These are the supplementary materials for the article "MapColorAI: Designing Contextually Relevant Choropleth Map Color Schemes Using a Large Language Model".GeoJSON data samples: Administrative Divisions of the People's Republic of China.jsonmapping data examples (The specific values in the following data are randomly generated and solely intended for system testing.):mapping data example1 Forest Coverage Rate by Province in China.jsonmapping data example2 Internet penetration rate by province.jsonmapping data example3 National Intangible Cultural Heritage Items.jsonmapping data example4 Seventh National Population Census in China .jsondemonstration video: Demonstration video.mp4system usage documentation: System usage documentation.html

Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

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

CrimeMapTutorial is a step-by-step tutorial for learning crime mapping using ArcView GIS or MapInfo Professional GIS. It was designed to give users a thorough introduction to most of the knowledge and skills needed to produce daily maps and spatial data queries that uniformed officers and detectives find valuable for crime prevention and enforcement. The tutorials can be used either for self-learning or in a laboratory setting. The geographic information system (GIS) and police data were supplied by the Rochester, New York, Police Department. For each mapping software package, there are three PDF tutorial workbooks and one WinZip archive containing sample data and maps. Workbook 1 was designed for GIS users who want to learn how to use a crime-mapping GIS and how to generate maps and data queries. Workbook 2 was created to assist data preparers in processing police data for use in a GIS. This includes address-matching of police incidents to place them on pin maps and aggregating crime counts by areas (like car beats) to produce area or choropleth maps. Workbook 3 was designed for map makers who want to learn how to construct useful crime maps, given police data that have already been address-matched and preprocessed by data preparers. It is estimated that the three tutorials take approximately six hours to complete in total, including exercises.

With this add in it is possible to create map templates from GIS files in KML format, and create choropleths with them. Providing you have access to KML format map boundary files, it is possible to create your own quick and easy choropleth maps in Excel. The KML format files can be converted from 'shape' files. Many shape files are available to download for free from the web, including from Ordnance Survey and the London Datastore. Standard mapping packages such as QGIS (free to download) and ArcGIS can convert the files to KML format. A sample of a KML file (London wards) can be downloaded from this page, so that users can easily test the tool out. Macros must be enabled for the tool to function. When creating the map using the Excel tool, the 'unique ID' should normally be the area code, the 'Name' should be the area name and then if required and there is additional data in the KML file, further 'data' fields can be added. These columns will appear below and to the right of the map. If not, data can be added later on next to the codes and names. In the add-in version of the tool the final control, 'Scale (% window)' should not normally be changed. With the default value 0.5, the height of the map is set to be half the total size of the user's Excel window. To run a choropleth, select the menu option 'Run Choropleth' to get this form. To specify the colour ramp for the choropleth, the user needs to enter the number of boxes into which the range is to be divided, and the colours for the high and low ends of the range, which is done by selecting coloured option boxes as appropriate. If wished, hit the 'Swap' button to change which colours are for the different ends of the range. Then hit the 'Choropleth' button. The default options for the colours of the ends of the choropleth colour range are saved in the add in, but different values can be selected but setting up a column range of up to twelve cells, anywhere in Excel, filled with the option colours wanted. Then use the 'Colour range' control to select this range, and hit apply, having selected high or low values as wished. The button 'Copy' sets up a sheet 'ColourRamp' in the active workbook with the default colours, which can just be extended or deleted with just a few cells, so saving the user time. The add-in was developed entirely within the Excel VBA IDE by Tim Lund. He is kindly distributing the tool for free on the Datastore but suggests that users who find the tool useful make a donation to the Shelter charity. It is not intended to keep the actively maintained, but if any users or developers would like to add more features, email the author. Acknowledgments Calculation of Excel freeform shapes from latitudes and longitudes is done using calculations from the Ordnance Survey.

Spatial and space-time clusters of SARS-CoV-2 infection in household cats in Illinois, United States, 2021–2023.

