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Comparing Transportation Hub Built Environment and Social Variables as Auxiliary Data in Street Crime Prediction, a case study in San Jose, USA
Accurate crime prediction is crucial for allocating police resources to reduce and prevent crime, particularly in large cities. Previous research has explored the relationship between social and built environments and street crime, but few studies have combined multi-source data using geo-statistical modeling. In this research, we applied a Spatial-Temporal Cokriging algorithm to predict street crime risk in San Jose, California, by integrating historical street crime data with sociological and built environmental variables. We used time-series data from 2,479 police call records in 2019, including assaults and robberies, as the primary variable. Secondary variables included transit density and walkability data from the Environmental Protection Agency (EPA)'s National Walkability Index. We analyzed crime risks across quad-week periods, predicting separately for weekdays and weekends. Our multi-variable crime prediction model compares outcomes using historical data, socio-economic data, and environmental data. The results reveal distinct crime patterns for weekdays and weekends, aiding in crime prediction for rapidly growing metropolitan areas. The study found that the spatial-temporal geostatistical prediction model performed better for weekday crimes, achieving over 70% accuracy, compared to around 60% for weekend crimes. Including transportation density and socio-economic variables significantly improved prediction accuracy, with socioeconomic variables outperforming environmental ones. Interestingly, the impact of these secondary variables varied by day of the week. The study underscores the importance of incorporating auxiliary data to enhance crime prediction and offers a nuanced understanding of the interplay between socio-economic and environmental factors in crime risk.
