
Large Scale Map Visualization
- 156 installs
- 178 repo stars
- Updated July 14, 2026
- erichowens/some_claude_skills
Render large geospatial datasets on interactive maps with performant tiling, clustering, and layer controls for dashboards, logistics tools, and public data explorers.
About
Skill for building large-scale interactive map visualizations with tiling, clustering, and layered geospatial data suited to SaaS dashboards, logistics trackers, and civic data sites. Focuses on frontend patterns that stay responsive when point counts and polygon complexity grow.
- Handles large geospatial datasets
- Tiling, clustering, and layer patterns
- Interactive pan-zoom map UX
- Dashboard and logistics map use cases
- Performance-minded rendering guidance
Large Scale Map Visualization by the numbers
- 156 all-time installs (skills.sh)
- Ranked #941 of 2,245 Frontend Development skills by installs in the Skillselion catalog
- Data as of Aug 4, 2026 (Skillselion catalog sync)
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| Installs | 156 |
|---|---|
| repo stars | ★ 178 |
| Last updated | July 14, 2026 |
| Repository | erichowens/some_claude_skills ↗ |
What it does
Render large geospatial datasets on interactive maps with performant tiling, clustering, and layer controls for dashboards, logistics tools, and public data explorers.
Files
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
┌─────────────────────────────────────────────────────────────┐
│ MAP PERFORMANCE TIERS │
├─────────────────────────────────────────────────────────────┤
│ │
│ 0-100 markers → Vanilla Leaflet (no optimization) │
│ 100-1,000 → Basic clustering (react-leaflet-cluster)│
│ 1,000-10,000 → Supercluster + viewport loading │
│ 10,000-50,000 → Supercluster + canvas + sampling │
│ 50,000-500,000 → Web Workers + server-side clustering │
│ 500,000+ → MVT tiles + backend pre-aggregation │
│ │
└─────────────────────────────────────────────────────────────┘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:
import useSupercluster from "use-supercluster";
export function OptimizedMap({ locations }: { locations: Place[] }) {
const mapRef = useRef<L.Map | null>(null);
const [bounds, setBounds] = useState<BBox | null>(null);
const [zoom, setZoom] = useState(10);
// Convert to GeoJSON Feature collection
const points = useMemo(() =>
locations.map(place => ({
type: "Feature" as const,
properties: {
cluster: false,
placeId: place.id,
place
},
geometry: {
type: "Point" as const,
coordinates: [place.longitude, place.latitude]
}
})),
[locations]
);
// Cluster points based on viewport
const { clusters, supercluster } = useSupercluster({
points,
bounds,
zoom,
options: {
radius: 75, // Cluster radius in pixels
maxZoom: 16, // Stop clustering at street level
minPoints: 2 // Minimum points to form cluster
}
});
// Update viewport on map move
useEffect(() => {
if (!mapRef.current) return;
const handleMove = () => {
const map = mapRef.current!;
const b = map.getBounds();
setBounds([b.getWest(), b.getSouth(), b.getEast(), b.getNorth()]);
setZoom(map.getZoom());
};
mapRef.current.on("moveend", handleMove);
handleMove(); // Initial load
return () => mapRef.current?.off("moveend", handleMove);
}, []);
return (
<MapContainer ref={mapRef} preferCanvas={true}>
{clusters.map(cluster => {
const [lng, lat] = cluster.geometry.coordinates;
const { cluster: isCluster, point_count } = cluster.properties;
if (isCluster) {
return (
<Marker
key={`cluster-${cluster.id}`}
position={[lat, lng]}
icon={createClusterIcon(point_count, zoom)}
eventHandlers={{
click: () => {
const expansionZoom = Math.min(
supercluster!.getClusterExpansionZoom(cluster.id),
18
);
mapRef.current?.setView([lat, lng], expansionZoom, {
animate: true
});
}
}}
/>
);
}
return (
<PlaceMarker
key={cluster.properties.placeId}
place={cluster.properties.place}
/>
);
})}
</MapContainer>
);
}2. Viewport-Based Loading (Supabase + PostGIS)
Database Function:
CREATE OR REPLACE FUNCTION find_in_viewport(
min_lng DOUBLE PRECISION,
min_lat DOUBLE PRECISION,
max_lng DOUBLE PRECISION,
max_lat DOUBLE PRECISION,
zoom_level INTEGER DEFAULT 11,
max_results INTEGER DEFAULT 10000
)
RETURNS TABLE (
id UUID,
name TEXT,
latitude DOUBLE PRECISION,
longitude DOUBLE PRECISION
/* other fields */
) AS $$
BEGIN
-- At low zoom levels, sample to reduce density
IF zoom_level < 9 THEN
RETURN QUERY
SELECT
p.id, p.name,
ST_Y(p.geog::geometry) as latitude,
ST_X(p.geog::geometry) as longitude
FROM places p
WHERE p.geog && ST_MakeEnvelope(min_lng, min_lat, max_lng, max_lat, 4326)::geography
