Using ArcGIS, anyone can quickly make and share a map-but creating an effective map requires knowing a few design fundamentals. Enroll in this plan to learn techniques to appropriately symbolize and label map features, apply settings that enhance user interaction with your maps, and create impactful data visualizations that resonate with your intended audience.Goals Choose appropriate map symbols to represent your data. Create attractive labels to provide information about map features. Visualize data in 2D and 3D.

This map contains multibeam sonar survey data collected during the 2021 field project. This file supports the New Technology and the Search for Historic Shipwrecks StoryMap created by the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) and Office of National Marine Sanctuaries (ONMS). The StoryMap can be viewed here. The StoryMap was funded through NOAA's Office of Ocean Exploration and Research. More information on the project can be found here. All project files are stored in the NOAA National Centers for Environmental Information.

The 3D Visualisation Map (Tile-based models) are based on the mesh model made from the oblique aerial images. The dataset covers the whole territory of Hong Kong. You can click the link below to access the 3D Visualisation Map (https://3d.map.gov.hk/).

Discover the booming interactive map creation tools market! This in-depth analysis reveals a $2.5 billion market in 2025, projected to reach $8 billion by 2033, driven by cloud-based solutions and growing data visualization needs. Learn about key players, market segmentation, and regional trends shaping this exciting sector.

Discover how to display and symbolize both 2D and 3D data. Search, access, and create new map symbols. Learn to specify and configure text symbols for your map. Complete your map by creating an effective layout to display and distribute your work.

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) 📍

3D Visualisation Map (2017)

The dataset includes cartographic visualization data and software designed, implemented, and published for the ARCHITRAVE research project website. The research focused on the edition, executed in German and French, of six travelogues by German travelers of the Baroque period who visited Paris and Versailles. The edited texts are published in the Textgrid repository. For all further information on the content and objectives of the research, please refer to the website (https://architrave.eu/) and given literature. Three visualizations were created for the website: the travel stops of five of the travelers on their way to Paris and Versailles the sites in Europe mentioned in the six travelogues the sites in Paris described by the six travelers The visualizations were implemented with Leaflet.js. The dataset contains scripts for data crunching processed geodata scripts for leaflet.js License README

The land use legend originates from the CORINE land cover project. It is a tessellation of artificially modeled terrains, agricultural territories, wooded territories and semi-natural environments, wetlands, waters, etc. - Coverage: Entire Regional Territory - Origin: Photo-interpretation and aerial shots in B/W or in color at 1:13000 scale.

Download statistics and trends for 141 plugins in the Visualization & Mind Maps category

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.

The DC Office of Zoning (OZ) proudly announces an expansion of its online mapping services with the release of the DCOZ 3D Zoning Map. This new mapping application builds off existing DC Open Datasets and new OZ Zoning data to visualize the District in 3D, providing greater context for proposed development projects and helping enhance Board of Zoning Adjustment and Zoning Commission decisions throughout the District. The 3D Zoning Map was developed to enhance District resident’s understanding, knowledge, and participation in Zoning matters, and help increase transparency in the Zoning process.

Map Visualization example of RHESSys output at Coweeta subbasin18

Maps are currently experiencing a paradigm shift from static representations to dynamic platforms that capture, visualize and analyse new data, bringing different possibilities for exploration and research. The first objective of this paper is to present a map that illustrates, for the first time, the real flow of casual cyclists and bike messengers in the city of Madrid. The second objective is to describe the development and results of the Madrid Cycle Track initiative, an online platform launched with the aim of collecting cycling routes and other information from volunteers. In the framework of this initiative, different online maps are presented and their functionalities described. Finally, a supplemental video visualizes the cyclist flow over the course of a day.

High-Precision 3D Map Market Outlook

The global market size for High-Precision 3D Maps was valued at approximately USD 6.3 billion in 2023 and is projected to reach around USD 21.8 billion by 2032, growing at a robust CAGR of 15.2% during the forecast period. One of the remarkable growth factors driving this market is the increasing demand for autonomous vehicles, which rely heavily on high-precision 3D maps for navigation and safety.

The burgeoning adoption of autonomous vehicles stands as a cornerstone of growth in the High-Precision 3D Map Market. Companies like Tesla, Waymo, and Uber are investing significantly in the development of self-driving technologies, which necessitate the use of high-accuracy 3D maps to operate effectively. These maps provide detailed spatial data, crucial for safe and efficient navigation, thereby driving their demand. Additionally, advancements in sensor technologies, including LiDAR and radar, have made the creation of high-precision 3D maps more accurate and cost-effective, further propelling market growth.

Another notable growth driver is the rapid urbanization and infrastructure development across the globe. Smart city initiatives, which involve the deployment of advanced technologies for efficient urban management, rely heavily on high-precision 3D maps. These maps are used for planning, construction, and maintenance of urban infrastructure, ensuring seamless integration of various city components. Governments and private sectors are increasingly investing in these technologies to enhance urban living conditions, thereby fueling the demand for high-precision 3D maps.

