Choropleth maps are fundamental tools for geographic data analysis, primarily relying on color to convey information. Consequently, the design of their color schemes is of paramount importance in choropleth map production. The traditional coloring methods offered by GIS tools such as ArcGIS and QGIS are not user-friendly for non-professionals. These tools provide numerous color schemes, making selection difficult, and cannot also easily fulfill personalized coloring needs, such as requests for 'summer-like' map colors. To address these shortcomings, we develop a novel system that leverages a large language model and map color design principles to generate contextually relevant and user-aligned choropleth map color schemes. The system follows a three-stage process: Data processing, which provides an overview and classification of the data; Color Concept Design, where color theme and mode are conceptualized based on data characteristics and user intentions; and Color Scheme Design, where specific colors are assigned to classes. Our system incorporates an interactive interface for choropleth map color design and allows users to customize color choices flexibly. Through user studies and evaluations, the system demonstrates acceptable usability, accuracy, and flexibility, with users highlighting its efficiency and ease of use.

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

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.

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

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) 📍

When creating choropleth maps, mapmakers often bin (i.e. group, classify) quantitative data values into groups to help show that certain areas fall within a similar range of values. For instance, a mapmaker may divide counties into groups of high, middle, and low life expectancy (measured in years). It is well known that different binning methods (e.g. natural breaks, quantiles) yield different groupings, meaning the same data can be presented differently depending on how it is divided into bins. To help guide a wide variety of users, we present a new, open-source, web-based, geospatial visualization tool, Exploropleth, that lets users interact with a catalog of established data binning methods, and subsequently compare, customize, and export custom maps. This tool advances the state of the art by providing multiple binning methods in one view and supporting administrative unit reclassification on-the-fly. We interviewed 16 cartographers and geographic information systems (GIS) experts from 13 government organizations, non-government organizations (NGOs), and federal agencies who identified opportunities to integrate Exploropleth into their existing mapmaking workflow, and found that the tool has the potential to educate students as well as mapmakers with varying levels of experience. Exploropleth is open-source and publicly available at https://exploropleth.github.io.

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

List of sociodemographic variables used in PCA analysis to create new indicators for spatial analysis.

Context

I used this publicly available data for making interactive map visualization of NYC. Zipcode geodata is useful for building interactive maps with each zip code area representing a separate area on the map.

Content

NYC zipcode geodata in geojson format

Acknowledgements

The rights belong to the original authors.

OverviewThis feature layer shows population change compared to pre-crisis baseline in the affected areas by Hurricane Idalia on a daily basis for all Census Designated Population (CDP), an administrative unit smaller than county, in Florida, Georgia, Alabama, and South Carolina. The layer has time enabled to show the change from 2023-08-28 to the latest date when population change data harvested by Data for Good at Meta is available.Population maps provided by Data for Good at Meta are generated based on users of Facebook. For more information about the disaster population maps provided by Data for Good at Meta, please refer to this link.Default data visualizationA divergent color ramp was employed to create a choropleth map for % population change compared to the pre-crisis baseline. The size of pre-crisis baseline is visualized using circles in different sizes. Each polygon represents one census designated place in the affected areas.This feature layer contains the following metrics for mapping and analysis:Baseline population - an estimated number of Facebook users during the pre-crisis period. It is calculated as an average of 90 days before the crisis (in this case, 2023-08-28 was used as the onset of crisis).Crisis population - an estimated number of Facebook users during the crisis. Original data are provided every 8 hours.Difference in population - the difference between crisis population and the baseline populationPercent change in population - the percentage of population change from baseline to a given date during the crisisZ-score - a unitless normalized measurement to quantify the population change from baselineDate - Date of data acquisition. Original data are provided three times a day (8-hour interval). We calculated a daily average using all three timestamps available for each day. Users can filter by Date to create a subset showing the population change on a selected dateMethod of data preparationRemove data points without a valid baseline population or percent change in populationCalculate daily average using the three timestamps available for each dayAggregate the original point data to census designated places in the affected areasAppend all daily average census designated places data to a single file to enable time option of the layer

These are 2 files with Geographic vector layers with Continent Boundaries. You can use this data to create Choropleth maps or for any map visualization that requires vectors at the Continent level.

Citation of the data source:

Shepherd, Stephanie (2020). Continent Polygons. figshare. Dataset. https://doi.org/10.6084/m9.figshare.12555170.v3

I transformed the data lightly to make a GPKG file and to add the documents to Kaggle.

The original data is similar to the file continent_boundaries_8.gpkg, which includes the following continents:

• Africa
• Antarctica
• Asia
• Australia
• Europe
• North America
• Oceania
• South America

I also created a file called continent_boundaries_7.gpkg that merges Australia and Oceania as a single continent.

