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Exploring the relationship between geolocated social network service text and crime

Kim, Eon; (2020) Exploring the relationship between geolocated social network service text and crime. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

The purpose of this thesis is to explore the potential utility of Geocoded Social Network Service datasets, especially geolocated Twitter, to help understand criminal activities in urban settings. In particular, the thesis is concerned with how these datasets can advance understanding of crime events in their spatiotemporal context. Such data has potential to reveal usually unobserved underlying conditions of crime events as a complementary source of knowledge for advancing the development of crime prevention strategies. In pursuit of these goals, the thesis comprises three case studies conducted using data for New York City in the United States. It examines: 1) Mobile dynamic population patterns 2) the emotion patterns of the mobile population, and 3) the attribute patterns of the mobile population (i.e. online-traits such as type and topic of discussion). With the combined results from the three case studies, this thesis explores how adding this new type of data improves existing knowledge of crime patterns. Results demonstrated that tracking spatial-temporal fluctuations of populations, along with their emotions and concerns has potential for explaining patterns across different crime types.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Exploring the relationship between geolocated social network service text and crime
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2020. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI: https://discovery.ucl.ac.uk/id/eprint/10092339
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