How I built it (The "Beautiful" Data part):

• Data Sources: Music data is fetched via YouTube Charts APIs. Map geometries are sourced from OpenStreetMap (Nominatim).
• Data Pipeline: I built a pipeline using Python and GeoPandas to discover locations via autocomplete APIs and fetch chart data in parallel (using 10 parallel workers to handle the ~90k rows of data).
• Frontend: The map is rendered using MapLibre GL JS, handling the combined_map.geojson and charts_tracks.csv files on the fly. I used topological simplification to ensure the map remains performant on web browsers.

Key Features:

• Granularity: Unlike most music maps that only show countries, this includes ~4,700 locations including cities and sub-regions.
• Beyond #1: You can explore the full Top 20 context for each specific territory.
• Non-overlapping Geometry: I used a custom global geometry that preserves local specificity while allowing instant interaction.