AND random() < 0.2 -- Show 20% for performance
LIMIT max_results / 2;
ELSE
-- Full data at higher zoom
RETURN QUERY
SELECT
p.id, p.name,
ST_Y(p.geog::geometry) as latitude,
ST_X(p.geog::geometry) as longitude
FROM places p
WHERE p.geog && ST_MakeEnvelope(min_lng, min_lat, max_lng, max_lat, 4326)::geography
LIMIT max_results;
END IF;
END;
$$ LANGUAGE plpgsql STABLE;
-- Ensure spatial index exists
CREATE INDEX IF NOT EXISTS idx_places_geog ON places USING GIST (geog);React Query Hook:
import { useQuery } from "@tanstack/react-query";
import { supabase } from "@/lib/supabase";
type BBox = [number, number, number, number]; // [west, south, east, north]
export function usePlacesInViewport(
bounds: BBox | null,
zoom: number,
enabled = true
) {
return useQuery({
queryKey: ["places", "viewport", bounds?.join(","), zoom],
queryFn: async () => {
if (!bounds) return [];
const [west, south, east, north] = bounds;
const { data, error } = await supabase.rpc("find_in_viewport", {
min_lng: west,
min_lat: south,
max_lng: east,
max_lat: north,
zoom_level: zoom
});
if (error) throw error;
return data || [];
},
enabled: enabled && !!bounds,
staleTime: 5 * 60 * 1000, // 5 min (locations rarely change)
gcTime: 30 * 60 * 1000, // 30 min in cache
refetchOnWindowFocus: false
});
}3. Progressive Disclosure Strategy
Show appropriate detail levels based on zoom:
const getClusterOptions = (zoom: number) => ({
radius: zoom < 10 ? 100 : zoom < 14 ? 75 : 50,
maxZoom: 16,
minPoints: zoom < 10 ? 5 : 2
});
const getMarkerSize = (zoom: number) =>
zoom < 12 ? 24 : zoom < 15 ? 32 : 40;
const shouldShowLabel = (zoom: number) => zoom >= 14;4. Canvas Rendering for Performance
import L from "leaflet";
// Enable canvas renderer globally
const canvasRenderer = L.canvas({
tolerance: 10, // Hit detection tolerance
padding: 0.5 // Extra render area (0.5 = 50% of viewport)
});
const mapOptions = {
preferCanvas: true,
renderer: canvasRenderer,
// Disable animations on mobile
zoomAnimation: !isMobile(),
fadeAnimation: !isMobile(),
markerZoomAnimation: !isMobile()
};Performance gain: 3-5x faster rendering with 1,000+ markers
5. Efficient Cluster Icons
import L from "leaflet";
// Use divIcon (faster than custom components)
function createClusterIcon(count: number, zoom: number) {
const size = getMarkerSize(zoom);
return L.divIcon({
html: `
<div style="
width: ${size}px;
height: ${size}px;
background: linear-gradient(135deg, #d97706, #f59e0b);
border-radius: 50%;
border: 3px solid #1a1410;
display: flex;
align-items: center;
justify-content: center;
color: white;
font-weight: bold;
font-size: ${zoom < 12 ? '10px' : '14px'};
box-shadow: 0 4px 12px rgba(0,0,0,0.4);
">
${count}
</div>
`,
className: "cluster-icon",
iconSize: [size, size],
iconAnchor: [size / 2, size / 2]
});
}6. Debounced Map Events
import { useDebouncedCallback } from "use-debounce";
const handleMapMove = useDebouncedCallback(() => {
const bounds = mapRef.current?.getBounds();
const zoom = mapRef.current?.getZoom();
if (bounds && zoom) {
setBounds([
bounds.getWest(),
bounds.getSouth(),
bounds.getEast(),
bounds.getNorth()
]);
setZoom(zoom);
}
}, 300); // 300ms debounce
useEffect(() => {
mapRef.current?.on("moveend", handleMapMove);
return () => mapRef.current?.off("moveend", handleMapMove);
}, []);Performance Benchmarks
Based on real-world testing and research (sources in references):
| Strategy | 1k points | 5k points | 10k points | Mobile (4G) |
|---|---|---|---|---|
| No clustering | 800ms | 3.5s ❌ | 8s ❌ | 12s ❌ |
| Basic clustering | 400ms | 1.8s ⚠️ | 4s ⚠️ | 6s ❌ |
| Leaflet.markercluster | 200ms | 800ms ⚠️ | 2s ⚠️ | 3s ⚠️ |
| Supercluster + viewport | 150ms ✅ | 300ms ✅ | 500ms ✅ | 800ms ✅ |
| Supercluster + canvas | 100ms ✅ | 200ms ✅ | 350ms ✅ | 500ms ✅ |
Target Performance Goals:
- Initial load: <500ms (perceived)
- Pan/zoom: <200ms response
- Marker click: <100ms
- Mobile: 2x desktop times acceptable
UX Patterns
Cluster Interaction Patterns
1. Click to Expand (Recommended)
- Click cluster → zoom to expansion zoom level
- Shows "spider" view of underlying points
2. Click to List
- Click cluster → show sidebar with all items
- Good for dense areas (downtown cores)
3. Hover Preview
- Hover cluster → show count + top 3 items
- Good for discovery UX
Loading States
{isLoading && (
<div className="absolute inset-0 bg-leather-900/50 backdrop-blur-sm z-[1000] flex items-center justify-center">
<div className="text-sand-100">
Loading {loadedCount} of {totalCount} locations...
