Could social media reflect acquisitive crime patterns in London?

Wang, Zenghui; Li, Yijing

doi:10.1016/j.jnlssr.2021.08.007

Cited by 5 publications

(1 citation statement)

References 72 publications

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“…Geotagged tweets contain information about people's actual location at a given time. This opens new possibilities for geography of crime studies, e.g., predicting future crime patterns [25,26]; preventing crime in short-and medium-term periods [27]; detecting the ambient population for crime analysis [12,[28][29][30]; analyzing crime's intraday variations and the spillover effect of the ambient population [31]; detecting emerging crimes, traffic accidents, emergencies, and hazards, etc. [32,33]; studying the importance of Twitter as a platform for the crime news dissemination [34]; assessing how social media influence the number of different types of crime [35]; helping people to report suspicious activities or crimes [36]; providing security alerts for the real-time detection of phishing tweets and security alert proposal [37]; assessing how major events influence crime patterns in cities [13,38]; studying crime related to prejudice or intolerance towards the issues related to national origin, sentiment, religion, race, etc.…”

Section: Introductionmentioning

confidence: 99%

Different Ways Ambient and Immobile Population Distributions Influence Urban Crime Patterns

Sypion-Dutkowska

Lan

Dutkowski

et al. 2022

IJGI

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The article aims to propose a new way of estimating the ambient and immobile urban population using geotagged tweets and age structure, and to test how they are related to urban crime patterns. Using geotagged tweets and age structure data in 37 neighborhoods of Szczecin, Poland, we analyzed the following crime types that occurred during 2015–2017: burglary in commercial buildings, drug crime, fight and battery, property damage, and theft. Using negative binomial regression models, we found a positive correlation between the size of the ambient population and all investigated crime types. Additionally, neighborhoods with more immobile populations (younger than 16 or older than 65) tend to experience more commercial burglaries, but not other crime types. This may be related to the urban structure of Szczecin, Poland. Neighborhoods with higher rates of poverty and unemployment tend to experience more commercial burglaries, drug problems, property damage, and thefts. Additionally, the count of liquor stores is positively related to drug crime, fight-battery, and theft. This article suggests that the age structure of the population has an influence on the distribution of crime, thus it is necessary to tailor crime prevention strategies for different areas of the city.

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Section: Introductionmentioning

confidence: 99%

Different Ways Ambient and Immobile Population Distributions Influence Urban Crime Patterns

Sypion-Dutkowska

Lan

Dutkowski

et al. 2022

IJGI

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GIS Empowered Urban Crime Research

Li,

Haining

2022

New Thinking in GIScience

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Role of Artificial Intelligence in the crime prediction and pattern analysis studies published over the last decade: a scientometric analysis

Kaur,

Saini

2024

Artif Intell Rev

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Crime is the intentional commission of an act usually suspected as socially detrimental and specifically defined, forbidden, and punishable under criminal law. Developing a society that is less susceptible to criminal acts makes crime prediction and pattern analysis (CPPA) a paramount topic for academic research interest. With the innovation in technology and rapid expansion of Artificial Intelligence (AI), the research in the field of CPPA has evolved radically to predict crime efficiently. While the number of publications is expanding substantially, we believe there is a dearth of thorough scientometric analysis for this topic. This work intends to analyze research conducted in the last decade using Scopus data and a scientometric technique, emphasizing citation trends and intriguing journals, nations, institutions, their collaborations, authors, and co-authorship networks in CPPA research. Furthermore, three field plots have been staged to visualize numerous associations between country, journal, keyword, and author. Besides, a comprehensive keyword analysis is carried out to visualize the CPPA research carried out with AI amalgamation. A total of five clusters have been identified depicting several AI methods used by the researchers in CPPA and the evolution of research trends over time from various perspectives.

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Could social media reflect acquisitive crime patterns in London?

Cited by 5 publications

References 72 publications

Different Ways Ambient and Immobile Population Distributions Influence Urban Crime Patterns

Different Ways Ambient and Immobile Population Distributions Influence Urban Crime Patterns

GIS Empowered Urban Crime Research

Role of Artificial Intelligence in the crime prediction and pattern analysis studies published over the last decade: a scientometric analysis

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