A tested method for assessing and predicting weather-crime associations

Algahtany, Mofza; Kumar, Lalit; Barclay, Elaine

doi:10.1007/s11356-022-20440-6

Cited by 3 publications

(2 citation statements)

References 78 publications

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“…When considering static social environmental factors, two types of environmental factors– diurnal and weather—are selected in this section. This is because poor weather conditions can limit going out behavior and increase online behavior, while dark environments can promote the growth of sensuality and impulsivity and increase the likelihood of being cheated [ 32 , 33 ].…”

Section: Bbn Network Model Design and Constructionmentioning

confidence: 99%

A Framework for Analyzing Fraud Risk Warning and Interference Effects by Fusing Multivariate Heterogeneous Data: A Bayesian Belief Network

et al. 2023

Entropy

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In the construction of a telecom-fraud risk warning and intervention-effect prediction model, how to apply multivariate heterogeneous data to the front-end prevention and management of telecommunication network fraud has become one of the focuses of this research. The Bayesian network-based fraud risk warning and intervention model was designed by taking into account existing data accumulation, the related literature, and expert knowledge. The initial structure of the model was improved by utilizing City S as an application example, and a telecom-fraud analysis and warning framework was proposed by incorporating telecom-fraud mapping. After the evaluation in this paper, the model shows that age has a maximum sensitivity of 13.5% to telecom-fraud losses; anti-fraud propaganda can reduce the probability of losses above 300,000 yuan by 2%; and the overall telecom-fraud losses show that more occur in the summer and less occur in the autumn, and that the Double 11 period and other special time points are prominent. The model in this paper has good application value in the real-world field, and the analysis of the early warning framework can provide decision support for the police and the community to identify the groups, locations, and spatial and temporal environments prone to fraud, to combat propaganda and provide a timely warning to stop losses.

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Section: Bbn Network Model Design and Constructionmentioning

confidence: 99%

A Framework for Analyzing Fraud Risk Warning and Interference Effects by Fusing Multivariate Heterogeneous Data: A Bayesian Belief Network

et al. 2023

Entropy

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“…For example, robbery and burglary are less likely to occur during periods of extreme weather [26], while both violent and non-violent robbery rates increase significantly with heat stress in spring [27]. Haze pollution has the greatest impact on theft, drug-related, and assault crimes [28], and bus pickpocketing increases with worsening air quality [29]. Urban construction can also affect crime, such as the positive correlation between nightlight gradients and street robbery rates, but negative correlation between nightlight gradients and burglary rates [30].…”

Section: Factors Affecting Crimementioning

confidence: 99%

Crime Risk Analysis of Tangible Cultural Heritage in China from a Spatial Perspective

Ding

Zhai

2023

IJGI

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Tangible cultural heritage is vulnerable to various risks, particularly those stemming from criminal activity. Through analyzing the distribution and flow of crime risks from a spatial perspective based on quantitative methods, risks can be better managed to contribute to the protection of cultural heritage. This paper explores and summarizes the spatial characteristics of crime risks from 2011 to 2019 in China. Firstly, the average nearest neighbor (ANN) and the Jenks Natural Breaks Classification method showed that the national key protected heritage sites (NPS) and crime risks exhibit clustering features in space, and most of the NPS were located in the middle and lower reaches of the Yangtze River and the Yellow River. Secondly, the economy has no impact on crime risks in the spatial statistical analysis. However, the population density, distribution of NPS, and tourism development influenced specific types of crime risks. Finally, Global Moran’s I was used to examine the strong sensitivity between crime risks and cultural relics protection policies. The quantitative results of this study can be applied to improve strategies for crime risk prevention and the effectiveness of heritage security policy formulation.

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The impact of weather anomalies on violence in the subtropics: A cross-national comparison

Breetzke,

Corcoran

2024

Preprint

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Cross-national studies examining the relationship between weather and crime are rare. Reasons are manifold but include the differences in countries due to geographical, climatic, and seasonal variations. In contrast in this study we examine the causal impact of temperature and rainfall anomalies on violent crime in two locations located in comparable geographic and climatic zones: Khayelitsha (in South Africa) and Ipswich (in Australia). We use ANOVA and Tukey's tests to identify statistical meaningful differences (if any) in the impact of these weather anomalies on crime alongside the use of visualisations capturing the anomalous weather-violence relationship in these two contexts. Results show some similarities but also notable differences between locations which we attribute to their inherent socio-demographic differences which we expand upon. We conclude by highlighting the benefits of cross-national crime research, and motivate for its increased application in future research of this nature.

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A tested method for assessing and predicting weather-crime associations

Cited by 3 publications

References 78 publications

A Framework for Analyzing Fraud Risk Warning and Interference Effects by Fusing Multivariate Heterogeneous Data: A Bayesian Belief Network

A Framework for Analyzing Fraud Risk Warning and Interference Effects by Fusing Multivariate Heterogeneous Data: A Bayesian Belief Network

Crime Risk Analysis of Tangible Cultural Heritage in China from a Spatial Perspective

The impact of weather anomalies on violence in the subtropics: A cross-national comparison

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