FAIR URBAN CRIME PREDICTION WITH HUMAN MOBILITY BIG DATA
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Crime imposes significant costs on society. Reported crime data is important in quantifying the severity of crimes, based on which decision-makers would allocate resources for crime interventions. Human mobility big data has triggered the interest in various fields to study the relationship between urban crimes and mobility at a large scale, especially the predictive power of mobility for urban crimes. This research direction can enrich our understanding of crimes and better inform crime-related decision-making. One concern about reported crime data is the bias issue. The bias could be produced by different levels of residents’ willingness to report potential crime incidents and police activity in neighborhoods. While lots of studies about crime prediction are aware of biases in reported crimes, few of them propose solutions to address or mitigate this issue or to evaluate how this issue would affect prediction models in terms of accuracy or fairness. My dissertation research aims to explore the potential of human mobility big data for crime prediction. Specifically, my dissertation will advance the state-of-the-art by addressing three challenges in mobility-based crime prediction: 1) Constructing mobility features might be sensitive to different methodological choices. Without careful examination of these choices, there might be conflicting findings. One critical area of mobility analysis to predict crime is the identification of urban hotspots. Therefore, my work performs a systematic spatial sensitivity analysis on the impact of these choices and provides guidelines to identify the most stable ones. 2) Under-reporting generates biases in reported crime data. To address such bias, I develop a Bayesian model for long-term crime prediction that infers the unobserved true number of crime incidents. Comprehensive experiments show how the accuracy and fairness of long-term crime prediction would be affected by modeling the under-reporting of crimes. 3) Although empirical studies show promising results about the relationship between human mobility and long-term crime prediction, the effects of mobility features on short-term crime prediction have yet to be explored. Therefore, my work conducts a series of experiments to explore how incorporating mobility features into short-term crime prediction models affects their performance in terms of accuracy and fairness.