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The interdisciplinary research aims to explore the fundamental characters and underlying mechanisms of urban neighborhoods. I am using big data of urban dwellers’ movements collected from geo-social networking platforms as an indicator to understand social inequality in Boston areas and large population centers across the U.S.
Figure 1. Radii of Activity Spaces.
I used machine learning to identify each Twitter user's home location and then calculated their radii.
Figure 2. Urban Radii of Activity Spaces.
The log-normal distribution governs urban mobility in the top 50 cities in the U.S.
Figure 3. Geo-social Networks of Urban Neighborhoods. Each node is a block group.
The size of a node represents the number of visitors, and the thickness of an edge represents the number of travelers between two nodes.
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