Agent skill · data analytics · erichowens
large-scale-map-visualization
Master of high-performance web map implementations handling 5,000-100,000+ geographic data points. Specializes in Leaflet.js optimization, Supercluster algorithms, viewport-based loading, canvas
Why this skill is useful
Provides specific optimization techniques and implementation patterns for handling large-scale geospatial data visualizations that the AI wouldn't reliably generate on its own.
What it needs
About 8k tokens when loaded. Last updated 2026-07-14. 180 stars on the source repository.
What this skill does
Large-Scale Map Visualization Expert Master of high-performance web map implementations handling 5,000-100,000+ geographic data points. Specializes in Leaflet.js optimization, spatial clustering algorithms, viewport-based loading, and progressive disclosure UX patterns for map-based applications. Activation Triggers Activate on: "map performance", "too many markers", "slow map", "clustering", "10k points", "marker clustering", "leaflet performance", "spatial visualization", "geospatial clustering", "viewport loading", "map data optimization", "real-time map", "Supercluster", "marker cluster" NOT for: Static map images (use Mapbox/Google Static) 3D visualizations (use Maplibre GL) Non-geographic data visualization (use D3.js/Chart.js) Simple maps with <100 markers (vanilla Leaflet is fine) Core Expertise Performance Architecture Technology Stack Decisions Use Case Best Library Why ---------- -------------- ----- React + <5k points react-leaflet-cluster Simple drop-in, wraps Leaflet.markercluster React + 5-50k points use-supercluster hook 3-5x faster, viewport-aware, GeoJSON native React + 50k+ points supercluster + Web Workers Offload clustering to background thread Static sites Server-side clustering Pre-compute at build time Real-time updates Canvas renderer + sampling Minimize DOM manipulation Key Techniques 1. Marker Clustering with Supercluster Why Supercluster beats alternatives: Performance: Handles 500k points in 1-2 seconds vs 8+ seconds for Leaflet.markercluster Architecture: Index-based k-d tree clustering, can run server-side or in Workers API: Simple GeoJSON input/output Viewport-aware: Only clusters visible points Implementation Pattern: 2. Viewport-Based Loading (Supabase + PostGIS) Database Function: React Query Hook: 3. Progressive Disclosure Strategy Show appropriate detail levels based on zoom: 4. Canvas Rendering for Performance Performance gain: 3-5x faster rendering with 1,000+ markers 5. Efficient Cluster Icons 6. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills erichowens/large-scale-map-visualization