You can find the transformations in the following Kaggle Notebook: https://www.kaggle.com/code/ericnarro/create-continents-geodataframe-and-file/notebook

Snapshot img

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.
Request Free Sample

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,

OverviewThis feature layer shows population change compared to pre-crisis baseline in Hawaii on a daily basis for all Census Designated Population (CDP), an administrative unit smaller than county, in Hawaii. The layer has time enabled to show the change from 2023-08-10 to the latest date when population change data harvested by Data for Good at Meta is available.Population maps provided by Data for Good at Meta are generated based on users of Facebook. For more information about the disaster population maps provided by Data for Good at Meta, please refer to this link.Default data visualizationA divergent color ramp was employed to create a choropleth map for % population change compared to the pre-crisis baseline. The size of pre-crisis baseline is visualized using circles in different sizes. Each polygon represents one census designated place in Hawaii.This feature layer contains the following metrics for mapping and analysis:Baseline population - an estimated number of Facebook users during the pre-crisis period. It is calculated as an average of 90 days before the crisis (in this case, 2023-08-10 was used as the onset of crisis).Crisis population - an estimated number of Facebook users during the crisis. Original data are provided every 8 hours.Difference in population - the difference between crisis population and the baseline populationPercent change in population - the percentage of population change from baseline to a given date during the crisisZ-score - a unitless normalized measurement to quantify the population change from baselineDate - Date of data acquisition. Original data are provided three times a day (8-hour interval). We calculated a daily average using all three timestamps available for each day. Users can filter by Date to create a subset showing the population change on a selected dateMethod of data preparationRemove data points without a valid baseline population or percent change in populationCalculate daily average using the three timestamps available for each dayAggregate the original point data to census designated places in HawaiiAppend all daily average census designated places data to a single file to enable time option of the layer

This project aims to identify areas in Los Angeles that are at high risk of crime in the future and to propose optimal locations for new police stations in those areas. By applying machine learning to post-COVID-19 crime data and various socioeconomic indicators, we predict crime risk at the ZIP Code level. Using a location-allocation model, we then determine suitable locations for new police stations to improve coverage of high-risk zones. The results of our analysis can support the efficient allocation of public safety resources in response to growing demand and budget constraints, helping city officials optimize law enforcement services. The content of the archive- Jupyter Notebook- Data (GeoJSON, CSV)- Summary report PDF FileThe platform on which the notebook should be run.This notebook is designed to run on Datahub.Project materials - Project Material we created on AGOL 1 Los Angeles Crime Hotspothttps://ucsdonline.maps.arcgis.com/home/item.html?id=4bddbae65c164f2d9b0285e09cb2820e 2 Choropleth Map of Predicted Crime Levels by ZIP Codehttps://ucsdonline.maps.arcgis.com/home/item.html?id=e47abb448f0a411ab77c6ac754ba0c34 3. Optimizing LA Police Station: A Location Allocation Analysishttps://ucsdonline.maps.arcgis.com/home/item.html?id=2409da85c3fe410e9578a0eaaed8471e - ArcGIS StoryMaphttps://ucsdonline.maps.arcgis.com/home/item.html?id=cfbd4fc27a3b400296e4e31555951d27 Software dependencies - pandas: Used for loading, formatting, and performing matrix operations on tabular data.- geopandas: Used for loading and processing spatial data, including spatial joins and coordinate transformations.- shapely.geometry.Point: Used to create spatial point objects from latitude and longitude coordinates.- arcgis.gis, arcgis.features, arcgis.geometry, arcgis.geoenrichment: Used to retrieve and manipulate geographic data from ArcGIS Online and to extract population statistics using the GeoEnrichment module.- numpy: Used for feature matrix formatting and numerical computations prior to model training.- IPython.display (display, Markdown, Image): Used to format and display Markdown text, data tables, and images within Jupyter Notebooks.- scikit-learn: Used for building and evaluating machine learning models. Specifically, it was used for data preprocessing (StandardScaler), splitting data (train_test_split), model selection and tuning (GridSearchCV, cross_val_score), training various regressors (e.g.,LinearRegression, RandomForestRegressor, KNeighborsRegressor), and assessing performance using metrics such as R², RMSE, and MAE.Other Components we used - ArcGIS Online: Used to create and host interactive web maps for spatial visualization and public presentation purposes.- Flourish: Used to create interactive graphs and charts for visualizing trends and supporting the analysis.

FlowMapper.org is a web-based framework for automated production and design of origin-destination flow maps. FlowMapper has four major features that contribute to the advancement of existing flow mapping systems. First, users can upload and process their own data to design and share customized flow maps. The ability to save data, cartographic design and map elements in a project file allows users to easily share their data and/or cartographic design with others. Second, users can generate customized flow symbols to support different flow map reading tasks such as comparing flow magnitudes and directions and identifying flow and location clusters that are strongly connected with each other. Third, FlowMapper supports supplementary layers such as node symbols, choropleth, and base maps to contextualize flow patterns with location references and characteristics. Finally, the web-based architecture of FlowMapper supports server-side computational capabilities to process and normalize large flow data and reveal natural patterns of flows.

