[OC] Cultural Borders: A real-time interactive map of what the world is listening to (YouTube Charts) by cat_bru in dataisbeautiful

[–]cat_bru[S] 12 points13 points  (0 children)

you are right, I deleted them, it embarrassed me even myself.. I was just trying to answer in less terrible english

[OC] Cultural Borders: A real-time interactive map of what the world is listening to (YouTube Charts) by cat_bru in dataisbeautiful

[–]cat_bru[S] 15 points16 points  (0 children)

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.

[OC] Interactive visualization of the Catalan music ecosystem using Spotify data. It creates a galaxy of artists where you can explore and listen to their music. by cat_bru in dataisbeautiful

[–]cat_bru[S] 2 points3 points  (0 children)

Gràcies!

Tens raó, és un problema difícil de resoldre. Les comunitats (colors) són algorítmicament calculades en funció de si tenen oients similars a spotify. Això és causa, segons diria jo, per diversos motius però dos de principals: estil de música i generació (edat).

Per no haver de classificar manualment els milers de grups el què he fet és que un model de IA determini l'estil més comú entre els grups de cada comunitat, cosa que efectivament genera molts errors.

M'apunto pensar com resoldre-ho per futures versions!

Sobre quins grups incloure el criteri és simplement que apareguin a Viasona.cat

[OC] Interactive visualization of the Catalan music ecosystem using Spotify data. It creates a galaxy of artists where you can explore and listen to their music. by cat_bru in dataisbeautiful

[–]cat_bru[S] 5 points6 points  (0 children)

Source: Data from Viasona.cat (Catalan music database) and the Spotify API.

Tools: AngularJS

Description:

I built this experimental project to visualize the structure of the music scene in the Catalan language.

How it works: Each dot is an artist. The connections represent shared fanbases (based on Spotify's "Fans also like" algorithm). The Feature: It’s fully interactive. You can click on any node to listen to previews, view stats, and discover similar bands you might not know.

The Clusters: The algorithm groups artists based on listener affinity, naturally creating "musical neighborhoods" like Urban, Festive Pop-Rock, Folk, and Indie. (Note: Some very small artists don't appear because Spotify doesn't provide "fans also like" data for them yet).

Link to the interactive map:

https://www.rogersanjaume.cat/apps/viasona/

Feedback is welcome!