Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted geospatial data cover administrative and postal divisions with up to 5 precision levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Administrative Boundaries Database (Geospatial data, Map data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the map data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

This dataset contains a collection of JSON files used to configure map catalogs in TerriaJS, an interactive geospatial data visualization platform. The files include detailed configurations for services such as WMS, WFS, and other geospatial resources, enabling the integration and visualization of diverse datasets in a user-friendly web interface. This resource is ideal for developers, researchers, and professionals who wish to customize or implement interactive map catalogs in their own applications using TerriaJS.

Folium makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing rich vector/raster/HTML visualizations as markers on the map. These files can be used to mark the state boundaries on the map of INDIA using folium library and the CSV also contains the state data and how to use it in our notebooks. I have used it in one of my kernels which can be viewed.

The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys. folium supports both Image, Video, GeoJSON, and TopoJSON overlays. Due to extensible functionalities I find folium the best map plotting library in python. Do give it a try and use it in your kernels.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted geospatial data cover postal divisions for the whole world. The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (Geospatial data, Map data, Polygon daa)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the map data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

This vector tile layer presents the World Street Map (Local Language) style (World Edition) and provides a basemap for the world, symbolized with a classic Esri street map style. This comprehensive street map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. Labels are in local languages at large scale. This vector tile layer provides unique capabilities for customization, high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.This layer is used in the Streets (Local Language) web map included in ArcGIS Living Atlas of the World.See the Vector Basemaps group for other vector tile layers. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.

USGS developed The National Map Gazetteer as the Federal and national standard (ANSI INCITS 446-2008) for geographic nomenclature based on the Geographic Names Information System (GNIS). The National Map Gazetteer contains information about physical and cultural geographic features, geographic areas, and locational entities that are generally recognizable and locatable by name (have achieved some landmark status) and are of interest to any level of government or to the public for any purpose that would lead to the representation of the feature in printed or electronic maps and/or geographic information systems. The dataset includes features of all types in the United States, its associated areas, and Antarctica, current and historical, but not including roads and highways. The dataset holds the federally recognized name of each feature and defines the feature location by state, county, USGS topographic map, and geographic coordinates. Other attributes include names or spellings other than the official name, feature classification, and historical and descriptive information. The dataset assigns a unique, permanent feature identifier, the Feature ID, as a standard Federal key for accessing, integrating, or reconciling feature data from multiple data sets. This dataset is a flat model, establishing no relationships between features, such as hierarchical, spatial, jurisdictional, organizational, administrative, or in any other manner. As an integral part of The National Map, the Gazetteer collects data from a broad program of partnerships with federal, state, and local government agencies and other authorized contributors. The Gazetteer provides data to all levels of government and to the public, as well as to numerous applications through a web query site, web map, feature and XML services, file download services, and customized files upon request. The National Map download client allows free downloads of public domain geographic names data by state in a pipe-delimited text format. For additional information on the GNIS, go to https://www.usgs.gov/tools/geographic-names-information-system-gnis. See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata. Data Refreshed March, 2025

Center for Human Neuroscience (CHN) Retinotopic Mapping Dataset

The Center for Human Neuroscience (CHN) Retinotopic Mapping Dataset collected at the University of Washington is part of "Improving the reliability and accuracy of population receptive field measures using a 'log-bar' stimulus" by Kelly Chang, Ione Fine, and Geoffrey M. Boynton.

The full dataset is comprised of the raw, preprocessed (with fMRIPrep), and pRF estimated data from 12 participants across 2 sessions.

Dataset Organization

• dataset
  This directory contains the raw, unprocessed data for each participant.

• dataset/derivatives/fmriprep
  This directory contains the fMRIPrep processed data for each particpant.

• dataset/derivatives/freesurfer
  This directory contains the standard FreeSurfer processed data for each participant.

• dataset/derivatives/prf-estimation
  This directory contains the pRF estimation data and results for each participant.

• dataset/derivatives/prf-estimation/files
  This directory contains miscellaneous files used for pRF estimation or visualizations.

  • angle_lut.json: Custom polar angle lookup table for visualization with FreeSurfer's freeview.
  • eccen_lut.json: Custom eccentricity lookup table for visualization with FreeSurfer's freeview.
  • participants_hrf_paramters.json: Corresponding metadata for participants_hrf_paramters.tsv.
  • participants_hrf_paramters.tsv: Estimated HRF parameters used during pRF estimation by participant and hemisphere.

