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) 📍

Description

This GeoJSON file contains the coordinates of the Brazilian states.

Inspiration

As a Brazilian, everytime I needed a GeoJSon file of my country, I had to Google for it. So, I decided to save my own version and publish it.

Data files for the examples in the book Geographic Data Science in R: Visualizing and Analyzing Environmental Change by Michael C. Wimberly.

TrashVisualization.RR code that merges and analyzes all of the data. SizesOfObjects:Table of sizes of objects we compare in the VR. WPP2017_POP_F01_1_TOT:United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision, DVD Edition.Population:Cleaned population data from UN data set above taking only 2015.1260352_SupportingFile:Jambeck JR, Geyer R, Wilcox C, Siegler TR, Perryman M, Andrady A, et al. Marine pollution. Plastic waste inputs from land into the ocean. Science. 2015 Feb 13;347(6223):768–71.DetailedSummary-Earth (+1-2):Coastal Cleanup Day Data from 2016-2018 https://www.coastalcleanupdata.org/WCD:World Cleanup Day Data for 2018https://www.letsdoitworld.org/wp-content/uploads/2019/01/WCD_2018_Waste_Report_FINAL_26.01.2019.pdfAnything with the word "Key":A key used for merging country names between data sets.

This data set was acquired with a Video Camera assembled as part of the MARGINS Data Portal. These data files are of Google Earth (KML/KMZ) format include photos and vehicle navigation information.

The interactive map creation tools market is experiencing robust growth, driven by increasing demand for visually engaging data representation across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7.8 billion by 2033. This expansion is fueled by several key factors. The rising adoption of location-based services (LBS) and geographic information systems (GIS) across industries like real estate, tourism, logistics, and urban planning is a major catalyst. Businesses are increasingly leveraging interactive maps to enhance customer engagement, improve operational efficiency, and gain valuable insights from geospatial data. Furthermore, advancements in mapping technologies, including the integration of AI and machine learning for improved data analysis and visualization, are contributing to market growth. The accessibility of user-friendly tools, coupled with the decreasing cost of cloud-based solutions, is also making interactive map creation more accessible to a wider range of users, from individuals to large corporations. However, the market also faces certain challenges. Data security and privacy concerns surrounding the use of location data are paramount. The need for specialized skills and expertise to effectively utilize advanced mapping technologies may also hinder broader adoption, particularly among smaller businesses. Competition among established players like Mapbox, ArcGIS StoryMaps, and Google, alongside emerging innovative solutions, necessitates constant innovation and differentiation. Nevertheless, the overall market outlook remains positive, with continued technological advancements and rising demand for data visualization expected to propel growth in the coming years. Specific market segmentation data, while unavailable, can be reasonably inferred from existing market trends, suggesting a strong dominance of enterprise-grade solutions, but with substantial growth expected from simpler, more user-friendly tools designed for individuals and small businesses.

HazMatMapper is an online and interactive geographic visualization tool designed to facilitate exploration of transnational flows of hazardous waste in North America (http://geography.wisc.edu/hazardouswaste/map/). While conventional narratives suggest that wealthier countries such as Canada and the United States (US) export waste to poorer countries like Mexico, little is known about how waste trading may affect specific sites within any of the three countries. To move beyond anecdotal discussions and national aggregates, we assembled a novel geographic dataset describing transnational hazardous waste shipments from 2007 to 2012 through two Freedom of Information Act requests for documents held by the US Environmental Protection Agency. While not yet detailing all of the transnational hazardous waste trade in North America, HazMatMapper supports multiscale and site-specific visual exploration of US imports of hazardous waste from Canada and Mexico. It thus enables academic researchers, waste regulators, and the general public to generate hypotheses on regional clustering, transnational corporate structuring, and environmental justice concerns, as well as to understand the limitations of existing regulatory data collection itself. Here, we discuss the dataset and design process behind HazMatMapper and demonstrate its utility for understanding the transnational hazardous waste trade.

This site provides free access to Iowa geographic map data, including aerial photography, orthophotos, elevation maps, and historical maps. The data is available through an on-line map viewer and through Web Map Service (WMS) connections for GIS. The site was developed by the Iowa State University Geographic Information Systems Support and Research Facility in cooperation with the Iowa Department of Natural Resources, the USDA Natural Resources Conservation Service, and the Massachusetts Institute of Technology. This site was first launched in March 1999.

