Lecture by Jamie Saxon, Center for Data and Computing at the Department of Computer Science & Center for Spatial Data Science, University of Chicago
A large literature establishes the role of mobility in the maintenance of neighborhood social structures. Jane Jacobs famously argued that social capital is maintained through “cross-use of space,” and James Coleman formalized its dependence on the “closure” of human interactions. Since many of these interactions entail human movement, neighborhoods with higher social capital should be distinguishable by more cohesive mobility networks.
Having observed the mobility of Chicago residents through a large dataset of smartphone users, this research constructs a neighborhood-level mobility network for the city and characterize neighborhoods according to their local graph structure. Neighborhoods that are well integrated with their surroundings have higher income and educational attainment. Consistent with social capital theory and routine activity theory in criminology, higher local network integration independently predicts lower levels of violent and property crime.
The methodologies presented provide a meaningful, replicable, and inexpensive approach to the structural measurement of neighborhood networks and social structure. However, additional work is required “groundtruth” and extend large data-streams.
Dr. Saxon is a postdoctoral fellow of the Center for Data and Computing, of the University of Chicago, where he develops data pipelines to evaluate the availability and accessibility of resources in neighborhoods of American cities. Formerly, he was a postdoctoral fellowwith the Harris School of Public Policy and the Center for Spatial Data Science, Chicago. Originally trained as an experimental physicist, he has expertise in instrumentation, sensors, data acquisition systems, and big data methods. He was closely involved in the discovery of the Higgs Boson.
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