Search Results (39)

Property crime is a pressing issue in maintaining social order and urban sustainability, particularly in regions marked by pronounced socioeconomic disparity. While the link between socioeconomic stress and crime is well established, regional variations in Thailand have not been fully examined. Therefore, the purpose of this research was to examine spatial patterns of property crime and identify the potential associations between property crime and socioeconomic environment across Thailand. Using nationally compiled property-crime data from official sources across all provinces of Thailand, we employed geographic information system (GIS) tools to conduct a spatial cluster analysis at the sub-national level across 76 provinces. Both global and local statistical techniques were applied to identify spatial associations between property-crime rates and neighborhood-level socioeconomic conditions. The results revealed that property-crime clusters are primarily concentrated in the south, while low-crime areas dominate parts of the north and northeast regions. To analyze the spatial dynamics of property crime, we used geospatial statistical models to investigate the influence of socioeconomic variables across provinces. We found that property-crime rates were significantly associated with monthly income, areas experiencing high levels of household debt, migrant populations, working-age populations, an uneducated labor force, and population density. Identifying associated factors and mapping geographic regions with significant spatial clusters is an effective approach for determining where issues concentrate and for deepening understanding of the underlying patterns and drivers of property crime. This study offers actionable insights for enhancing safety, resilience, and urban sustainability in Thailand’s diverse regional contexts by highlighting geographies of vulnerability. Full article

Contrary to what is represented in geospatial databases, places are dynamic and shaped by events. Point clustering analysis commonly assumes events occur in an empty space and therefore ignores geospatial features where events take place. This research introduces relational density, a novel concept redefining density as relative to the spatial structure of geospatial features rather than an absolute measure. Building on this, we developed Space-Time Plume, a new algorithm for detecting and tracking evolving event clusters as smoke plumes in space and time, representing dynamic places. Unlike conventional density-based methods, Space-Time Plume dynamically adapts spatial reachability based on the underlying spatial structure and other zone-based parameters across multiple temporal intervals to capture hierarchical plume dynamics. The algorithm tracks plume progression, identifies spatiotemporal relationships, and reveals the emergence, evolution, and disappearance of event-driven places. A case study of crime events in Dallas, Texas, USA, demonstrates the algorithm’s performance and its capacity to represent and compute criminogenic places. We further enhance metaball rendering with Perlin noise to visualize plume structures and their spatiotemporal evolution. A comparative analysis with ST-DBSCAN shows Space-Time Plume’s competitive computational efficiency and ability to represent dynamic places with richer geographic insights. Full article

With rapid urbanization, the spatial layout and functional characteristics of urban facilities have a strong correlation with the spatial distribution of criminal activities. Using Changsha City as a case study, this research analyzes 2023 urban crime data, Point of Interest (POI) data, and socioeconomic data. The Multi-scale Geographically Weighted Regression (MGWR) model and clustering analysis are applied to examine how different types of urban facilities influence the spatial heterogeneity of crimes and propose tailored urban planning recommendations and crime prevention strategies. The findings reveal the following: (1) The spatial distribution of crimes in Changsha’s central urban area demonstrates significant spatial heterogeneity. Property crimes dominate in frequency and spatial distribution, primarily clustering around commercial hubs and transport nodes, while violent crimes are more common in scenic areas and open spaces with high pedestrian flow. (2) The impact of built facilities on crime exhibits spatial variability. Facilities such as Financial Services Facilities (FSF) and Shopping facilities (SHF) significantly contribute to property crime in core urban areas, while Scientific, educational, and cultural facilities (SEC) suppress crime in university towns. Scenic spots and facilities (SPF) are associated with violent crimes near scenic site entrances and transport hubs. (3) Facility resource allocation and preventive strategies should be optimized based on dominant factors in different areas to enhance security management efficiency through precise and differentiated planning, fostering sustainable urban safety systems. This study provides insights into the spatial patterns of crime distribution and its dominant factors from the perspective of urban facilities, offering a scientific basis for improving urban crime management and facility planning. Full article