• dataset/derivatives/prf-estimation/stimuli
  This directory contains the stimuli used in the experiment and stimulus apertures used in pRF estimation.

  • task-(fixed|log)bar_run-<n>: Name of the stimulus condition and run number.
  • *_desc-full_stim.mat: Stimulus images (uint8) at full resolution of 540 by 540 pixels and 6 Hz.
  • *_desc-down_aperture.mat: Stimulus aperature (binary) where 1s indicated stimulus and 0s indicated the background at a downsampled (down) resolution of 108 by 108 pixels and 1 Hz.

• dataset/derivatives/prf-estimation/sub-<n>/anat
  This directory contains the participant's surface (inflated and sphere) and curvature files for visualization using FreeSurfer's freeview.

• dataset/derivatives/prf-estimation/sub-<n>/func
  This directory contains the preprocessed and denoised functional data, sampled onto the participant's surface, used during pRF estimation.

• dataset/derivatives/prf-estimation/sub-<n>/prfs This directory contains the estimated pRF parameter maps separated by which data was used during estimation.

  • ses-(01|02|all): Sessions used during pRF estimation, either Session 1, Session 2, or both.
  • task-(fixedbar|logbar|all): Stimuli type used during pRF estimation, either fixed-bar, log-bar, or both.

  Within the pRF estimate directories are the estimated pRF parameter maps for: - *_angle.mgz: Polar angle maps, degrees from (-180, 180). Negative values represent the left hemifield and positive values represent the right hemifield. - *_eccen.mgz: Eccentricity maps, visual degrees. - *_sigma.mgz: pRF size maps, visual degrees. - *_vexpl.mgz: Proportion of variance explained maps. - *_x0.mgz: x-coordinate maps, visual degrees, with origin (0,0) at screen center. - *_y0.mgz: y-coordinate maps, visual degrees, with origin (0,0) at screen center.

• dataset/derivatives/prf-estimation/sub-<n>/rois
  This directory contains the roi (.label) files for each participant.

  • *_evc.label: Early visual cortex (EVC). A liberal ROI that covered V1, V2, and V3 used for pRF estimation.
  • *_fovea.label: Foveal confluence ROI.
  • *_v<n>.label: Corresponding visual area ROI files.

• dataset/tutorials
  This directory contains tutorial scripts in MATLAB and Python to generate log distorted images from a directory of input images.

  • create_distorted_images.[m,ipynb]: Tutorial script that generates log-distorted images when given an image input directory.
  • fixed-bar: Sample image input directory.
  • log-bar: Sample image output directory.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted GIS data cover administrative and postal divisions with up to 6 precision levels: a zip code layer and up to 5 administrative levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (GIS data, Geospatial data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the GIS data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our geospatial data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All GIS data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

This dataset contains download links for the contours mapped over the Brisbane City Council local government area in 2002\. The contours data uses the Geocentric Datum of Australia 1994 (GDA94\) datum and is projected in Zone 56 of the Map Grid of Australia (MGA56\).

Dataset Downloads The dataset map provides two download options for each grid envelope:

• DWG: Predefined attachments associated with the grid envelope.
• JSON: Uses the ESRI Rest API to extract complete contour lines, that have any part of the contour line, within the grid envelope. This option allows you to define a custom envelope.

To download a file in the dataset map, click on a grid envelope, select the download type, click the download link.

Custom Envelope If you need contour lines for a specific area, you can create a custom envelope. By following these steps, you can easily download contour lines for any specific area within the dataset:

1. Determine Custom Coordinates: Find the latitude and longitude (coordinates) for the top\-left and bottom\-right corners of your specific area.
2. Replace Coordinates: Replace the coordinates in any JSON download link with your custom coordinates.

Coordinate Format The coordinate format in the JSON download links is:

(top left corner)longitude,latitude,(bottom right corner)longitude,latitude

The Data and resources section of this dataset contains further information for this dataset including links to additional contours feature layers.

This dataset contains download links for the contours mapped over the Brisbane City Council local government area in 2002. The contours data uses the Geocentric Datum of Australia 1994 (GDA94) datum and is projected in Zone 56 of the Map Grid of Australia (MGA56).

Dataset Downloads

The dataset map provides two download options for each grid envelope:

DWG: Predefined attachments associated with the grid envelope.

JSON: Uses the ESRI Rest API to extract complete contour lines, that have any part of the contour line, within the grid envelope. This option allows you to define a custom envelope.

To download a file in the dataset map, click on a grid envelope, select the download type, click the download link.

Custom Envelope

If you need contour lines for a specific area, you can create a custom envelope. By following these steps, you can easily download contour lines for any specific area within the dataset:

Determine Custom Coordinates: Find the latitude and longitude (coordinates) for the top-left and bottom-right corners of your specific area.

Replace Coordinates: Replace the coordinates in any JSON download link with your custom coordinates.

Coordinate Format

The coordinate format in the JSON download links is: (top left corner)longitude,latitude,(bottom right corner)longitude,latitude

The Data and resources section of this dataset contains further information for this dataset including links to additional contours feature layers.

Centralisation de l'ensemble des jeux de données mis en ligne concernant le découpage géographique de la Tunisie, pour l'essentiel extraits depuis OSM mais aussi de fichiers gouvernementaux. Conversions à l'aide de http://mapshaper.org/

This layer contains detailed outlines of Maryland counties. The Maryland land county boundaries were built using political county boundaries and the National Hydrology Data (NHD). Land boundaries are a key geographic featue in our mapping process.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Last Updated: UnknownFeature Service Link:https://mdgeodata.md.gov/imap/rest/services/Boundaries/MD_PhysicalBoundaries/FeatureServer/0

Thailand (THA) Administrative Boundary Common Operational Database (COD-AB): Level 0 (country), 1 (province), 2 (district), and 3 (sub-district, tambon) boundaries.

Aims and scope

The European Alien Species Information Network team (EASIN, http://easin.jrc.ec.europa.eu) of the Joint Research Centre (JRC) requests the European member states to provide and verify the baseline distribution data of invasive alien species of Union Concern (Tsiamis et al. 2017) as provided by the EASIN mapping system (Katsanevakis et al. 2012). These are species with documented biodiversity impacts sensu the European Union Regulation on the prevention and management of the introduction and spread of Invasive Alien Species in Europe (IAS Regulation No 1143/2014) (European Union 2014). The purpose of this baseline is to set a representative geographic account of the distribution of these species at (i) country and (ii) 10km2 grid level before the entry into force of the Regulation (and the listing of species through implementing regulations). This distribution provides the baseline for subsequent reporting by the member states as required by the IAS Regulation.

The dataset provides a shapefile on the baseline distribution of the invasive species of EU concern in Belgium based on an aggregated dataset (ias_belgium_t0_xxxx). Data were compiled from various datasets holding invasive species observations such as data from research institutes and research projects (76%), citizen science observatories (23%) and a range of other sources (1%) such as governmental agencies, water managers, invasive species control companies, angling and hunting organizations etc. Data were normalized using a custom mapping of the original data files to Darwin Core (Wieczorek et al. 2012) where possible. Species names were mapped to the GBIF Backbone Taxonomy (GBIF 2016) using the species API (http://www.gbif.org/developer/species). Appropriate selection of records was performed based on predefined cut-off dates (see data range) and record content validation (see validation procedure). Data were then joined with GRID10k layer Belgium based on GRID10k cellcodes (ETRS_1989_LAEA).

File description

The dataset contains two types of data:

Shapefiles (ias_belgium_t0_2016.zip, ias_belgium_t0_2018.zip, ias_belgium_t0_2020.zip and ias_belgium_t0_2023.zip) providing the presence of the species of EU concern at 10km2 (European Terrestrial Reference System projection - 1989 ETRS_1989_LAEA) level (resp. for 1st, 2nd, 3rd and 4th batch of species added to the Union List). The attributes table field “ACCEPTED” provides coded information on the distribution validation: correct squares (Y) represent data overlapping between the collated baseline data for Belgium and the EASIN maps. Incorrect data (N) can represent records mapped on wrong 10km2 squares, non-validated records or records that fall outside of the date range applied. New squares (New) represent previously unpublished data that were absent from EASIN. The work was supervised and validated by the Belgian national scientific council on invasive alien species, an official consultative structure coordinating scientific input and data aggregation between Belgian regions and institutions with regards to technical implementation of the Regulation No 1143/2014 on invasive alien species.

A geojson version of the same shapefiles (ias_belgium_t0_2016.geojson, ias_belgium_t0_2018.geojson, ias_belgium_t0_2020.geojson, ias_belgium_t0_2023.geojson), in WGS84 projection.

