EarthPlot for Developers: APIs, Examples, and Best PracticesEarthPlot is a powerful visualization library designed to help developers render geospatial data on interactive 2D and 3D maps with high performance and aesthetic flexibility. This article covers EarthPlot’s core concepts, API structure, practical examples in JavaScript and Python, performance and integration tips, and best practices for building production-grade geospatial applications.
What is EarthPlot?
EarthPlot is a client-side and server-friendly library for visualizing geospatial datasets. It focuses on:
- High-performance rendering of large datasets (points, lines, polygons).
- Multiple projection and coordinate system support.
- Layered visualization with customizable styles and animations.
- Interoperability with common geospatial formats (GeoJSON, TopoJSON, shapefiles) and tile services (XYZ, WMTS).
- Extensible API for plugins and integrations (data sources, analytics, UI components).
Core Concepts and Architecture
- Layers: Each visual element is a layer (points, lines, polygons, heatmaps, tiles). Layers can be composed and reordered.
- Data Sources: EarthPlot supports direct GeoJSON, vector tiles, server-streamed data (WebSocket/Server-Sent Events), and raster tiles.
- Renderers: Uses WebGL for GPU-accelerated rendering; falls back to Canvas when WebGL is unavailable.
- Projections: Built-in projections (Web Mercator, equirectangular, orthographic) and hooks for custom projection functions.
- Interaction Model: Event-driven (click, hover, drag), declarative filters, and spatial queries (point-in-polygon, nearest-neighbor).
- Styling: Declarative style rules with data-driven properties (size by attribute, color ramps, opacity transitions).
- Plugins: Authentication, analytics, custom renderers, and spatial analysis modules.
API Overview
This section outlines common API patterns. (Note: API signatures below are illustrative.)
-
Initialization
const map = new EarthPlot.Map({ container: 'map', projection: 'web-mercator', center: [0, 0], zoom: 2, }); -
Adding a layer
map.addLayer(new EarthPlot.Layer.Vector({ id: 'cities', source: 'data/cities.geojson', style: { symbol: 'circle', size: d => Math.sqrt(d.properties.population) / 100, color: 'rgba(0,123,255,0.8)' } })); -
Tile layer
map.addLayer(new EarthPlot.Layer.Tile({ id: 'satellite', url: 'https://tiles.example.com/{z}/{x}/{y}.png' })); -
Event handling
map.on('click', 'cities', (evt) => { const feature = evt.feature; showPopup(feature.properties); }); -
Data update (streaming)
const source = map.getSource('vehicle-stream'); source.on('data', batch => map.getLayer('vehicles').setData(batch));
JavaScript Example: Interactive Dashboard
Below is a full example demonstrating an interactive dashboard that loads GeoJSON, adds a heatmap and clustered points, and responds to user interactions.
<!doctype html> <html> <head> <meta charset="utf-8" /> <title>EarthPlot Demo</title> <style> body, html, #map { height: 100%; margin: 0; padding: 0; } .sidebar { position: absolute; left: 10px; top: 10px; z-index: 10; background: rgba(255,255,255,0.9); padding:10px; border-radius:6px; } </style> <script src="https://cdn.example.com/earthplot/earthplot.min.js"></script> </head> <body> <div id="map"></div> <div class="sidebar"> <button id="toggleHeat">Toggle Heatmap</button> </div> <script> const map = new EarthPlot.Map({ container: 'map', projection: 'web-mercator', center: [-98.35, 39.50], zoom: 4 }); map.addLayer(new EarthPlot.Layer.Tile({ id: 'basemap', url: 'https://tiles.example.com/{z}/{x}/{y}.png' })); map.addSource('cities', { type: 'geojson', data: '/data/us_cities.geojson' }); map.addLayer(new EarthPlot.Layer.Cluster({ id: 'city-clusters', source: 'cities', radius: 40, style: { color: d => d.properties.cluster ? 'rgba(255,87,34,0.9)' : 'rgba(0,123,255,0.9)', size: d => d.properties.point_count ? 10 + Math.log(d.properties.point_count) * 4 : 6 } })); const heat = new EarthPlot.Layer.Heatmap({ id: 'city-heat', source: 'cities', style: { intensity: 2.0, radius: 30, colorRamp: ['rgba(0,0,255,0)', 'rgba(0,255,0,0.6)', 'rgba(255,0,0,0.8)'] } }); map.addLayer(heat); document.getElementById('toggleHeat').addEventListener('click', () => { map.toggleLayerVisibility('city-heat'); }); map.on('click', 'city-clusters', (evt) => { const props = evt.feature.properties; alert(`Cluster count: ${props.point_count || 1}`); }); </script> </body> </html> Python Example: Server-Side Generation & Tiling
EarthPlot can be paired server-side to pre-process data and serve vector tiles. Below is a Flask example generating simple vector tiles from GeoJSON.
