less than or equal to 1m, 3m, 5m, 7m, 9m, 10m, 12m, and 20m. Additionally, estimates are provided for elevations greater than 20m, and nationally. The spatial coverage of this data set includes 202 of the 232 countries and statistical areas delineated in the Gridded Rural-Urban Mapping Project version 1 (GRUMPv1) data set. The 30 omitted areas were not included because they were landlocked, or otherwise lacked coastal features. This data set makes use of the population inputs of GRUMPv1 allocated at 3 arc-seconds to match the SRTM elevations, and at 30 arc-seconds resolution in order to reflect uncertainty levels in the product resulting from the interplay of input population data resolutions (based on census Units) and the elevation data. Urban and rural areas are differentiated by the GRUMPv1 Urban Extents. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
Georeferenced Population Datasets of Mexico (GEO-MEX): Urban Place Time-Series Population of Mexico
The Urban Place Time-Series Population of Mexico contains population counts for more than 700 urban centers every 10 years from 1921 through 1990. The urban centers include metropolitan, conurbation, and city areas with more than 5,000 inhabitants as of 1980. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
Urban Landsat: Cities from Space
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.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.