When you’re carving neighborhood‑scale territories, you need data that follows settlement patterns closely.
Overview
WorldPop is a global population mapping initiative that produces high-resolution, gridded estimates. The constrained top-down approach disaggregates census totals to ~100 m grids but only within cells mapped as settlements or buildings. This improves alignment with where people actually live.
Methodology & Spatial Characteristics
Settlement mask: Allocation restricted to cells with mapped buildings/built areas.
Machine learning: Random Forest models use census + covariates (satellite, land cover, infrastructure).
Resolution: ~3 arc-seconds (~100 m).
Normalization: Totals aligned to census aggregates.
Strengths & Use Cases
Settlement alignment: Maps residential distribution accurately.
Higher resolution: Finer than LandScan’s ~1 km.
Reduced misallocation: Prevents population in empty zones.
Limitations & Caveats
Settlement detection: Missing or misclassified dwellings lead to underestimates.
Input census quality: Errors propagate.
Comparability: Diverges from ambient-use datasets like LandScan.
Related Resources
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