Note: This post was originally published by Luis Natera on his personal blog. It has been republished here as part of TYN Studio's content.
I found this data visualization by Shawn Allen and thought about doing something similar as an exercise for data manipulation and plotting. The goal was to map Manhattan's nightlife venues—bars, pubs, and nightclubs—using the city's walking network as a backdrop.
Technical Approach
The project utilized several Python libraries:
- OSMnx for downloading points of interest and the walking network
- GeoPandas for geospatial data handling
- Matplotlib for visualization
Implementation
I created two primary Python functions:
load_data()
Downloads POI data for specified amenities (bar, pub, nightclub), projects coordinates, and filters locations within Manhattan boundaries using spatial joins. This ensures we're only looking at venues actually in Manhattan, not in neighboring boroughs.
def load_data():
# Download POI data from OpenStreetMap
amenities = ['bar', 'pub', 'nightclub']
pois = ox.geometries_from_place(place, tags={'amenity': amenities})
# Project and filter to Manhattan boundaries
pois = pois.to_crs(epsg=2263)
manhattan = ox.geocode_to_gdf('Manhattan, New York')
pois = gpd.sjoin(pois, manhattan, predicate='within')
return pois
make_plot()
Creates visualization using the Manhattan walking network as background, overlaying scatter plots in cyan, yellow, and magenta colors representing different nightlife venue types. The color scheme references the CMYK color model for a visual layering effect.
When different venue types overlap spatially, the colors mix—creating visual hotspots that show where nightlife is most concentrated.
Result
The visualization reveals the concentration of nightlife in certain Manhattan neighborhoods. You can clearly see the density in areas like the Lower East Side, Greenwich Village, and Midtown—places known for their vibrant nightlife scenes.
The walking network provides context, showing how these venues connect through the city's pedestrian infrastructure. After all, much of Manhattan's nightlife is experienced on foot, moving from one venue to another through the city streets.