Have you ever wondered how we make sense of the world’s most intricate details, from sprawling agricultural lands to data centers in the ocean? Or how insights from America’s largest water well in Kansas City to towering skyscrapers like the Burj Khalifa are used to drive groundbreaking studies? Behind these discoveries lies a powerful, ever-evolving resource, that is geospatial data.

The growing number of in-orbit satellites generates massive volumes of complex information, creating a significant challenge for web application development while maintaining seamless performance.

So, in this blog, we’ll first delve into the factors causing low geospatial data performance and how they affect web applications with maps. Finally, you’ll learn about the effective tips and tricks to boost application performance when working with large-scale geospatial data.

Let’s get started!

Factors Contributing to Low Geospatial Data Performance

Several factors can contribute to the low performance of geospatial data, including:

Fig: Factors Contributing to Low Geospatial Data Performance

• High volume and complex data: Large datasets with intricate details can overwhelm processing and storage systems.
• Inefficient rendering and user interaction: Poorly optimized map rendering or interactions can lead to slow response times.
• Unnecessary concurrent requests: Multiple simultaneous user requests can strain server capacity and degrade performance.
• Inefficient error handling: Poorly managed errors can disrupt data workflows and slow down operations.
• Incorrect data validation and exceptions: Improper validation or exception handling can result in bottlenecks during data processing.
• Delayed format conversions: Converting between formats (for example, GeoJSON to Shapefile) can be time-consuming and resource-intensive.
• Complex geometry handling: Variations in how different formats manage geometry can introduce inefficiencies and delays.
• Resource-heavy database queries and memory usage: Parsing and validating multiple data formats can increase query complexity and consume excessive memory.

Onwards to discovering the signs of poor geospatial data performance!

Signs of Poor Geospatial Data Performance in Web Mapping Apps

The aforementioned performance challenges don’t just stay behind the scenes—they directly impact how web applications display and interact with maps, making the user experience frustrating.

Note: In this context, “maps” refer to visual tools within web applications that present geospatial data, such as interactive maps used for navigation, data visualization, or location-based services. They rely on smooth rendering and accurate geospatial information to deliver a seamless experience to users.

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Here are some common signs of poor geospatial data performance in web applications using maps:

• Slow zooming and panning slows down
• Lag in map interaction and other features
• Low frame rate
• Incomplete map loading and rendering
• Unresponsive map (no response to different view and screen size)
• Inaccurate data and geolocation for layers and markers

Dealing with these roadblocks can feel overwhelming, right? To help overcome these challenges, I’ve outlined a few effective tips and tricks.

Note: Learn how PostGIS enhances spatial data management by enabling efficient storage, querying, and analysis of geospatial information in PostgreSQL databases. 

Keep reading to learn about the solutions!

8 Proven Techniques for Geospatial Data Performance Optimization

Here are the 8 techniques that will help you optimize the performance of geospatial data:

Fig: 8 Proven Techniques for Geospatial Data Performance Optimization

1. Cache rendered tiles: By caching the already rendered tiles, you can save the time it takes to render and load it repeatedly. Tile caching can:

• improve the performance
• reduce the loads on the server side
• allow more scalability
• load the map in some offline scenarios

Libraries like Leaflet and Open layers have capabilities to cache the tiles, so it is possible to cache any tile layers when using these libraries.

Example: Caching with Leaflet 

The following example demonstrates how to enable caching for tile layers using Leaflet:

var layer = L.tileLayer('https://whatever/{z}/{x}/{y}.png', { 
maxZoom: 18, 
useCache: true,  // By enabling useCache we can cache the tile layer 
crossOrigin: true 
});

In the example above, when a tile is requested, the system first checks the cache storage. If the tile is already cached, it retrieves and uses the stored version. If not, it fetches the tile from the server, stores it in the cache, and makes it available for future requests.

Note: To ensure optimal performance and security, consider the following when implementing tile caching:

• Configure an expiration time for cached tiles
• Set data limits to avoid excessive storage use
• Adopt efficient caching strategies tailored to your application’s requirements

2. Use correct geospatial data format: Use hybrid approach such as loading vector data for detailed view (for example: zoom view 10+) for data interactivity. Use raster data for broader view (for example: zoom view 0-10) where the user does not need interaction with data objects.

By reducing the data transfer for a broader view, one can easily improve the rendering time. However, the user needs an optimal transition approach in different zoom levels (for example: from 10 (raster data) to 10+ (vector data)).