Additionally, the growing application of high-precision 3D maps in sectors such as healthcare and logistics is opening new avenues for market expansion. In healthcare, for instance, 3D maps are used for precise surgical planning and navigation, improving patient outcomes. In logistics, high-precision 3D maps enable optimized route planning and real-time tracking, enhancing efficiency and reducing costs. The increasing integration of these maps in various industries underscores their versatile utility and potential for growth.

The concept of 3D Mapping has revolutionized the way spatial data is utilized across various sectors. In the realm of urban development, 3D Mapping provides a comprehensive view of city landscapes, enabling planners to visualize and simulate urban growth effectively. This technology aids in identifying potential areas for development, assessing environmental impact, and optimizing land use. By integrating 3D Mapping with Geographic Information Systems (GIS), urban planners can create dynamic models that reflect real-world scenarios, facilitating informed decision-making processes. The ability to visualize complex data in three dimensions enhances the accuracy and efficiency of urban planning, contributing to the sustainable development of smart cities.

On a regional level, North America currently holds a significant share in the High-Precision 3D Map Market, driven by technological advancements and substantial investments in autonomous vehicles and smart city projects. However, the Asia Pacific region is expected to witness the highest growth rate, attributed to rapid urbanization, increasing adoption of advanced technologies, and supportive government initiatives. Countries like China, Japan, and South Korea are at the forefront of this growth, making significant strides in smart infrastructure and autonomous vehicle development.

Component Analysis

The High-Precision 3D Map Market is segmented into three main components: Hardware, Software, and Services. Each of these segments plays a crucial role in the overall functionality and adoption of high-precision 3D mapping technologies.

The Hardware segment encompasses various physical devices required for capturing, storing, and processing spatial data. This includes advanced sensors like LiDAR, cameras, and GPS systems. The increasing demand for high-accuracy and high-resolution data capture has driven significant advancements in hardware technologies. For instance, modern LiDAR systems offer greater accuracy and range, enabling the creation of more detailed and precise 3D maps. With continuous innovations in sensor technology, the Hardware segment is expected to maintain a steady growth trajectory.

On the other hand, the Software segment involves applications and platforms used to process, analyze,

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).

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 case study includes multiple workflows, visualizing global countries' COVID-19 cases as dynamic maps, such as HTML, GIF, and MP4.

The Flood Inundation Mapping (FIM) Visualization Deck is a web-based application designed to display and compare flood extent and depth information across various temporal and scenario conditions. It provides a front-end interface for accessing geospatial flood data and interacting with mapped outputs generated from hydraulic modeling.

Core Functions: • Flood Extent Mapping: Visualizes flood extents from modeled scenarios (e.g., 2-year, 10-year, 100-year events) and real-time conditions based on streamflow observations or forecasts. • Flood Depth Visualization: Displays depth rasters over affected areas, derived from hydraulic simulations (e.g., HEC-RAS). • Scenario Comparison: Allows side-by-side viewing of multiple FIM outputs to support calibration or decision analysis. • Layer Management Toolbox: Users can toggle basemaps, adjust layer transparency, load datasets, and control map extents.

Data Inputs: • Precomputed flood inundation extents (raster/tile layers) • Depth grids • Stream gauge metadata • Associated hydraulic model outputs

Technical Stack: • Front-end: Built with JavaScript, primarily using Leaflet.js for interactive map rendering. • Back-end Services: Uses GeoServer to serve raster tiles and vector layers (via WMS/WFS). Uses OGC-compliant services and REST endpoints for data queries. • Data Formats: Raster layers (e.g., GeoTIFF, PNG tiles), vector layers (GeoJSON, shapefiles), elevation models, and model-derived grid outputs. • Database: Integrates with a PostgreSQL/PostGIS backend or similar spatial database for hydrologic and geospatial data management. • Deployment: Hosted via University of Iowa infrastructure, with modular UI elements tied to specific watersheds or study areas.

Intended Use: The application provides a reference and exploratory tool for comparing modeled flood scenarios, visualizing extent and depth data, and interacting with region-specific inundation data products.

Context

When I started exploring how to create interactive maps (using the leaflet() package in R) I come across this free data set (shapefile format) that contains the geographical coordinates (polygons) for all the countries in the world. I thought it would be nice to share this with the Kaggle community.

Content

The .zip folder contains all the necessary files needed for the shapefile data to work properly on your computer. If you are new to using the shapefile format, please see the information provided below:

https://en.wikipedia.org/wiki/Shapefile "The shapefile format stores the data as primitive geometric shapes like points, lines, and polygons. These shapes, together with data attributes that are linked to each shape, create the representation of the geographic data. The term "shapefile" is quite common, but the format consists of a collection of files with a common filename prefix, stored in the same directory. The three mandatory files have filename extensions .shp, .shx, and .dbf. The actual shapefile relates specifically to the .shp file, but alone is incomplete for distribution as the other supporting files are required. "

Acknowledgements

Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com.