Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherTAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)

This is a compiled geospatial dataset in ESRI polygon shapefile format of ultramafic soils of the Americas showing the location of ultramafic soils in Canada, the United States of America, Mexico, Guatemala, Cuba, Dominican Republic, Puerto Rico, Costa Rica, Colombia, Argentina, Chile, Venezuela, Ecuador, Brazil, Suriname, French Guiana, and Bolivia. The R code used to compile the dataset as well as an image of the compiled dataset are also included. , The data are derived from ten geospatial datasets. Original datasets were subset to include only ultramafic areas, datasets were assigned a common projection (WGS84), attribute tables were reconciled to a common set of fields, and the datasets were combined., , README: Geodatabase of ultramafic soils of the Americas

Author: Catherine Hulshof, Virginia Commonwealth University, cmhulshof@vcu.edu

Abstract: This is a compiled geospatial dataset in ESRI polygon shapefile format of ultramafic soils of many countries in the Americas showing the location of ultramafic soils in Canada, the United States of America, Guatemala, Cuba, Dominican Republic, Puerto Rico, Costa Rica, Colombia, Argentina, Chile, Venezuela, Ecuador, Brazil, Suriname, French Guiana, and Bolivia. The data are derived from nine geospatial datasets. Original datasets were subset to include only ultramafic areas, datasets were assigned a common projection (WGS84), attribute tables were reconciled to a common set of fields, and the datasets were combined.

Contents: The data are in ESRI shapefile format and thus have four components with extensions .shp, .shx, .prj, and .dbf. The .shp file contains the feature geometries, the .prj file contains the geographic coordin...

Description

This dataset consist of two vector files which show the change in the building stock of the City of DaNang retrieved from satellite image analysis. Buildings were first identified from a Pléiades satellite image from 24.10.2015 and classified into 9 categories in a semi-automatic workflow desribed by Warth et al. (2019) and Vetter-Gindele et al. (2019).

In a second step, these buildings were inspected for changes based on a second Pléiades satellite image acquired on 13.08.2017 based on visual interpretation. Changes were also classified into 5 categories and aggregated by administrative wards (first dataset: adm) and a hexagon grid of 250 meter length (second dataset: hex).

The full workflow of the generation of this dataset, including a detailled description of its contents and a discussion on its potential use is published by Braun et al. 2020: Changes in the building stock of DaNang between 2015 and 2017

Contents

Both datasets (adm and hex) are stored as ESRI shapefiles which can be used in common Geographic Information Systems (GIS) and consist of the following parts:

shp: polygon geometries (geometries of the administrative boundaries and hexagons)

dbf: attribute table (containing the number of buildings per class for 2015 and 2017 and the underlying changes (e.g. number of new buildings, number of demolished buildings, ect.)

shx: index file combining the geometries with the attributes

cpg: encoding of the attributes (UTF-8)

prj: spatial reference of the datasets (UTM zone 49 North, EPSG:32649) for ArcGIS

qpj: spatial reference of the datasets (UTM zone 49 North, EPSG:32649) for QGIS

lyr: symbology suggestion for the polygons(predefined is the number of local type shophouses in 2017) for ArcGIS

qml: symbology suggestion for the polygons (predefined is the number of new buildings between 2015 and 2017) for QGIS

Citation and documentation

To cite this dataset, please refer to the publication

Braun, A.; Warth, G.; Bachofer, F.; Quynh Bui, T.T.; Tran, H.; Hochschild, V. (2020): Changes in the Building Stock of Da Nang between 2015 and 2017. Data, 5, 42. doi:10.3390/data5020042

This article contains a detailed description of the dataset, the defined building type classes and the types of changes which were analyzed. Furthermore, the article makes recommendations on the use of the datasets and discusses potential error sources.

This directory contains the following maps in GeoTIFF format: open-water frequency, time-series of reflectance for each band (Red and NIR), and frequency of SSSC classes.

Fassoni-Andrade, A. C., & Paiva, R. C. D. (2019). Mapping spatial-temporal sediment dynamics of river-floodplains in the Amazon. Remote Sensing of Environment, 221, 94-107.

https://www.sciencedirect.com/science/article/abs/pii/S0034425718305005

Corresponding autor: alice.fassoni@ufrgs.br

1) Data Description:

1.1) Spatial Representation Type: Raster Format: TIFF Columns and rows: 7661, 2405 Cell Size (X,Y): 0.002245, 0.002245 (~250m)

1.2) Extent (coordinate system): Top: -0.025 Left: -67.28 Right: -50.081055 Botton: -5.424225

1.3) Spatial Reference Properties (GCS_WGS_1984.prj file): Type: Geographic Geographic Coordinate Reference: WGS 1984 Open Geospatial Consortium (OGC) Well Known Text (WKT): GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137.0,298.257223563]], PRIMEM["Greenwich",0.0], UNIT["Degree",0.0174532925199433], AUTHORITY["EPSG",4326]]

2) Data description for individual files:

2.1) File: open_water_frequency.tif This map indicates for how long, during 15 years (2003-2017), each pixel in rivers and lakes of central Amazon basin remained as open-water at every four days. Number of Bands: 1 Values between 0 and 100. Pixel Type: floating point Pixel Depth: 32 bit No Data Value: 0

2.2) File: class_SSC_frequency.tif This map represents a 15-year frequency (2003-2017) at which each pixel in rivers and lakes of central Amazon basin remains in one of the surface suspended sediments concentration classes (SSSC): high, moderate, and low. The open-water frequency map must be considered to interpret the sediments temporal dynamics in the class frequency map. For example, a pixel in the floodplain lake with frequency of 10, 30, and 20% in SSSC classes low, medium and high respectively, remains 40% of the time as no open-water. Number of Bands: 3 band 1: low SSSC class; band 2: Moderate SSSC class; band 3: High SSSC class Composition of bands for best visualization: R(3)G(2)B(1) without contrast Values between 0 and 100. Pixel Type: double precision Pixel Depth: 64 bit No Data Value: 0

2.3) File: time_series_nir.tif This map represents the climatology time series of infrared (nir) reflectance in period of four-days in rivers and lakes of central Amazon basin between 2003 and 2017 (15 years). Number of Bands: 92 (each band represent a date that is identified in dates.txt file) Values between 0 and 10000. Pixel Type: floating point Pixel Depth: 32 bit No Data Value: 0

2.4) File: time_series_red.tif This map represents the climatology time series of red reflectance in period of four-days in rivers and lakes of central Amazon basin between 2003 and 2017 (15 years). Number of Bands: 92 (each band represent a date that is identified in dates.txt file) Values between 0 and 10000. Pixel Type: floating point Pixel Depth: 32 bit No Data Value: 0

This data is related to the spatial distribution study of restaurants and cafes in Surabaya City. There are two operational data: 1) spatial data in *shp, shx, prj, cpg, qpj, dbf format, which can be opened using GIS software; and 2) statistical data in *omv format, opened using Jamovi software.

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherTAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained