This dataset contains geometry data for the countries of the world together with their names and country codes in various formats. The primary use case is choropleths, color-coded maps. The data can be read as a pandas DataFrame with geopandas and plotted with matplotlib. See the starter notebook for an example how to do it.

The data was created by Natural Earth. It is in public domain and free to use for any purpose at the time of this writing; you might want to check their Terms of Use.

Photo by KOBU Agency on Unsplash

Context

I created a dataset to help people create choropleth maps of United States states.

Content

One geojson to plot the countries borders, and one csv from the Census Bureau for the us population per state.

Inspiration

I think the best way to use this dataset is in joining it with other data. For example, I used this dataset to plot police killings using the data from https://www.kaggle.com/jpmiller/police-violence-in-the-us

Natural Earth is a public domain map dataset available at 1:10, 1:50 and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.

The dataset contains the fixation of individual participants in separate excel files.

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Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance app

Natural Earth is a public domain map dataset available at 1:10, 1:50 and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.

Synthetic populations for regions of the World (SPW) | Guatemala

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (gtm_data_v_0_9.zip)

Derived data files

Validation and measures files

Synthetic populations for regions of the World (SPW) | Alabama

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (usa_140002904_data_v_0_9.zip)

Derived data files

Validation and measures files

Synthetic populations for regions of the World (SPW) | SwedenDataset informationA synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics). LicenseCC-BY-4.0 AcknowledgmentThis project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541). Contact informationHenning.Mortveit@virginia.edu Identifiers Region name Sweden Region ID swe Model coarse Version 0_9_0 Statistics Name Value Population 9143037.0 Average age 40.8 Households 3820873.0 Average household size 2.4 Residence locations 3820873.0 Activity locations 1440586.0 Average number of activities 5.8 Average travel distance 49.3 Sources Description Name Version Url Activity template data World Bank 2021 https://data.worldbank.org Administrative boundaries ADCW 7.6 https://www.adci.com/adc-worldmap Curated POIs based on OSM SLIPO/OSM POIs http://slipo.eu/?p=1551 https://www.openstreetmap.org/ Population count with demographic attributes GPW v4.11 https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11 Files descriptionBase data files (swe_data_v_0_9.zip) Filename Description swe_person_v_0_9.csv Data for each person including attributes such as age, gender, and household ID. swe_household_v_0_9.csv Data at household level. swe_residence_locations_v_0_9.csv Data about residence locations swe_activity_locations_v_0_9.csv Data about activity locations, including what activity types are supported at these locations swe_activity_location_assignment_v_0_9.csv For each person and for each of their activities, this file specifies the location where the activity takes place Derived data files Filename Description swe_contact_matrix_v_0_9.csv A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model. Validation and measures files Filename Description swe_household_grouping_validation_v_0_9.pdf Validation plots for household construction swe_activity_durations_{adult,child}_v_0_9.pdf Comparison of time spent on generated activities with survey data swe_activity_patterns_{adult,child}_v_0_9.pdf Comparison of generated activity patterns by the time of day with survey data swe_location_construction_0_9.pdf Validation plots for location construction swe_location_assignement_0_9.pdf Validation plots for location assignment, including travel distribution plots swe_swe_ver_0_9_0_avg_travel_distance.pdf Choropleth map visualizing average travel distance swe_swe_ver_0_9_0_travel_distr_combined.pdf Travel distance distribution swe_swe_ver_0_9_0_num_activity_loc.pdf Choropleth map visualizing number of activity locations swe_swe_ver_0_9_0_avg_age.pdf Choropleth map visualizing average age swe_swe_ver_0_9_0_pop_density_per_sqkm.pdf Choropleth map visualizing population density swe_swe_ver_0_9_0_pop_size.pdf Choropleth map visualizing population size

Synthetic populations for regions of the World (SPW) | Italy

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Sources

Files description

Base data files (ita_data_v_0_9.zip)

Derived data files

Validation and measures files

Synthetic populations for regions of the World (SPW) | Spain

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (esp_data_v_0_9.zip)

Derived data files

Validation and measures files

Author: E Ripken, educator, MN Alliance for Geographic EducationGrade/Audience: grade 8, high schoolResource type: lessonSubject topic(s): maps, developmentRegion: africaStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.

Standard 14. Globalization, the spread of capitalism and the end of the Cold War have shaped a contemporary world still characterized by rapid technological change, dramatic increases in global population and economic growth coupled with persistent economic and social disparities and cultural conflict. (The New Global Era: 1989 to Present)

Objectives: Students will be able to:

1. Read and analyze maps.
2. Make correlations and generalizations between data sets.
3. Make inferences about modern day problems that have plagued Africa. Summary: Students will work in groups to create several choropleth maps that show GDP per capita, literacy rate, life expectancy, % urban, % agriculture and the AIDS rate. Students will use these maps to make generalizations, establish correlations and construct inferences.

Final global negative binomial model showing significant predictors of COVID-19 risk in the Greater St. Louis Area, Missouri (USA).

Synthetic populations for regions of the World (SPW) | Germany

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (deu_data_v_0_9.zip)

Derived data files

Validation and measures files

Synthetic populations for regions of the World (SPW) | Delhi

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (ind_140001944_data_v_0_9.zip)

Derived data files

Validation and measures files

Summary of the OLS a and GWR b models.

Synthetic populations for regions of the World (SPW) | Denmark

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (dnk_data_v_0_9.zip)

Derived data files

Validation and measures files

Synthetic populations for regions of the World (SPW) | Kenya

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (ken_data_v_0_9.zip)

Derived data files

Validation and measures files

Results of assessment of stationarity of the coefficients of the predictors of the COVID-19 risks in the Greater St. Louis Area, Missouri.

This is a single-layer version of a prototype design for an accessible basemap - one that meets the requirements of the WCAG (Web Content Accessibility Guidelines) AA standard, and US Government Section 508. Map detail is built using a hierarchy with a higher contrast than usual, and with color-blind-safe colors. Smaller labels have been enlarged, and haloes are used extensively. The map will work as a reference map, or as a basemap in the right circumstances. It is bright, and may not work for choropleth-style maps, but otherwise it should be suitable for users who wish to add it to their own content.This version of the map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints and administrative boundaries. The vector tile layer in this map is built using the same data sources used for the World Street Map and other Esri basemaps. Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map.Customize this MapBecause this map includes a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers, switch to alternate local language (in some areas), and refine the treatment of disputed boundaries. See the Vector Basemap group for other vector web maps. For details on how to customize this map, please refer to these articles on the ArcGIS Online Blog.