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Discovering Geographical Patterns of Crime Localization in Mexico City

Authors:
Roberto Zagal-Flores
Félix Mata-Rivera
Christophe Claramunt
Edgar Catalan-Salgado

Keywords: Crossing-data; Social Web mining; Geo-social Web Analytics; Web ontology

Abstract:
The search for a better understanding of crime patterns in large urban areas is still a crucial issue that deserves novel research methods and approaches. In particular, combination of institutional databases and novel information medias such as social networks appear as a promising trend that might favor development of more efficient criminal information management and crime prevention systems. However, most existing systems do not take into account to the best of our knowledge the geographical dimension although this might provide a better representation of how crimes spread over space and time. The research presented in this paper develops a knowledge discovery approach based on a close integration of official, social and geographical data sources. The result is a modeling approach that provides a-priori knowledge of safe and unsafe places and the ones that are even candidates to become unsecured places. The aim is to not only give an overall geographical representation of crime patterns that might be useful for decision-makers, but also web-based resources to the citizens. The whole approach has been applied to Mexico City.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2017

Publication date: May 21, 2017

Published in: conference

ISSN: 2308-4421

ISBN: 978-1-61208-557-9

Location: Barcelona, Spain

Dates: from May 21, 2017 to May 25, 2017