This proves that, next to the residential population of an area, the ambient population there is strongly predictive of the area's crime levels. The proposed models achieve absolute R^2 metrics of up to 65% (on a geographical out-of-sample test set) and up to 89% (on a temporal out-of-sample test set). Our study shows that spatial and spatio-temporal features derived from Foursquare venues and checkins, subway rides, and taxi rides, improve the baseline models relying on census and POI data. We employ averaging and boosting ensemble techniques from machine learning, to investigate their power in predicting yearly counts for different types of crimes occurring in New York City at census tract level. In this paper, we leverage large human mobility data to craft an extensive set of features for crime prediction, as informed by theories in criminology and urban studies. With the rise of ubiquitous computing, there is the opportunity to improve such models with data that make for better proxies of human presence in cities. Traditional crime prediction models based on census data are limited, as they fail to capture the complexity and dynamics of human activity.
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