Identifying Topical Hot Spots in Urban Areas

In this article, I show a quick and easy-to-use methodology that is capable of identifying hot spots for a given interest based on Point of interest (POI) collected from OpenStreeetMap (OSM) using the DBSCAN algorithm of sklearn. First, I will collect the raw data of POIs belonging to a couple of categories that I found on ChatGPT, and I assumed they are characteristic of the sometimes-called hyp-lifestyle (e.g., cafes, bars, marketplaces, yoga studios); after converting that data into a handy GeoDataFrame, I do the geospatial clustering, and finally, evaluate the results based on how well the different urban functionalities mix in each cluster.

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