In response to the growing concern in geographic information science, which pertains to utilizing contemporary internet technology to communicate past information or knowledge for establishing foundations in geography. Recent studies have investigated geomatics solutions for historical city, and enhancing GIS skills through collaborative approach. In this study, we build upon prior research by exploring how the implementation of current technology can promote a cooperative learning environment, particularly within the realm of forestry education. Minetest, the 3D voxel game engine has high capability of modification, for visualizing natural environments and urban structures. The goal of this study was to investigate the potential of using the game engine for forestry education purposes. To meet this objective, we developed precise and detailed models of building structures and their surrounding environment. We also explored the visualization beyond 3D geospatial data, by generating analytical results of solar radiation on building roofs using GIS software. The visualization process was facilitated by the use of 3D light detection and ranging (LiDAR) data, provided by the UBC Campus + Community Planning department. The proposed approach proved to be effective in producing compatible geospatial data for visualization in the game engine. We also conducted exploratory statistical analysis to examine the relationship between building energy usage and solar radiation. The exploratory regression result of the solar radiation analysis has an R2adj of 0.19, which indicates its insignificant impact on building energy usage. Finally, the findings of this research provide a foundation for future studies that can continue to explore the potential of using 3D game engines. Keywords: 3D Geo-Visualization, Forestry Education, Remote Sensing, Light Detection and Ranging (LiDAR), Building Energy Usage, Solar Radiation Analysis

Keep up to date on data visualization technologies - Assess tools and keep a list of required functionalities - Be informed and prepared should a funding opportunity arise.

The Digital Geologic-GIS Map of San Miguel Island, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (smis_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (smis_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (smis_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (smis_geology_metadata_faq.pdf). Please read the chis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (smis_geology_metadata.txt or smis_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Intrinsic tag maps fit a tag cloud inside a geographic boundary to emphasize the association of the tags with a particular administrative region. So far, little is known about their utility and usability. Here, we present the results of an empirical study to help fill this gap. The study uses information retrieval tasks to evaluate intrinsic tag map utility and uses user confidence and preference judgments as a metric of usability. Key independent variables in the empirical study include tag orientation and shape of geographic territory tags are positioned within. The user responses show that the intrinsic tag maps have good performance in some gisting tasks and can be used with great confidence. However, the performance degrades when the intrinsic tag maps are used to search for specific tags. The user responses also show that the readability and layout of the intrinsic tag maps needs improvement. Additionally, results show that geographic territory shape has a significant effect on the information retrieval and both geographic territory shape and tag orientation have a significant effect on the confidence, readability, and preference of the intrinsic tag map. Overall, our research results can be used to improve tag map designs to achieve better utility and usability and as the starting point for subsequent tag map research.

Provided By: United States Census Bureau Source: https://www2.census.gov/geo/tiger/TIGER2022/COUNTY/ Source Description: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html

This data set was acquired with a Video Camera assembled as part of the 2007 R2K_GoogleEarth data compilation (Chief Scientist: MGDS; Investigator(s): Dr. Vicki Ferrini). These data files are of Google Earth (KML/KMZ) format include photos and vehicle navigation information.

This research explores the innovative use of a 3D gaming engine, Minetest, for visualizing changes in canopy cover change at the University of British Columbia (UBC) campus, addressing the pressing challenge of urban expansion on green spaces. We compared and visualized canopy height change for UBC campus in both 2D traditional environment and 3D gaming engine environment and we revealed a consistency between the spatial patterns of canopy cover change observed in both environments. Our findings indicate 3D environment provided multi-dimensional insights into canopy cover changes, offering decision-makers more straightforward and transparent insight than traditional maps can achieve in an immersive and interactive environment. We observed there is a significant change in canopy cover with 25 percent loss in total where Wesbrook community area experienced the most significant canopy cover loss in past 5 years due to rapid urban development. Our findings goes beyond merely presenting geographic maps and attributes from a 3D voxel game perspective. Instead, it will serve as a useful tool and references for UBC decision makers and planners to inform management plan on the pathway of building a green, well-planned community.

This data set was acquired with a Video Camera assembled as part of the 2003 MGDS_GoogleEarth data compilation (Chief Scientist: MGDS; Investigator(s): Dr. Vicki Ferrini). These data files are of Google Earth (KML/KMZ) format and include Google Earth Visualization data and were processed after data collection.

🌍 Ultimate Geographic Data Collection | Cities & Zip Codes

📌 Overview

Welcome to the Ultimate Geographic Data Collection, a comprehensive dataset providing valuable geographic insights. This dataset includes U.S. Zip Codes, U.S. Cities, and World Cities data, making it an essential resource for developers, data analysts, and researchers. Whether you're building location-based applications, conducting geographic analysis, or working on machine learning projects, this dataset offers an extensive and curated collection of location-based information.