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

Results from a New Mexico county based gravity model measuring geographic accessibility using 2015 population and physician data. Both Euclidean and road distance measures were used. The relative difference between the Euclidean and road distance measures is presented. An IDW interpolation for road distance results is presented in addition choropleth maps. The 2015 census population estimates are from UNM-GPS and the 2015 primary care physician estimates were obtained from the New Mexico Health Care Workforce Committee, 2016 Annual Report: (http://hsc.unm.edu/assets/doc/economic-development/nmhcwc-presentation-2016.PDF).Additional results from a New Mexico Census Tract based gravity model measuring geographic accessibility using 2002 population and physician data. Both Euclidean and road distance measures were used. The relative difference between the Euclidean and road distance measures is presented. An IDW interpolation for road distance results is presented in addition choropleth maps. The 2015 census population estimates are from UNM-GPS and the 2002 primary care physicians estimates were from the Division of Government Research, UNM as part of work performed for the New Mexico Health Policy Commission from 1998 through 2002.Note: both choropleth and IDW interpolation examples are presented.More information at: (http://www.unm.edu/~lspear/health_stuff.html).

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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 applications,

This project provides a comprehensive dataset on intentional homicides in Mexico from 1990 to 2023, disaggregated by sex and state. It includes both raw data and tools for visualization, making it a valuable resource for researchers, policymakers, and analysts studying violence trends, gender disparities, and regional patterns.ContentsHomicide Data: Total number of male and female victims per state and year.Population Data: Corresponding male and female population estimates for each state and year.Homicide Rates: Per 100,000 inhabitants, calculated for both sexes.Choropleth Map Script: A Python script that generates homicide rate maps using a GeoJSON file.GeoJSON File: A spatial dataset defining Mexico's state boundaries, used for mapping.Sample Figure: A pre-generated homicide rate map for 2023 as an example.Requirements File: A requirements.txt file listing necessary dependencies for running the script.SourcesHomicide Data: INEGI - Vital Statistics MicrodataPopulation Data: Mexican Population Projections 2020-2070This dataset enables spatial analysis and data visualization, helping users explore homicide trends across Mexico in a structured and reproducible way.

BackgroundWhile the United Kingdom National Health Service aimed to reduce social inequalities in the provision of joint replacement, it is unclear whether these gaps have reduced. We describe secular trends in the provision of primary hip and knee replacement surgery between social deprivation groups.Methods and findingsWe used the National Joint Registry to identify all hip and knee replacements performed for osteoarthritis from 2007 to 2017 in England. The Index of Multiple Deprivation (IMD) 2015 was used to identify the relative level of deprivation of the patient living area. Multilevel negative binomial regression models were used to model the differences in rates of joint replacement. Choropleth maps of hip and knee replacement provision were produced to identify the geographical variation in provision by Clinical Commissioning Groups (CCGs).A total of 675,342 primary hip and 834,146 primary knee replacements were studied. The mean age was 70 years old (standard deviation: 9) with 60% and 56% of women undergoing hip and knee replacements, respectively. The overall rate of hip replacement increased from 27 to 36 per 10,000 person-years and knee replacement from 33 to 46. Inequalities of provision between the most (reference) and least affluent areas have remained constant for both joints (hip: rate ratio (RR) = 0.58, 95% confidence interval [0.56, 0.60] in 2007, RR = 0.59 [0.58, 0.61] in 2017; knee: RR = 0.82 [0.80, 0.85] in 2007, RR = 0.81 [0.80, 0.83] in 2017). For hip replacement, CCGs with the highest concentration of deprived areas had lower overall provision rates, and CCGs with very few deprived areas had higher provision rates. There was no clear pattern of provision inequalities between CCGs and deprivation concentration for knee replacement.Study limitations include the lack of publicly available information to explore these inequalities beyond age, sex, and geographical area. Information on clinical need for surgery or patient willingness to access care were unavailable.ConclusionsIn this study, we found that there were inequalities, which remained constant over time, especially in the provision of hip replacement, by degree of social deprivation. Providers of healthcare need to take action to reduce this unwarranted variation in provision of surgery.