OverviewThis feature layer shows population change compared to pre-crisis baseline in Pakistan on a daily basis for all level 3 administrative units of Pakistan. The layer has time enabled to show the change from 2022-08-14 to the latest date when population change data harvested by Data for Good at Meta is available.Population maps provided by Data for Good at Meta are generated based on users of Facebook. For more information about the disaster population maps provided by Data for Good at Meta, please refer to this link.Default data visualizationA divergent color ramp was employed to create a choropleth map for % population change compared to the pre-crisis baseline. The size of pre-crisis baseline is visualized using circles in different sizes. Each circle represents one Level 3 administrative unit in Pakistan.This feature layer contains the following metrics for mapping and analysis:Baseline population - an estimated number of Facebook users during the pre-crisis period. It is calculated as an average of 90 days before the crisis (in this case, 2022-08-14 was used as the onset of crisis).Crisis population - an estimated number of Facebook users during the crisis. Original data are provided every 8 hours.Difference in population - the difference between crisis population and the baseline populationPercent change in population - the percentage of population change from baseline to a given date during the crisisZ-score - a unitless normalized measurement to quantify the population change from baselineDate - Date of data acquisition. Original data are provided three times a day (8-hour interval). We calculated a daily average using all three timestamps available for each day. Users can filter by Date to create a subset showing the population change on a selected dateMethod of data preparationRemove data points without a valid baseline population or percent change in populationCalculate daily average using the three timestamps available for each dayAggregate the original point data to Level 3 administrative units of PakistanAppend all daily average level 3 administrative units data to a single file to enable time option of the layer

This dataset contains a comprehensive collection of geographic shapefiles representing the boundaries of countries and territories worldwide. The shapefiles define the outlines of each nation and are based on the most recent and accurate geographical data available. The dataset includes polygon geometries that accurately represent the territorial extent of each country, making it suitable for various geographical analyses, visualizations, and spatial applications.

Content: The dataset comprises shapefiles in the ESRI shapefile format (.shp) along with associated files (.shx, .dbf, etc.) that contain the attributes of each country, such as country names, ISO codes, and other relevant information. The polygons in the shapefiles correspond to the land boundaries of each nation, enabling precise mapping and spatial analysis.

Use Cases: This dataset can be utilized in a wide range of applications, including but not limited to:

• Creating choropleth maps to visualize and analyze various socio-economic indicators by country.
• Conducting spatial analysis to study population distribution, territorial areas, and geographic trends.
• Performing geopolitical research and country-level comparisons.
• Integrating with other datasets to enrich geographic analyses and insights.

Source: The shapefile data is sourced from reputable and authoritative geographic databases, ensuring its accuracy and reliability for diverse applications.

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.

OverviewThis feature layer shows population change compared to pre-crisis baseline in Greece on a daily basis for all level 3 administrative units of Greece. The layer has time enabled to show the change from 2023-07-20 to the latest date when population change data harvested by Data for Good at Meta is available.Population maps provided by Data for Good at Meta are generated based on users of Facebook. For more information about the disaster population maps provided by Data for Good at Meta, please refer to this link.Default data visualizationA divergent color ramp was employed to create a choropleth map for % population change compared to the pre-crisis baseline. The size of pre-crisis baseline is visualized using circles in different sizes. Each polygon represents one Level 3 administrative unit in Greece.This feature layer contains the following metrics for mapping and analysis:Baseline population - an estimated number of Facebook users during the pre-crisis period. It is calculated as an average of 90 days before the crisis (in this case, 2023-07-20 was used as the onset of crisis).Crisis population - an estimated number of Facebook users during the crisis. Original data are provided every 8 hours.Difference in population - the difference between crisis population and the baseline populationPercent change in population - the percentage of population change from baseline to a given date during the crisisZ-score - a unitless normalized measurement to quantify the population change from baselineDate - Date of data acquisition. Original data are provided three times a day (8-hour interval). We calculated a daily average using all three timestamps available for each day. Users can filter by Date to create a subset showing the population change on a selected dateMethod of data preparationRemove data points without a valid baseline population or percent change in populationCalculate daily average using the three timestamps available for each dayAggregate the original point data to Level 3 administrative units of GreeceAppend all daily average level 3 administrative units data to a single file to enable time option of the layer

Abstract: In the last decades, few epidemiological studies have discussed the mortality rates due to leukemia and lymphoma in Brazil. This study analyzes the evolution over time of the number of deaths due to leukemia and lymphoma in Brazil, between 2010 and 2016, considering the population’s characteristics and spatial distribution. This is a retrospective epidemiological study based on data obtained in the Brazilian Health Informatics Department (DATASUS), associated with the quantitative population. We created choropleth maps and predictive models of mortality rates, using the incidence rate ratio (IRR) to measure the size of the effect. Leukemia had a 1.76 higher mortality rate than lymphoma. Leukemia mortality trends increased by 1.2% per year between 2010 and 2016. Regions with the lowest social inequality had higher mortality rates for both diseases. There was a difference between peaks with higher chances of death due to leukemia (> 60 years) and lymphoma (> 70 years). Older age, male, white, and South and Southeast regions were associated with higher mortality by leukemia or lymphoma.