Simultaneous causality occurs when two variables mutually influence each other, creating empirical contexts where cause and effect are not clearly unidirectional. Crime and policing often appear in urban studies presenting the following characteristic: sometimes, increased police patrols can reduce criminal activities, and other times, higher crime rates can prompt law enforcement administrations to increase patrols in affected areas. This study aims to explore the relationships between patrol dynamics and crime locations using spatial regression to support public policies. We identify spatial patterns and the potential impact of crime on policing and vice versa. Data on crimes and patrol locations were collected from the database provided by the Planning and Management Secretariat and the Social Defense Secretariat of Pernambuco, Brazil. The study employed Ordinary Least Squares (OLS) to create a spatial simultaneous regression model for integrated security zones within the Brazilian geography. This approach provides a holistic visualization, enhancing our understanding and predictive capabilities regarding the intricate relationship between police presence and crime. The results report a significant relationship, with crime locations explaining police patrols (varying in geographic domain and type of crime). No statistically significant results from most geographic locations point to the inverse relation. The quantitative analysis segregated by typology presents a potential for effective public decision support by identifying the categories that most influence the patrol security time. Full article

Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during the nighttime, investigating the temporal definition of nighttime crime and the correlation between nighttime lights and criminal activities. The study concentrates on four types of nighttime crimes, assault, theft, burglary, and robbery, conducting univariate and multivariate analyses. In the univariate analysis, correlations between nighttime crimes and nighttime light (NTL) values detected in satellite images and between streetlight density and nighttime crimes are explored. The results highlight that nighttime burglary strongly relates to NTL and streetlight density. The multivariate analysis delves into the relationships between each nighttime crime type and socioeconomic and urban infrastructure variables. Once again, nighttime burglary exhibits the highest correlation. For both univariate and multivariate regression models the geographically weighted regression (GWR) outperforms ordinary least squares (OLS) regression in explaining the relationships. This study underscores the importance of considering the location and offense time in crime geography research and emphasizes the potential of using NTL in nighttime crime analysis. Full article

Introduction: A neighbourhood’s environmental characteristics can positively or negatively influence health and well-being. To date, no studies have examined this concept in the context of Saudi Arabian youth. Therefore, this study aimed to evaluate the association between a neighbourhood’s environmental characteristics and health risk factors among Saudi Arabian youth. Methods: A total of 335 secondary-school students (175 males, 160 females), aged 15–19 years old, participated. Body mass index (BMI) and waist circumference measurements were taken, and physical activity (steps) was measured via pedometer. The perceived neighbourhood environment was assessed using the International Physical Activity Questionnaire Environment Module (IPAQ-E). Results: Significant differences were found between the youths from urban, rural farm, and rural desert locations in terms of BMI, waist circumference, daily steps, accessibility, infrastructure, social environment, household vehicles, safety, and access to facilities (p < 0.001). Rural desert youths were less active, and males (26.43 + 8.13) and females (24.68 + 5.03) had higher BMIs compared to the youths from other areas. Chi-square analysis revealed a significant difference (χ²₁ = 12.664, p < 0.001) between the genders as to social-environment perceptions. Males perceived their neighbourhood as a social environment more than was reported by females (68.39% and 50.28%, respectively). Pearson’s correlation revealed negative significant relationships between steps and both safety of neighbourhood (r = −0.235, p < 0.001) and crime rate (r = −0.281, p < 0.001). Discussion: Geographical location, cultural attitudes, lack of facilities, and accessibility impact youth physical-activity engagement and weight status; this includes environmental variables such as residential density, neighbourhood safety, household motor vehicles, and social environment. Conclusions: This is the first study examining associations with neighbourhood environments in the youths of the Kingdom of Saudi Arabia. Significant associations and geographical differences were found. More research and policy interventions to address neighbourhoods’ environmental characteristics and health risk factors relative to Saudi Arabian youth are warranted. Full article