PostGIS data for London and Greater London ward boundaries as of 2018.

This dataset is used in the london_votes sample Splitfile in which the 2017 General Election results and London Ward geodata are joined through the ONS UK Ward-Constituency lookup table to build a dataset of London constituencies and Conservative/Labour votes in each, ready for plotting as a Choropleth map.

https://data.london.gov.uk/dataset/statistical-gis-boundary-files-london

Contains National Statistics data © Crown copyright and database right 2012

Contains Ordnance Survey data © Crown copyright and database right 2012

This project provides comprehensive information on the total number of suicides in Mexico from 1990 to 2024, categorized by sex and state. It includes the main dataset along with a Python script and supporting files that enable users to analyze suicide rates and trends across the country.The dataset follows the official government methodology, using year of registration and state of residence of the deceased as key variables. It includes:Total male and female populationsSuicide counts for males and femalesSuicide rates for each sexData SourcesSuicide Data: Extracted from the INEGI database of registered deathshttps://www.inegi.org.mx/programas/edr/#microdatosPopulation Data: Derived from Mexican government population projections for 2020–2070https://datos.gob.mx/dataset/proyecciones-de-poblacion/resource/de522924-f4d8-4523-a6fd-6b2efe73f3afIncluded Filesscript.py – Python script to generate choropleth maps of suicide rates by state for a selected yearrequirements.txt – Required Python packages to run the scriptmexico.json – GeoJSON file containing administrative boundaries of Mexico by stateSample Chart (2024) – Example visualization featuring suicide rates for 2024This project can be used by researchers, public health professionals, policymakers, journalists, and students interested in understanding suicide trends in Mexico. It allows users to explore long-term and state-level patterns, compare differences between males and females, generate spatial visualizations, and incorporate the data into broader statistical, geographic, or public health analyses.

Dataset info

The dataset contains Local Authority Boundaries for Great Britain (England, Scotland and Wales) as of December 2021. A total of 363 Local Authority objects are included. Created for future use in folium choropleth maps when combined with other datasets that contain the matching Local Authority Codes. Additionally, subsets were created for convenience holding the boundaries of local authorities in England and Wales together, and in each individual country, i.e., England, Scotland and Wales on their own.

Methodology

The original dataset was downloaded from ONS. Since the dataset was too large for most use cases (129.4MB) due to the level of detail, it was simplified with https://mapshaper.org/ using the default method (Visvalingam / weighted area) with 'prevent shape removal' enabled. The simplification was set to 1.4%, followed by intersection repair and export back to geojson. The shape coordinates were originally in British National Grid (BNG) format, which had to be converted to WGS84 (latitude and longitude) format. Finally, the coordinates were rounded to 6 decimal places, resulting in a file containing 2.2MB of uncompressed data with a sensible level of detail. The individual country data were extracted, based on the LAD21CD property, to create the additional files.

Licence

https://www.ons.gov.uk/methodology/geography/licences

Digital boundary products and reference maps are supplied under the Open Government Licence. You must use the following copyright statements when you reproduce or use this material:

• Source: Office for National Statistics licensed under the Open Government Licence v.3.0
• Contains OS data © Crown copyright and database right 2023

This project features a Python script designed to visualize Mexico's trade relationships from 1993 to 2025. Using official trade data sourced from the DataMexico VizBuilder, the script generates:Bar Charts: Highlighting the top 30 export or import trade partners of Mexico for any given year.Choropleth Maps: Showing the trade values (exports or imports) for all countries, customizable by a specific year.The dataset included provides comprehensive trade figures for over three decades, broken down by country and trade flow type (exports or imports).Additionally, the project includes a requirements.txt file for easy dependency installation and sample visualizations to demonstrate the script's functionality.This tool aims to provide researchers, policymakers, and educators with a clear, customizable way to explore and analyze Mexico's trade dynamics over time.