Method Overview
Define and operationalize two axes of urban quality:
Efficiency: proximity to amenities, transit coverage, walkability (using OSM, GTFS, Walk Score, etc.)
Serendipity: POI diversity, semantic adjacency, ambiguous use, temporal variance, street network entropy
Use publicly available or purchasable datasets:
OpenStreetMap
SafeGraph / Veraset / Cuebiq
Yelp, Foursquare, Google Places
GTFS feeds and transit APIs
Facebook or Eventbrite event data
Map and score neighborhoods on both axes
Identify spatial typologies and performance quadrants:
High-efficiency / high-serendipity
High-efficiency / low-serendipity
Low-efficiency / high-serendipity
Low-low zones
Analyze across scales: block, neighborhood, district
Interpret role of 1.5 / 2.5 places in enabling rich experience