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Under the umbrella of the <jats:italic>crime opportunity theory<\/jats:italic>, that suggests that human mobility can play a role in crime generation, increasing attention has been paid to the predictive power of human mobility in place-based short-term crime models. Researchers have used call detail records (CDR), data from location-based services such as Foursquare or from social media to characterize human mobility; and have shown that mobility metrics, together with historical crime data, can improve short-term crime prediction accuracy. In this paper, we propose to use a publicly available fine-grained human mobility dataset from a location intelligence company to explore the effects of human mobility features on short-term crime prediction. For that purpose, we conduct a comprehensive evaluation across multiple cities with diverse demographic characteristics, different types of crimes and various deep learning models; and we show that adding human mobility flow features to historical crimes can improve the F1 scores for a variety of neural crime prediction models across cities and types of crimes, with improvements ranging from 2% to 7%. Our analysis also shows that some neural architectures can slightly improve the crime prediction performance when compared to non-neural regression models by at most 2%.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-022-00366-2","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T06:02:37Z","timestamp":1667887357000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Enhancing short-term crime prediction with human mobility flows and deep learning architectures"],"prefix":"10.1140","volume":"11","author":[{"given":"Jiahui","family":"Wu","sequence":"first","affiliation":[]},{"given":"Saad Mohammad","family":"Abrar","sequence":"additional","affiliation":[]},{"given":"Naman","family":"Awasthi","sequence":"additional","affiliation":[]},{"given":"Enrique","family":"Frias-Martinez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5114-7633","authenticated-orcid":false,"given":"Vanessa","family":"Frias-Martinez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,8]]},"reference":[{"key":"366_CR1","unstructured":"[n.d.]. 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