mapbox-data-visualization-patterns

mapbox-data-visualization-patterns is a comprehensive pattern library for visualizing data on Mapbox GL maps. It solves the problem of choosing and correctly implementing the right map visualization for a given dataset — choropleth maps, heat maps, point clustering, 3D extrusions, data-driven styling, and animated time-series — by providing ready-to-adapt JavaScript patterns using Mapbox GL layer types and expressions.

The skill's main document covers choropleth maps with data-driven `interpolate`, `step`, and `match` color expressions and reusable hover popups, plus heat maps for point density, and explains when to prefer smooth interpolation versus discrete buckets. Reference files go deeper on clustering (with a decision table for clustering versus heatmaps and custom cluster properties), 3D extrusions (including the caveat that the classic example works only with classic styles and an anti-pattern warning against constant extrusion heights), circles and lines, legends and use cases, animation, and performance optimization for data-heavy applications. Throughout, it emphasizes data-driven styling from feature properties rather than hard-coded values, and it ships an evals file for testing.

Target users are web developers and data practitioners building interactive maps with Mapbox GL JS. Typical use cases include visualizing statistical data by region, showing incident or event density, extruding building or population data in 3D, animating time-series, and optimizing rendering for large datasets. All examples are client-side Mapbox GL styling code that reads GeoJSON sources; the skill contains no destructive operations, credential handling, or remote code execution.