📊 What's Inside?

• U.S. Zip Codes Database (Free Version) 🏙️

  • Includes ZIP codes, state associations, and geographic coordinates.
  • 🔗 Usage Condition: Requires a visible backlink to SimpleMaps US Zip Code Database.

• U.S. Cities Database (Free Version) 🌆

  • Includes city names, state information, latitude, longitude, and population data.
  • 🔗 Usage Condition: Requires a visible backlink to SimpleMaps US Cities Database.

• Basic World Cities Database 🗺️

  • Provides global city data licensed under Creative Commons Attribution 4.0.
  • 📜 Learn more: CC BY 4.0 License.

• Comprehensive & Pro World Cities Database (Density Data) 🌎

  • Population density estimates sourced from CIESIN - Columbia University.
  • 🔗 Licensed under Creative Commons Attribution 4.0 with no additional restrictions.

⚖️ License & Usage Terms

• ✅ You CAN:

  • Use this dataset in private and public-facing applications.
  • Create copies and backups for your projects.
  • Transfer the license (with provider approval via email).

• 🚫 You CANNOT:

  • Redistribute the dataset publicly without written permission.
  • Use it in a way that violates any laws.
  • Bypass the backlink requirement (for free U.S. Zip Code & Cities Databases).

🛠️ How to Use

1. Download the dataset 📥.
2. Ensure compliance with licensing terms.
3. Use it in your projects for analysis, visualization, or machine learning.
4. Provide attribution (if applicable) for free datasets.

⚠️ Disclaimer

• This dataset is provided "AS IS", without any warranties.
• The provider is not liable for any issues arising from usage.
• Users are responsible for ensuring legal compliance in their jurisdiction.

🔥 Get Started!

Enhance your geographic projects with this powerful dataset today! 🚀

📩 For any inquiries, licensing requests, or attribution clarifications, contact the dataset provider.

NDS is an interactive, web-based system, for the visualization of multidimensional neighborhood dynamics across the 50 largest US Metropolitan Statistical Areas (MSAs) from 1980 to 2010 (http://neighborhooddynamics.dreamhosters.com). Four different visualization tools are developed: (1) an interactive time slider to show neighborhood classification changes for different years; (2) multiple interactive bar charts for each variables of each neighborhood; (3) an animated neighborhood’s trajectory and sequence cluster on a self-organizing map (SOM) output space; and (4) a synchronized visualization tool showing maps for four time stamps at once. The development of this interactive online platform for visualizing dynamics overcomes many of the challenges associated with communicating changes for multiple variables, across multiple time stamps, and for a large geographic area when relying upon static maps. The system enables users to select and dive into details on particular neighborhoods and explore their changes over time.

This data set was acquired with a Video Camera assembled as part of the 2007 R2K_GoogleEarth data compilation (Chief Scientist: MGDS; Investigator(s): Dr. Vicki Ferrini). These data files are of Google Earth (KML/KMZ) format include photos and vehicle navigation information.

The datasets used for this manuscript were derived from multiple sources: Denver Public Health, Esri, Google, and SafeGraph. Any reuse or redistribution of the datasets are subjected to the restrictions of the data providers: Denver Public Health, Esri, Google, and SafeGraph and should consult relevant parties for permissions.1. COVID-19 case dataset were retrieved from Denver Public Health (Link: https://storymaps.arcgis.com/stories/50dbb5e7dfb6495292b71b7d8df56d0a )2. Point of Interests (POIs) data were retrieved from Esri and SafeGraph (Link: https://coronavirus-disasterresponse.hub.arcgis.com/datasets/6c8c635b1ea94001a52bf28179d1e32b/data?selectedAttribute=naics_code) and verified with Google Places Service (Link: https://developers.google.com/maps/documentation/javascript/reference/places-service)3. The activity risk information is accessible from Texas Medical Association (TMA) (Link: https://www.texmed.org/TexasMedicineDetail.aspx?id=54216 )The datasets for risk assessment and mapping are included in a geodatabase. Per SafeGraph data sharing guidelines, raw data cannot be shared publicly. To view the content of the geodatabase, users should have installed ArcGIS Pro 2.7. The geodatabase includes the following:1. POI. Major attributes are locations, name, and daily popularity.2. Denver neighborhood with weekly COVID-19 cases and computed regional risk levels.3. Simulated four travel logs with anchor points provided. Each is a separate point layer.