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.

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

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 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 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.

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.

Map Visualization example of RHESSys output at Coweeta subbasin18

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

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.

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

The number of dataset files divided into the original published studies (original) and expert-modified distributions (expert) with two overall time periods.

Folium makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing rich vector/raster/HTML visualizations as markers on the map. These files can be used to mark the state boundaries on the map of INDIA using folium library and the CSV also contains the state data and how to use it in our notebooks. I have used it in one of my kernels which can be viewed.

The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys. folium supports both Image, Video, GeoJSON, and TopoJSON overlays. Due to extensible functionalities I find folium the best map plotting library in python. Do give it a try and use it in your kernels.

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.

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

COVID-19, India This tutorial help in understanding basics of data visualization and mapping using Python.

Content

Data sets contain State wise confirmed cases, death toll, and cured cases till date.

Acknowledgements

I owe my thanks to the data sets provider.

Inspiration

Data visualization helps in creating trends, patterns, interactive graphs and maps. This will help policy and decision makers to understand,discuss and visualize the data.

Recommendations for the suitable contents of the geospatial datasets presenting the distribution of languages including the benefits of each, and our solutions (selected in the case study) concerning the Uralic languages.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Tomales Point map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Tomales Point map area data layers. Data layers are symbolized as shown on the associated map sheets.

These is an examples to test Data Processing Kernel in CyberGIS-Jupyter for water. The 2_map_visualization folder is an example of an interactive map visualization which is the high-level visualization using PyViz tools as post-processing of environmental modeling. For this example, we used the following PyViz tools: - geopandas (https://geopandas.org/), cartopy (https://scitools.org.uk/cartopy/), geoviews (https://geoviews.org/), and holoviews (https://holoviews.org/)

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.

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/).