How to visualize spatial data online effectively?
Spatial data visualization is indispensable to modern Geographic Information Systems (GIS), remote sensing, and geospatial analysis. Spatial data visualization turns geographic data into interactive 2D and 3D maps, allowing experts to interpret spatial relationships, discover patterns, and present geographic data effectively.
As satellite images, aerial photographs taken by drones, LiDAR point clouds, DEMs, and SAR data become more common, there is an increasing need for organizations to find ways of visualizing their big geospatial data sets online.
Online spatial data visualization provides an opportunity for users to access geographic data using web browsers rather than GIS software installed on personal computers.
What Is Spatial Data Visualization?
Spatial data visualization refers to the graphical display of geographic data through maps, graphs, 3D visualizations, and geospatial interfaces.
It enables viewers to analyze the location, spatial distribution, attribute characteristics, elevation, and interaction between geographic data.
Spatial data can be categorized into two main types:
Vector Data Visualization
Vector data involves the use of points, lines, and polygon shapes to depict geographic features.
Applications include:
Points: Global positioning system locations, monitoring stations, landmarks, and infrastructure facilities.
Lines: Roads, waterways, oil pipelines, and transport routes.
Polygons: Geographic boundaries, land parcels, farmland plots, and building footprints.
Some examples of vector formats include GeoJSON, Shapefile, GeoPackage, and GeoParquet.
Vector visualization mainly relies on varying colors, symbols, line weights, and classifications.
Raster Data Visualization
Raster data is the type of geographic data represented by pixels or cells, where each cell has a numeric or categorical value.
Examples include:
Land cover maps
Temperature and precipitation datasets
Vegetation indices (e.g., NDVI)
SAR imagery
Visualizing raster data usually requires the use of color ramps, band compositions, transparency, and value enhancement.
How to Visualize Spatial Data Online Effectively
An ideal visualization process on the web starts with data preparation, selection of the best rendering technique, performance optimization, and presentation using an interactive web-based interface.
Step 1: Prepare and Validate Geospatial Data
Prior to publication of spatial datasets on the web, it is necessary to ensure that they are prepared correctly and spatially referenced.
Key preparation steps should include:
Coordinate Reference System (CRS): Ensure that the CRS of your dataset is correct. Web mapping applications often use Web Mercator (EPSG:3857) projection for map rendering, whereas geographic datasets can use WGS 84 (EPSG:4326) coordinate system.
Geometry validation: Verify vector geometries for the presence of invalid polygons, duplicates, and other geometry-related issues.
Raster validation: Make sure that rasters are properly checked for dimensions, pixel size, no data values, band count, and georeferencing.
Data compression: Proper data compression should be done to reduce data storage needs and improve data transfer speed.
In case of large raster datasets, it is important to use Cloud Optimized GeoTIFF (COG).
Step 2: Select the Appropriate Visualization Technique
Different spatial datasets require different visualization methods.
Spatial Data Type | Recommended Visualization |
Vector data | Interactive 2D maps, symbols, and thematic styling |
Satellite imagery | RGB composites and multispectral band combinations |
DEM | Elevation color ramps, hillshade, and 3D terrain |
LiDAR | 3D point clouds and elevation-based coloring |
SAR | Backscatter visualization and polarization composites |
Time-series raster | Temporal animation and interactive sliders |
Land cover | Categorical color classification |
Selecting the correct visualization method improves readability and helps users interpret spatial information accurately.
Step 3: Use Interactive Web Mapping Technologies
Interactive web maps enable users to zoom, pan, overlay layers, analyze geographic features, and view their spatial characteristics.
There are many open-source tools for visualization of spatial data on the internet.
Leaflet
Leaflet is a lightweight JavaScript library for creating interactive web maps.
It supports markers, vector overlays, popups, map controls, and tile-based basemaps.
Typical applications include location maps, asset monitoring, field data visualization, and lightweight GIS applications.
OpenLayers
OpenLayers provides advanced mapping capabilities for web-based GIS applications.
It supports multiple geographic data sources, coordinate transformations, vector rendering, raster layers, and Web Map Service (WMS) integrations.
OpenLayers is useful for applications requiring complex layer management and geospatial interactions.
MapLibre GL JS
MapLibre GL JS uses WebGL-based rendering to display interactive maps with vector tiles, raster tiles, and customizable map styles.
It supports smooth zooming, dynamic styling, and large-scale geographic visualization.
