We show that revolutions in the production and availability of individual location data at a global scale can improve our understanding in all of these areas. In particular we show that, at the city level, mobility metrics are more correlated with transportation utilization, pollution, and public health than traditional measures such as population density and urban sprawl. At the neighborhood level we demonstrate that mobility provides insights into how the particular dynamics of different cities alter the movement patterns of different socioeconomic and demographic groups. The ways in which these patterns differ between cities and over time may well provide valuable insights in urban planning. Meanwhile at the global scale we show that mobility patterns can be used to infer nighttime satellite imagery – a gold standard for uniform global analysis. Such satellite data has been used in applications from poverty mapping to climate change. We show not only that high fidelity artificial imagery can be inferred from mobility patterns, but that this imagery is often a more effective estimator of GDP than the original data. In all of the above applications we demonstrate the utmost regard for the preservation of individual privacy.
Advisor: Prof. Henry Kautz (Computer Science)
Committee: Prof. Jiebo Luo (Computer Science), Prof. Ehsan Hoque (Computer Science), and Prof. Gourab Ghoshal (Physics)