Date range

The baseline distribution reflects the current status and situation of the IAS of Union concern in Belgium at 10km2 grid level. Historical records were not taken into consideration for the baseline. The choice of cut-off date was based on an analysis of the relative contribution of a year in defining the total distribution of the species at 1km2 grid level (calculated as [the sum of unique UTM 1km2 grid squares year-1/total number of unique UTM 1km2 grid squares for that species]) based on the complete dataset.

The dataset comprises observations of Union List invasive species from 2000 until the entry into force for every species, hence between January 2000 (2000-01-01) and February 2016 (2016-01-31) for the species of the first batch (ias_belgium_t0_2016.zip), between January 2000 (2000-01-01) and August 2017 (2017-08-31) for the species of the first update of the Union List (ias_belgium_t0_2018.zip), between January 2000 (2000-01-01) and August 2019 (2019-08-31) for the species of the second update of the Union List (ias_belgium_t0_2020.zip), between January 2000 (2000-01-01) and August 2022 (2022-08-2) for the species of the third update (ias_belgium_t0_2023.zip). For raccoon dog (Nyctereutes procyonoides), included in the second update (ias_belgium_t0_2020.zip) the date cut-off is 01/01/2000 to 31/01/2019. Note that Pistia stratiotes, Xenopus laevis and Fundulus heteroclitus enter into force only as from 2 August 2024, Celastrus orbiculatus on 2 August 2027 because of prolonged transitionary measures. However, these species are already included in the baseline now with a cut-off date set on August 2022. The data include both casual records as well as established populations and also comprise data from eradicated populations for the period 2000-2022.

Validation procedure

Record validation was performed to exclude dubious records, wrong identifications etc. This was done based on the IdentificationVerificationStatus field (to which validation information from original data were mapped) if available. In general, non-validated data were not considered for ias_belgium_t0_xxxx. Data were validated in the original datasets based on evidence (e.g. pictures), on the observer’s experience, or based on a set of predefined rules (e.g. automated validation based on geographic filtering). Data from research institutes were generally considered validated. A few casual records of EU list species that were clearly planted were discarded manually. When the original dataset did not mention any validation status, records were not considered validated and therefore not taken into account for ias_belgium_t0_xxxx, unless for Chinese mitten crab Eriocheir sinensis, ruddy duck Oxyura jamaicensis, raccoon Procyon lotor, Siberian ground squirrel Tamias sibiricus, sacred ibis Threskiornis aethiopicus, and red-eared slider Trachemys spp. For these species, we assumed all records were correct as they originate from dedicated sampling (E. sinensis) within research projects or represent species that are readily recognizable by people in the field. Likewise, for the second batch species, all records of Egyptian goose Alopochen aegyptiaca, Himalayan balsam Impatiens glandulifera, giant hogweed Heracleum mantegazzianum and muskrat Ondatra zibethicus (mostly derived from public eradication services) were considered validated and taken into account. For the third batch species, records of the widespread tree of heaven Ailanthus altissima and pumpkinseed Lepomis gibbosus were also considered validated. For species with less than 10 records (Salvinia molesta, Acridotheres tristis), every record was manually checked.

A visual check was performed on the resulting distribution maps by representatives of the Belgian scientific council on IAS and the Belgian Comittee on IAS, two official bodies created in response to the EU Regulation within the framework of a cooperation agreement between the Belgian regions and the Federal Authority. Data in the distribution maps provided by EASIN but not present in ias_belgium_t0_xxxx were carefully checked and kept/rejected accordingly.

Data providers

The providers of the invasive species data for this exercise (individuals and their respective organizations) are listed in the "data providers" section of the dataset metadata. Much of the primary occurrence data that formed the basis for this aggregated dataset will be published as open data on the Global Biodiversity Information Facility (GBIF) within the framework of the Tracking Invasive Alien Species project (TrIAS, https://osf.io/7dpgr/, 2017-2020).

The North American Roads dataset was compiled on October 27, 2020 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset contains geospatial information regarding major roadways in North America. On March 31, 2025, the errant records with a value of 2 in the "NHS" field were corrected to have a value of 7 (Other NHS). The data set covers the 48 contiguous United States plus the District of Columbia, Alaska, Hawaii, Canada and Mexico. The nominal scale of the data set is 1:100,000. The data within the North American Roads layer is a compilation of data from Natural Resources Canada, USDOT’s Federal Highway Administration, and the Mexican Transportation Institute. North American Roads is a digital single-line representation of major roads and highways for Canada, the United States, and Mexico with consistent definitions by road class, jurisdiction, lane counts, speed limits and surface type.