from flask import Flask, send_file, request import mercantile import json from mapbox_vector_tile import encode app = Flask(__name__) with open('data/world_cities.geojson') as f: cities = json.load(f) def features_in_tile(tile): # Simple bbox filter for demo purposes bbox = mercantile.bounds(tile.x, tile.y, tile.z) minx, miny, maxx, maxy = bbox.west, bbox.south, bbox.east, bbox.north feats = [] for feat in cities['features']: lon, lat = feat['geometry']['coordinates'] if minx <= lon <= maxx and miny <= lat <= maxy: feats.append(feat) return feats @app.route('/tiles/<z>/<x>/<y>.pbf') def tile(z, x, y): tile = mercantile.Tile(x=int(x), y=int(y), z=int(z)) feats = features_in_tile(tile) layer = {'cities': feats} tile_data = encode(layer) return send_file( io.BytesIO(tile_data), mimetype='application/x-protobuf' ) if __name__ == '__main__': app.run(debug=True) Performance Considerations
- Use vector tiles or server-side tiling for large datasets instead of raw GeoJSON.
- Simplify geometries at different zoom levels (TopoJSON, mapshaper).
- Use WebGL layers for millions of points; reduce overdraw with clustering/aggregation.
- Lazy-load heavy data and use pagination or viewport-limited queries.
- Batch styling changes and avoid frequent layer re-creation.
Integration Tips
- Combine EarthPlot with mapping frameworks (Leaflet, MapLibre) if you need extensive UI controls.
- Use Web Workers for heavy preprocessing (spatial joins, indexing).
- Cache tiles and precompute aggregations for common queries.
- Secure tile endpoints with short-lived tokens if serving private data.
Best Practices
- Keep layer responsibilities narrow: one layer = one visual representation.
- Favor declarative styles to make theme changes predictable.
- Expose layer and source IDs for debugging and analytics.
- Monitor memory and GPU usage; provide graceful fallbacks for low-end devices.
- Write unit tests for spatial functions (projection, snapping, filtering).
Common Patterns & Anti-Patterns
- Pattern: Precompute heatmap intensity per tile on the server to reduce client CPU load.
- Anti-pattern: Loading an entire country’s high-precision GeoJSON into the client—use tiles or simplified geometries instead.
- Pattern: Use clustering and decluttering for dense point datasets to keep interactions snappy.
- Anti-pattern: Too many overlapping WebGL layers with expensive blending modes—combine where possible.
Example Projects & Use Cases
- Real-time fleet tracking with WebSocket streams feeding an EarthPlot vector layer.
- Environmental monitoring dashboards combining satellite raster tiles and sensor point layers.
- Urban planning tools showing 3D building footprints with extruded polygons and time-based animations.
- Education apps visualizing historical migrations, on top of an orthographic globe projection.
Troubleshooting
- Blank map: check WebGL availability, tile URL errors, CORS headers.
- Slow rendering: profile with browser devtools, reduce feature count or switch to aggregated layers.
- Incorrect coordinates: verify CRS and projection conversions; ensure GeoJSON is in EPSG:4326 unless otherwise documented.
Conclusion
EarthPlot offers a flexible, high-performance platform for geospatial visualization. For developers, the keys are: design layered, data-driven architectures; serve appropriately tiled/simplified data; and use GPU acceleration with sensible fallbacks. Applying these APIs, examples, and best practices will help you build responsive, scalable geospatial applications.
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