| Autor: |
Okmi M; Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia.; Department of Information Technology and Security, Jazan University, Jazan 45142, Saudi Arabia., Por LY; Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia., Ang TF; Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia., Al-Hussein W; Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia., Ku CS; Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia. |
| Jazyk: |
angličtina |
| Zdroj: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Apr 28; Vol. 23 (9). Date of Electronic Publication: 2023 Apr 28. |
| DOI: |
10.3390/s23094350 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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