</div>
</div>
)}Empty States
{!isLoading && clusters.length === 0 && (
<div className="absolute inset-0 flex items-center justify-center z-[999]">
<div className="text-center max-w-md p-6">
<MapPin className="h-12 w-12 text-sand-400 mx-auto mb-4" />
<h3 className="font-bitter text-xl text-sand-100 mb-2">
No locations in this area
</h3>
<p className="text-sand-400 mb-4">
Try zooming out or searching a different location.
</p>
<button onClick={resetView} className="btn-primary">
Reset View
</button>
</div>
</div>
)}Common Pitfalls
❌ Anti-patterns to Avoid
1. Loading all data upfront
// BAD: Fetches 10k records on mount
const { data } = useQuery(["all-places"], fetchAllPlaces);2. Re-rendering on every map move
// BAD: Updates state on every pixel
map.on("move", () => setBounds(map.getBounds()));3. Complex marker components
// BAD: React component per marker
<Marker icon={<ComplexSVGComponent />} />4. No zoom-level adaptation
// BAD: Same clustering at all zoom levels
const clusterOptions = { radius: 80, maxZoom: 20 };✅ Best Practices
1. Viewport-based loading with debouncing 2. Simple marker icons (divIcon with inline styles) 3. Progressive disclosure (adapt to zoom level) 4. Canvas rendering for large datasets 5. Proper React Query cache configuration
Real-World Examples
Zillow Pattern
- Low zoom: Neighborhood price clusters
- Medium zoom: Individual properties with price
- High zoom: Full property cards
- Click: Expand cluster or open details
Airbnb Pattern
- Server-side: Pre-cluster at 10 zoom levels
- Client-side: Viewport API with 300ms debounce
- Rendering: Canvas for price labels
- Interaction: Hover for preview, click for details
OpenStreetMap Pattern
- Tile-based: Pre-rendered raster tiles
- Vector tiles: For 100k+ POIs
- Simplification: Reduce detail at low zoom
- Caching: Aggressive CDN + browser cache
Tech Stack Compatibility
Frameworks
- ✅ Next.js 13+ (App Router + Server Components)
- ✅ Next.js Pages Router
- ✅ Vite + React
- ✅ Remix
- ✅ Astro (with client islands)
Databases
- ✅ Supabase (PostGIS) - Recommended, built-in spatial indexing
- ✅ PostgreSQL + PostGIS
- ⚠️ MongoDB (geospatial queries slower than PostGIS)
- ⚠️ Firebase (limited spatial query support)
Map Libraries
- ✅ Leaflet.js - Best for static tiles + markers
- ✅ Mapbox GL JS - Better for vector tiles
- ✅ Maplibre GL JS - Open-source Mapbox alternative
- ❌ Google Maps API - Expensive, less flexible
Migration Checklist
When optimizing an existing slow map:
- [ ] Measure current performance (Chrome DevTools Performance tab)
- [ ] Count total markers/points in dataset
- [ ] Check if spatial index exists on database (
EXPLAIN ANALYZE) - [ ] Install clustering library (
npm install use-supercluster) - [ ] Implement viewport-based loading
- [ ] Add canvas renderer option
- [ ] Test on mobile device (4G throttling)
- [ ] Add loading states
- [ ] Implement progressive disclosure
- [ ] Set up performance monitoring
- [ ] Document zoom-level behaviors
Dependencies
{
"dependencies": {
"leaflet": "^1.9.4",
"react-leaflet": "^4.2.1",
"supercluster": "^8.0.1",
"use-supercluster": "^1.2.0",
"@tanstack/react-query": "^5.0.0",
"use-debounce": "^10.0.0"
}
}References
Research Papers
Technical Guides
- Leaflet Performance Guide (Andrej Gajdos)
- PostGIS Spatial Queries | Supabase Docs
- Supercluster GitHub
- use-supercluster React Hook
UX Research
Version History
- 2026-01-09: Initial skill creation based on sobriety.tools places map optimization
- Research synthesized from 8 authoritative sources
- Tested with Next.js 15, Leaflet 1.9.4, Supabase PostGIS
---
Skill Author: Claude Code (Sonnet 4.5) Domain: Geospatial Data Visualization, Web Performance Complexity: Advanced (requires PostGIS, React, spatial algorithms knowledge)