Visualizing research data: Challenges of combining different datasources
summary
A concrete pipeline story: Python scripts ingest Excel, CSV and JSON APIs, read ESRI shapefiles with pyshp, normalize ISO codes and encodings, simplify geometry with mapshaper.org, emit GeoJSON and TopoJSON and precompute aggregates into Redis or static JSON, serve a minimal API or a single-call GraphQL (via Graphene Python) to power interactive choropleth maps. A World Bank open data beta built this way is live as a working proof, showing the approach scales and actually simplifies front-end work.
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chapters (10)
- 0:00Research data visualization intro
- 5:00Combining different data sources
- 10:07Data validation and encoding
- 14:07Transforming geospatial data
- 18:21Simplifying API design
- 21:35JavaScript frameworks for UIs
- 25:59Interactive visualization tips
- 28:51System architecture and scalability
- 30:17Implementation tools and resources
- 32:02Q&A
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