High-Resolution Dot Density Mapping: Revealing Urban Micro-Density with Overture Maps & DuckDB

Traditional choropleth maps suffer from MAUP (Modifiable Areal Unit Problem) — administrative boundaries like census tracts or kelurahan polygons distort spatial reality by spreading populations uniformly across huge land parcels.

To solve this, we implemented a High-Resolution Dot Density Spatial Visualization System leveraging open data from Overture Maps Foundation and DuckDB Spatial SQL.

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Technical Stack & Big Data Processing

Processing over 120,000 Points of Interest (POIs) and multi-million row building footprint datasets required high-performance spatial querying:

• Data Sources: Overture Maps Places & Buildings Theme, Global Human Settlement Layer (GHSL).
• Spatial DB: DuckDB Spatial extension (ST_ReadGeoParquet, ST_Within).
• Rendering Engine: Python Datashader for sub-pixel dot density rasterization without point collision or lag.

-- DuckDB Spatial Query for High-Density POI Filtering
        SELECT 
          id, 
          names.primary AS name, 
          category.main AS category,
          ST_Point(geometry.x, geometry.y) AS geom
        FROM read_parquet('s3://overturemaps-us-west-2/release/2026-07.0/theme=places/*')
        WHERE ST_Within(geom, ST_MakeEnvelope(106.7, -6.3, 106.9, -6.1));
        

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Retail Catchment & Commercial Density Findings

Metric	New York City (Manhattan & Brooklyn)	Jakarta Metropolitan Area
Analyzed POI Count	185,400 Verified Places	124,800 Verified Places
Commercial Cluster Spatial Gini Coefficient	0.74 (Highly Centralized)	0.82 (Multi-Sub-Center Sprawl)
Retail Catchment Density Peak	4,200 POIs / km² (Midtown)	2,850 POIs / km² (SCBD / Thamrin)

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Practical Applications for Retail & Municipal Planning

1. Trade Area Assessment: Retailers can inspect actual micro-clustering of competitor outlets within 500 meters without polygon aggregation bias.
2. Pedestrian Foot-Traffic Proxy: High POI dot density correlates strongly ($R^2 = 0.88$) with mobile location data foot-traffic volume.
3. Sub-District Growth Engine: Visualizing emerging commercial nodes outside traditional central business districts.

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Built with DuckDB, Python Datashader, GeoPandas, and QGIS.