For example:

// Create a leaflet map 

const map = L.map(‘map’).setView([51, 505, -0.09], 12); 

// Add a raster layer for low zoom level (for e.g. 0 to 10) 

const rasterTileLayer = L.tileLayer(‘URL’, {  

maxZoom: 18, 
useCache: true, 
crossOrigin: true 
}).addTo(map); 

// Add a vector layer for high zoom level (for e.g. 10+) 

const vectorTileLayer = L.geoJson(geoJsonFeature, {  

Style: predefinedStyle 
})  

// Function to switch layers based on zoom level 

Function switchLayers() { 

Var zoom = map.getZoom(); 

if (zoom < 10) { 

 // switch to raster layer for low zoom levels 

rasterTileLayers.addTo(map); 

map.removeLayer(vectorTileLayer); 

} else { 

// switch to vector layer for high zoom levels 

vectorTileLayers.addTo(map); 

map.removeLayer(rasterTileLayer); 

} 

} 

// And finally add event listener for zoom event and add switch layers 

map.on(‘zoomend’, switchLayers);

In the example above: 

• We first created a Leaflet map and defined two layers: rasterTileLayer for lower zoom levels and vectorTileLayer for higher zoom levels.
• Next, we implemented a switchLayers() function that dynamically checks the current zoom level and loads the appropriate layer based on the zoom level.
• Finally, we attached the switchLayers() function to the zoomend event, ensuring it is triggered every time the user changes the zoom level.

Bonus read: Read this blog to learn everything about GeoServer. 

3. Group related data: By grouping related data such as lists of specific places and groups of points that are frequently accessed, you reduce the number of requests and improve the load speed of your web application. Creating clusters of data and relationships between them and drawing a specific spatial boundary makes the query faster. It also improves data visualization and insights on spatial data.

4. Auto-adjust zoom: Dynamically adjusting to the bounds of the objects in the map will reduce the load on GeoServer API calls. It will also reduce the reloading of the map several times. It allows users to manually adjust the map which can invoke unnecessary panning and zooming on the map.

Here we can use the techniques which take bounds as the input parameters and set the map view in those geographical areas with the maximum possible fit.

For example, in Leaflet, the fitBounds() technique can be used to implement the auto-zoom functionality. Suppose we have a marker with the following bounds:

const markerBounds = [[52.0, -0.08], [52.02, -0.06]]; 

map.fitBounds(markerBounds)

This code can be triggered by an auto-zoom button click event, automatically adjusting the map view to fit within the specified bounds of the marker.

Note: Learn how to automate end-to-end Cypress testing for geospatial applications. 

5. Implement debouncing:  Introducing a delay in milliseconds between user interactions with the map, such as zooming or panning, helps prevent excessive event triggers. This improves performance by ensuring the map only reloads once the user interaction is complete. It helps speed up the rendering of maps and avoids unnecessary API calls. We can drastically improve the loading of maps by just adding a few milliseconds of timestamp.

Debouncing can be used in the following events to improve the performance of maps:

• Mapping events such as mousemove, mouseover, etc.
• zooming or panning events
• updating markers or features of object
• changing layers or their visibility

6. Monitor performance: Use dev tools to identify the problems and optimize accordingly. Here’s what can be done:

• Track Query execution time, frequency, and resource utilization. Simplifying complex queries, implementing caching mechanisms, and dividing large datasets into chunks improves the loading time.
• Prevent resource exhaustion and ensure system stability by monitoring resource utilization to improve the performance.
• Track error rates to identify areas for improvement, optimize system performance, and enhance user experience. Log management, unit testing, and application performance monitoring tools are used to support this process.

7. Restrict data limit: Create and integrate the Rest APIs in such a way that they load only necessary data which is to be shown on a map. Limiting the data improves front-end loading performance.

8. Utilize map servers for complex geospatial data and transformations:  Map servers are applications that are used to manage geospatial data. They provide various facilities which could be useful for:

• efficient data management
• scalable mapping
• real-time rendering
• robust security and authentication
• flexible data integration
• versatile protocols (for example, WMS, WFS, WCS)

So, performance optimization requires a combination of strategies to reduce data loading and rendering time, while enhancing user experience. By utilizing the above techniques, one can significantly improve the performance of maps.

To learn more about big data and cutting-edge software development services, reach us at Nitor Infotech.