The inference of biogeographical ancestry (BGA) can assist in police investigations of serious crime cases and help to identify missing people and victims of mass disasters. In this study, we evaluated the typing performance of 56 ancestry-informative SNPs in 177 samples using the ForenSeq™ DNA Signature Prep Kit on the MiSeq FGx system. Furthermore, we compared the prediction accuracy of the tools Universal Analysis Software v1.2 (UAS), the FROG-kb, and GenoGeographer when inferring the ancestry of 503 Europeans, 22 non-Europeans, and 5 individuals with co-ancestry. The kit was highly sensitive with complete aiSNP profiles in samples with as low as 250pg input DNA. However, in line with others, we observed low read depth and occasional drop-out in some SNPs. Therefore, we suggest not using less than the recommended 1ng of input DNA. FROG-kb and GenoGeographer accurately predicted both Europeans (99.6% and 91.8% correct, respectively) and non-Europeans (95.4% and 90.9% correct, respectively). The UAS was highly accurate when predicting Europeans (96.0% correct) but performed poorer when predicting non-Europeans (40.9% correct). None of the tools were able to correctly predict individuals with co-ancestry. Our study demonstrates that the use of multiple prediction tools will increase the prediction accuracy of BGA inference in forensic casework. Full article

This study aimed to evaluate the relationships between different groups of explanatory variables (i.e., dynamic human activity variables, static variables of social disorganisation and crime generators, and combinations of both sets of variables) and property crime patterns across neighbourhood areas of London during the pandemic (from 2020 to 2021). Using the dynamic human activity variables sensed from mobile phone GPS big data sets, three types of ‘Least Absolute Shrinkage and Selection Operator’ (LASSO) regression models (i.e., static, dynamic, and static and dynamic) differentiated into explanatory variable groups were developed for seven types of property crime. Then, the geographically weighted regression (GWR) model was used to reveal the spatial associations between distinct explanatory variables and the specific type of crime. The findings demonstrated that human activity dynamics impose a substantially stronger influence on specific types of property crimes than other static variables. In terms of crime type, theft obtained particularly high relationships with dynamic human activity compared to other property crimes. Further analysis revealed important nuances in the spatial associations between property crimes and human activity across different contexts during the pandemic. The result provides support for crime risk prediction that considers the impact of dynamic human activity variables and their varying influences in distinct situations. Full article

Urban crimes are not homogeneously distributed but exhibit spatial heterogeneity across a range of spatial scales. Meanwhile, while geographic space shapes human activities, it is also closely related to multiscale characteristics. Previous studies have explored the influence of underlying geographic space on crime occurrence from the mechanistic perspective, treating geographic space as a collection of points or lines, neglecting the multiscale nature of the spatial heterogeneity of crime and underlying geographic space. Therefore, inspired by the recent concept of “living structure” in geographic information science, this study applied a multiscale analysis method to explore the association between underlying geographic space and crime distribution. Firstly, the multiscale heterogeneity is described while simultaneously considering both the statistical and geometrical characteristics. Then, the spatial association rule mining approach is adopted to quantitatively measure the association between crime occurrence and geographic space at multiple scales. Finally, the effectiveness of the proposed methods is evaluated by crime incidents in the city of Philadelphia. Experimental results show that crime heterogeneity is indeed closely related with the spatial scales. It is also proven that the influence of underlying geographic space on crime heterogeneity varies with the spatial scales. This study may enrich the methodology in crime pattern and crime explanation analysis, and it provides useful insights for effective crime prevention. Full article

Typically, emergency bells are security facilities that, when activated, trigger an alarm and immediately dispatch a police car to prevent crime. However, there currently exists an ambiguity in the criteria for emergency bell installation. Consequently, this study aims to find an optimal location for emergency bells whilst considering several factors like cumulative crime incidents. In particular, we exploited emergency bell location data, data on five major crimes, and the geographic information of administrative dongs (primary division of districts) in this study. Specifically, we performed correlation analysis, principal component analysis, and K-means clustering for exploratory data analysis. To effectively cover all 17,437 crimes, which are not covered by the existing emergency bells in Gwangju metropolitan city from 2018 to 2021, the results from the implementation of the emergency bell location set-covering problem revealed the need for about 6228 emergency bells. More precisely, the emergency bell maximal covering location problem was employed to derive the coverage percentage for 250, 500, 800, 1000, and 1500 emergency bells. The results showed that 2850 emergency bells were required to cover over 80% of crime occurrence coordinates, saving over half of the budget compared with covering them all. Overall, this study is noteworthy in its potential role as a roadmap for the optimal placement of emergency bells for future crime prevention. Full article

