Predicting crime with routine activity patterns inferred from social media

Prior work in statistical crime prediction has not investigated micro-level movement patterns of individuals in the area of interest. Geotagged social media implicitly describe these patterns for many individuals; however, methods of extracting such patterns and integrating them into a statistical model remain undeveloped. This paper presents methods and experiments that begin to fill this gap. We investigate the use of spatiotemporally tagged Twitter posts for inferring micro-level movement patterns, and we use real crime data to develop and test a model informed by such patterns. Our results indicate improved performance for 15 of the 20 crime types studied, when comparing our model with a baseline that does not use micro-level movement patterns.