Step 4: Visualize Raster Data Efficiently
Visualization of large rasters can cause performance issues because large raster datasets are not easy to visualize directly in the browser.
The following approaches can be used to optimize visualization performance:
Conversion of the applicable raster datasets into Cloud Optimized GeoTIFFs.
Creation of image pyramids or overviews to enable multiresolution visualization.
Tiling of the raster services to provide only the requested geographic area.
Resampling in case of a change in visualization scale.
Use of raster color maps and contrast stretch for better visualization.
Caching of the map tiles.
For example, one single satellite image of the whole country can have billions of pixels. The visualization of the entire raster dataset is not required if the user wants to see the entire country.
Multiresolution visualization helps the application visualize a low-resolution version at a small scale and gradually visualize higher-resolution images as the user zooms in.
Improve Performance When Visualizing Large Spatial Datasets
It is vital to optimize performance while developing web-based GIS applications that involve high-resolution imagery, vector data, and 3D geospatial data.
Use Cloud-Optimized Formats
Cloud-Optimized GeoTIFF can make use of partial access to the raster dataset by making HTTP range requests from a suitable server.
When it comes to vector datasets, there are options like GeoParquet and tiled vector data that could be considered.
Implement Multiscale Rendering
Utilize multiple levels of detail to visualize geographic datasets at various zoom levels.
Examples include:
For country-scale visualization: Low-resolution raster overviews.
For regional-scale visualization: Medium-resolution imagery.
For local-scale visualization: High-resolution imagery and vector data.
Apply Spatial Indexing
Spatial indexes can help the application query geographic features in an efficient manner.
The most common type of index for spatial queries is R-tree indexing.
Use Tile-Based Delivery
Tile-based rendering splits up the geographic data into small parts that can be fetched separately.
Some common examples are:
XYZ tiles
Vector tiles
Web Map Tile Service (WMTS)
Web Map Service (WMS)
Elevation Tiles
Optimize Browser Rendering
Utilize WebGL wherever possible for visualization at large scale, geometry simplification at smaller scale, and also avoid transferring unnecessary attributes or features to the browser.
How AI Is Transforming Online Spatial Data Visualization
AI is enhancing online geospatial systems by merging visualization with automated feature extraction, classification, segmentation, and analysis.
AI-driven processes could enable users to analyze geospatial data faster and more efficiently.
Some examples include:
Detection of buildings and roads in satellite images.
Classification of land use types based on multispectral imagery.
Extraction of vegetation and infrastructure features.
Detection of changes between two satellite observations.
Analysis of terrain/elevation characteristics.
Geoanalysis of SAR images.
Integration of AI and web GIS would allow for creating a workflow where users would visualize geospatial data, perform analytical processes, and analyze their results all in one place.
GeoWGS84.ai: Upcoming Spatial Data Visualization Capabilities
The AI platform that GeoWGS84.ai plans to develop includes functionality for visualizing various types of spatial data, such as raster, vector, DEM, LiDAR, and SAR data sets.
It is clear that this set of capabilities should allow for expanding the geospatial capability of the platform by including spatial data visualization in its AI-centered ecosystem.
The ability to visualize different types of geospatial data should allow meeting the needs for visualization of satellite imagery, terrain data, vector data, point clouds, and even radar-based remote sensing data sets.
When the geospatial platforms continue to evolve, their integration with the data visualization capability should become a part of the process.
The visualization of spatial data on the web is more than just presenting geographic information on a map. It consists of choosing appropriate visualization techniques, data preparation, optimal data transmission, and interactive rendering technology.
There are specific visualization methods that should be applied to vector layers, raster imagery, DEM, LiDAR point clouds, and SAR data in order to illustrate their geographic features.
Tools like Leaflet, OpenLayers, MapLibre GL JS, Cloud Optimized GeoTIFF, vector tiles, and WebGL form the basis of scalable online geospatial visualization.
The incorporation of AI into GIS platforms will make it possible to use the combination of spatial data visualization and geospatial analysis automated by AI algorithms to work with geographic information.
Since the next version of GeoWGS84.ai, which will allow visualization of raster, vector, DEM, LiDAR, and SAR data, will be released soon, GeoWGS84.ai is extending its capabilities as an AI platform to cover geospatial visualization processes.
For more information or any questions regarding spatial data, please don't hesitate to contact us at
Email: info@geowgs84.com
USA (HQ): (720) 702–4849
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