Understanding urban infrastructure through network analysis reveals fascinating patterns about how cities function. For my final project in a data visualization course, I examined Budapest's street network using OSMnx, a Python library that extracts data from OpenStreetMap.
Street Network Mapping
I mapped 4 square kilometers of central Budapest, representing streets as edges and intersections as nodes, with edge width indicating street type. This visualization helps us understand the basic structure of the city's transportation network.
Attack Tolerance Analysis
To understand which parts of the network are most critical, I calculated the betweenness centrality of all the street intersections and removed the one with the highest betweenness one by one. This simulates what happens when critical infrastructure fails or becomes unavailable.
Key Finding
The analysis revealed that removing just under 2% of nodes created over 3 disconnected components, identifying Budapest's bridges (Margaret, Chain, and Elisabeth) as critical infrastructure connecting Buda and Pest across the Danube. This makes intuitive sense—the Danube River naturally divides the city, and the bridges are essential connectors.
This type of analysis can help urban planners understand which infrastructure is most critical and prioritize maintenance or redundancy in those areas.
This post was originally published on the Department of Network and Data Science Blog.