fastgeotoolkit

fastgeotoolkit is a library for GPS data processing and route density mapping. The core of the library is written in Rust and it's compiled to webassembly for use in the browser and node.

What it does

The main use case is creating route heatmaps where you want to see which paths/routes are used most frequently. You can test this functionality at https://fastgeotoolkit-demo.pages.dev/, using either your own data or sample data. This is an example of what a heatmap produced using fastgeotoolkit looks like:

However, beyond this primary usecase, this library helps you:

• Analyze GPS tracks (distance, statistics, intersections)
• Decode Google polylines
• Convert between GPS data formats

Documentation

Docs are available at https://fastgeotoolkit.pages.dev/.

Installation

npm install fastgeotoolkit
# or 
pnpm i fastgeotoolkit

Basic Usage

import { processGpxFiles } from 'fastgeotoolkit';

// Process GPX files into a heatmap
const gpxFile1 = new Uint8Array(/* your GPX file data */);
const gpxFile2 = new Uint8Array(/* another GPX file */);

const result = await processGpxFiles([gpxFile1, gpxFile2]);

// Result contains tracks with frequency data
console.log(`Found ${result.tracks.length} unique track segments`);
console.log(`Maximum frequency: ${result.max_frequency}`);

result.tracks.forEach(track => {
  console.log(`Track with ${track.coordinates.length} points, used ${track.frequency} times`);
});

Working with Polylines

import { decodePolyline, processPolylines } from 'fastgeotoolkit';

// Decode a single polyline
const coords = await decodePolyline('_p~iF~ps|U_ulLnnqC_mqNvxq`@');
console.log(coords); // [[lat, lng], [lat, lng], ...]

// Process multiple polylines into a heatmap
const polylines = [
  '_p~iF~ps|U_ulLnnqC_mqNvxq`@',
  'another_encoded_polyline',
  'yet_another_one'
];
const heatmap = await processPolylines(polylines);

Track Analysis

import { calculateTrackStatistics, validateCoordinates } from 'fastgeotoolkit';

const coordinates = [[37.7749, -122.4194], [37.7849, -122.4094]]; // [lat, lng] pairs

// Get basic statistics
const stats = await calculateTrackStatistics(coordinates);
console.log(`Distance: ${stats.distance_km.toFixed(2)} km`);
console.log(`${stats.point_count} GPS points`);
console.log(`Bounds: ${stats.bounding_box}`); // [min_lat, min_lng, max_lat, max_lng]

// Validate coordinates
const validation = await validateCoordinates(coordinates);
console.log(`${validation.valid_count} out of ${validation.total_count} coordinates are valid`);
if (validation.issues.length > 0) {
  console.log('Issues found:', validation.issues);
}

Data Conversion

import { coordinatesToGeojson, exportToGpx } from 'fastgeotoolkit';

// Convert to GeoJSON
const geojson = await coordinatesToGeojson(coordinates, {
  name: 'My Route',
  activity: 'cycling'
});

// Export multiple tracks as GPX
const tracks = [track1_coordinates, track2_coordinates];
const gpxString = await exportToGpx(tracks, {
  creator: 'My App',
  name: 'Route Collection'
});

Real-world Example

Here's an example of how you might use this in a web app to show route popularity:

import { processGpxFiles } from 'fastgeotoolkit';

async function createHeatmap(gpxFiles) {
  // Convert files to Uint8Array
  const fileBuffers = await Promise.all(
    gpxFiles.map(file => file.arrayBuffer().then(buf => new Uint8Array(buf)))
  );
  
  // Process into heatmap
  const heatmap = await processGpxFiles(fileBuffers);
  
  // Render on map (example with any mapping library)
  heatmap.tracks.forEach(track => {
    const intensity = track.frequency / heatmap.max_frequency;
    const color = `hsl(${(1-intensity) * 240}, 100%, 50%)`; // blue to red
    
    drawLineOnMap(track.coordinates, {
      color: color,
      weight: Math.max(2, intensity * 8)
    });
  });
}

// Usage
document.getElementById('file-input').addEventListener('change', async (e) => {
  const files = Array.from(e.target.files);
  await createHeatmap(files);
});

TypeScript Support

The library includes full TypeScript definitions:

import type { 
  Coordinate,        // [number, number] - [lat, lng]
  HeatmapResult,     // { tracks: HeatmapTrack[], max_frequency: number }
  HeatmapTrack,      // { coordinates: Coordinate[], frequency: number }
  TrackStatistics,   // distance, bounds, point count, etc.
  ValidationResult,  // validation results with issues
  FileInfo          // file format information
} from 'fastgeotoolkit';

JavaScript Utilities

For simple operations that don't rely on WebAssembly:

import { utils } from 'fastgeotoolkit';

// Basic coordinate validation
if (utils.isValidCoordinate(37.7749, -122.4194)) {
  console.log('Valid GPS coordinate');
}

// Calculate distance between two points
const distance = utils.haversineDistance(37.7749, -122.4194, 37.7849, -122.4094);
console.log(`Distance: ${distance.toFixed(2)} km`);

// Get bounding box
const bounds = utils.getBoundingBox(coordinates);
console.log(`Bounds: ${bounds}`); // [min_lat, min_lng, max_lat, max_lng]

Browser vs Node.js

Works the same in both environments:

// Browser
import { processGpxFiles } from 'fastgeotoolkit';

// Node.js  
const { processGpxFiles } = require('fastgeotoolkit');
// or with ES modules:
import { processGpxFiles } from 'fastgeotoolkit';

Performance Notes

• WebAssembly provides near-native performance for GPS processing
• Large datasets (thousands of tracks) process quickly
• First function call initializes WebAssembly (adds ~100ms startup time)

Common Issues

"Cannot resolve module" errors: Make sure your bundler supports WebAssembly. Modern bundlers (Vite, Webpack 5+, etc.) work out of the box.

TypeScript errors: Ensure you're using TypeScript 4.0+ for proper WebAssembly typing support.

File reading: Remember to convert File objects to Uint8Array:

const buffer = await file.arrayBuffer();
const uint8Array = new Uint8Array(buffer);

License

MIT