Simple Implementation of Criminal Investigation using Call Data Records (CDRs) through Big Data Technology

Khan, Er. Saiqa; Azmi, Hamza; Ansari, Firoz; Dhalvelkar, Sabiqua

doi:10.1109/icscet.2018.8537389

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…However, other studies [ 48 , 49 , 50 , 51 , 52 ] have not considered the spatial mapping of mobile devices to a given spatial unit. In this case, mobile phone locations were originally assigned to the coverage areas of a base station [ 16 ].…”

Section: Human Mobility Patternsmentioning

confidence: 99%

Mobile Phone Data: A Survey of Techniques, Features, and Applications

Okmi

Por

Ang

et al. 2023

Sensors

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Due to the rapid growth in the use of smartphones, the digital traces (e.g., mobile phone data, call detail records) left by the use of these devices have been widely employed to assess and predict human communication behaviors and mobility patterns in various disciplines and domains, such as urban sensing, epidemiology, public transportation, data protection, and criminology. These digital traces provide significant spatiotemporal (geospatial and time-related) data, revealing people’s mobility patterns as well as communication (incoming and outgoing calls) data, revealing people’s social networks and interactions. Thus, service providers collect smartphone data by recording the details of every user activity or interaction (e.g., making a phone call, sending a text message, or accessing the internet) done using a smartphone and storing these details on their databases. This paper surveys different methods and approaches for assessing and predicting human communication behaviors and mobility patterns from mobile phone data and differentiates them in terms of their strengths and weaknesses. It also gives information about spatial, temporal, and call characteristics that have been extracted from mobile phone data and used to model how people communicate and move. We survey mobile phone data research published between 2013 and 2021 from eight main databases, namely, the ACM Digital Library, IEEE Xplore, MDPI, SAGE, Science Direct, Scopus, SpringerLink, and Web of Science. Based on our inclusion and exclusion criteria, 148 studies were selected.

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Section: Human Mobility Patternsmentioning

confidence: 99%

Mobile Phone Data: A Survey of Techniques, Features, and Applications

Okmi

Por

Ang

et al. 2023

Sensors

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“…This study aimed to identify suspects based on their calling characteristics, including any phone calls made or received by the suspects at the crime scene, in conjunction with archived CDRs data drawn from a central database that contained details on previously convicted criminals whose names had been recorded in older cases. [84,85] Call and spatiotemporal features These studies aimed to improve the identification performance of suspects in terms of efficiency, effort, and scalability. To achieve this, they proposed a system-based big data analytic process to extract communication and mobility information from CDRs data, including aspects such as the most frequent caller, the number of times the suspect called other suspects, call frequency, suspect trajectories, and the most visited location based on the most frequently used cell tower.…”

Section: Referencementioning

confidence: 99%

“…In criminal investigations, location-based mobile phone data can be used to indicate the presence of suspects in an area at a certain time when a crime has taken place, whereas communication traces can be used to identify accomplices in a criminal activity. As examples, References [82,84] collected mobility traces left at crime scenes to determine suspects' positions and presence at the crime scenes.…”

Section: The First Group Of Applications Deals With Using Mobile Phon...mentioning

confidence: 99%

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

Okmi

Por

Ang

et al. 2023

Sensors

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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.

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“…The collected information for criminal analysis comes from various sources like bank transaction records, mobile call records, web, and social media (e. g. Social meeting sites like Facebook, Twitter, and LinkedIn), etc. [105,106]. These areas include what is related to stealing money through fake electronic cards and using them in purchases and obtaining services illegally.…”

Section: Criminal Analysis and Fraud Detectionmentioning

confidence: 99%

Big Data: Analytical Solutions, Research Challenges and Trends

Ali¹,

Novikov²

2020

Proceedings of ISP RAS

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The term Big Data refers to an extensive collections of digital data generating every second. Produced datasets come in structured, semi-structured, and unstructured formats throughout the world, which is difficult for the traditional database management systems to analyze. Recently, big data analytics emerges as an essential research area due to the popularity of the Internet and the advent of new Web technologies. This growing area of research represents a multidisciplinary that attracts researchers from various research fields. Interested researchers are invited to design, develop, and implement several tools, technologies, architecture, and platforms for analyzing these large volumes of data. This paper begins with a brief introduction to big data and related concepts, including the main characteristics of big data, followed by discussions of the most significant open research challenges and emerging trends. Next, we review a study of big data analytics, the advantages of using big data solutions, and the preliminary assessments required before migrating from traditional solutions. Finally, we present a review of the recent main applications to obtain a broad perspective of big data analytics.

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Simple Implementation of Criminal Investigation using Call Data Records (CDRs) through Big Data Technology

Cited by 8 publications

References 11 publications

Mobile Phone Data: A Survey of Techniques, Features, and Applications

Mobile Phone Data: A Survey of Techniques, Features, and Applications

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

Big Data: Analytical Solutions, Research Challenges and Trends

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