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dc.contributor.authorMascolo, Ceciliaen
dc.date.accessioned2020-04-20T15:19:30Z
dc.date.available2020-04-20T15:19:30Z
dc.date.issued2020-06-02en
dc.identifier.issn2334-0770
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/304536
dc.description.abstractCrime has been previously explained by social characteristics of the residential population and, as stipulated by crime pat- tern theory, might also be linked to human movements of non- residential visitors. Yet a full empirical validation of the latter is lacking. The prime reason is that prior studies are limited to aggregated statistics of human visitors rather than mobility flows and, because of that, neglect the temporal dynamics of individual human movements. As a remedy, we provide the first work which studies the ability of granular human mo- bility in describing and predicting crime concentrations at an hourly scale. For this purpose, we propose the use of data from location technology platforms. This type of data allows us to trace individual transitions and, therefore, we succeed in distinguishing different mobility flows that (i) are incom- ing or outgoing from a neighborhood, (ii) remain within it, or (iii) refer to transitions where people only pass through the neighborhood. Our evaluation infers mobility flows by lever- aging an anonymized dataset from Foursquare that includes almost 14.8 million consecutive check-ins in three major U.S. cities. According to our empirical results, mobility flows are significantly and positively linked to crime. These findings advance our theoretical understanding, as they provide con- firmatory evidence for crime pattern theory. Furthermore, our novel use of digital location services data proves to be an effective tool for crime forecasting. It also offers unprece- dented granularity when studying the connection between hu- man mobility and crime.
dc.language.isoenen
dc.publisherAssociation for the Advancement of Artificial Intelligence
dc.titleLeveraging Mobility Flows from Location Technology Platforms to Test Crime Pattern Theory in Large Citiesen
dc.typeConference Object
prism.publicationDate2020en
prism.publicationNamePROCEEDINGS OF THE FOURTEENTH INTERNATIONAL AAAI CONFERENCE ON WEB AND SOCIAL MEDIAen
dc.identifier.doi10.17863/CAM.51617
dcterms.dateAccepted2020-04-01en
rioxxterms.versionAMen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2020-06-02en
dc.contributor.orcidMascolo, Cecilia [0000-0001-9614-4380]
rioxxterms.typeConference Paper/Proceeding/Abstracten
dc.identifier.urlhttps://www.aaai.org/Library/ICWSM/icwsm20contents.phpen
pubs.conference-nameInternational Conference on Web and Social Media.en
pubs.conference-start-date2020-06-08en
cam.orpheus.successThu Nov 05 11:52:32 GMT 2020 - Embargo updated*
rioxxterms.freetoread.startdate2021-06-02


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