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A Systematic Review of Mobile Phone Data in Crime Applications: A Coherent Taxonomy Based on Data Types and Analysis Perspectives, Challenges, and Future Research Directions

Sensors (Basel, Switzerland), 2023-04, Vol.23 (9), p.4350 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2023 by the authors. 2023 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s23094350 ;PMID: 37177554

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  • Title:
    A Systematic Review of Mobile Phone Data in Crime Applications: A Coherent Taxonomy Based on Data Types and Analysis Perspectives, Challenges, and Future Research Directions
  • Author: Okmi, Mohammed ; Por, Lip Yee ; Ang, Tan Fong ; Al-Hussein, Ward ; Ku, Chin Soon
  • Subjects: Behavior ; call detail records (CDRs) ; Cell Phone ; Cell phones ; Cellular telephones ; China ; Communication ; Correlation analysis ; Crime ; Criminal investigations ; criminal networks ; Criminology ; Density distribution ; Human acts ; Human behavior ; Human communication ; human communication behavior ; Human motion ; Humans ; Literature reviews ; Mobile devices ; mobile phone data ; Mobility ; Network analysis ; New Jersey ; Organized crime ; Review ; Short message service ; Smartphones ; Social network analysis ; Social networks ; Systematic review ; Taxonomy ; Technology application ; Telecommunications towers ; Text Messaging ; Transportation ; Transportation networks ; Transportation planning ; United Kingdom ; urban dynamics ; urban human mobility patterns
  • Is Part Of: Sensors (Basel, Switzerland), 2023-04, Vol.23 (9), p.4350
  • Description: Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial-temporal patterns of crime, and ambient population measures have a significant impact on crime rates.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s23094350
    PMID: 37177554
  • Source: DOAJ : Directory of Open Access Journals
    Freely Accessible Journals
    MEDLINE
    PubMed Central
    Coronavirus Research Database
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central

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