The intricate relationship between financial fraud and credit risk, and their combined impact on banking stability, is a vital and under-researched aspect of financial system integrity. To fill this knowledge gap, this study embarked on a thorough bibliometric analysis of the field, utilizing 2790 documents from various sources, including 1853 articles, 504 books, and 177 reviews, spanning the years 1990 to 2023. Utilizing advanced tools, like Biblioshiny and VOSviewer, this study illuminated key geographical, thematic, and intellectual trends, shedding light on an annual growth rate of 13.43% in the related literature and an average citation per document of 28.29. This detailed analysis offered valuable insights into the current research landscape, emphasizing areas such as author collaboration, with 20.32% international co-authorships, and the prevalence of single-authored documents, at 1100. Despite the existing body of research, the interconnected dynamics between financial fraud and credit risk and their implications for banking stability remain underexplored. Therefore, this study sought to unravel this complex relationship and examine its effects at both the micro (individual banks) and macro (banking sector and wider economy) levels. The findings carry significant practical implications, informing policy development, shaping risk management strategies, and contributing to regulatory measures. Despite its limitations, including the potential transformation of identified trends due to evolving financial systems and financial crimes, this study represents a significant contribution to scholarly discourse in the field. It lays the groundwork for future research and facilitates a more secure and resilient banking sector, reflecting the data-driven insights obtained from the research. Full article

This paper analyses the spatial trends and patterns of the crime rates in London and explores how socio-economic characteristics affect crime rates with consideration of the geographic context across London. The 2015 London Crime Statistics and Socio-economic Characteristics datasets were used. First, we investigated the spatial patterns of crime rates through exploratory spatial analysis at the ward level. In addition, both the ordinary least square (OLS) model and geographically weighted regression (GWR) model, which allow the effects of factors to vary in spatial scales, were adopted and compared to explore the potential spatially varying effect across London. The results showed that there exists obvious spatial clustering for the crime rate in central London. Both global and local Moran’s I values indicated the spatial dependence of crime at the ward level. The GWR model performed better in explaining crime rates than the OLS model. Only two factors, namely, the percentage of children aged from 0 to 15 and employment rates, had significant spatial variability in London. The influences of the percentage of children aged 0 to 15 on crime rates are constantly negative over a spatial scale; however, employment rates positively affect crime rates in the north-western areas near the centre of London. Therefore, this paper focuses more on the spatial perspective, which fills the gap in traditional crime analysis, especially on the spatially varying influence of socio-economic status. Full article

Geographical mapping has revolutionized data analysis with the help of analytical tools in the fields of social and economic studies, whereby representing statistical research variables of interest as geographic characteristics presents visual insights. This study employed the QGIS mapping tool to create predicted choropleth maps of Visegrád Group countries based on crime rate. The forecast of the crime rate was generated by time series analysis using the ARIMA (autoregressive integrated moving averages) model in SPSS. The literature suggests that many variables influence crime rates, including unemployment. There is always a need for the integration of widespread data insights into unified analyses and/or platforms. For that reason, we have taken the unemployment rate as a predictor series to predict the future rates of crime in a comparative setting. This study can be extended to several other predictors, broadening the scope of the findings. Predictive data-based choropleth maps contribute to informed decision making and proactive resource allocation in public safety and security administration, including police patrol operations. This study addresses how effectively we can utilize raw crime rate statistics in time series forecasting. Moreover, a visual assessment of safety and security situations using ARIMA models in SPSS based on predictor time-series data was performed, resulting in predictive crime mapping. Full article

Emergency calls are defined by an ever-expanding utilisation of information and sensing technology, leading to extensive volumes of spatio-temporal high-resolution data. The spatial and temporal character of the emergency calls is leveraged by authorities to allocate resources and infrastructure for an effective response, to identify high-risk event areas, and to develop contingency strategies. In this context, the spatio-temporal analysis of emergency calls is crucial to understanding and mitigating distress situations. However, modelling and predicting crime-related emergency calls remain challenging due to their heterogeneous and dynamic nature with complex underlying processes. In this context, we propose a modelling strategy that accounts for the intrinsic complex space–time dynamics of some crime data on cities by handling complex advection, diffusion, relocation, and volatility processes. This study presents a predictive framework capable of assimilating data and providing confidence estimates on the predictions. By analysing the dynamics of the weekly number of emergency calls in Valencia, Spain, for ten years (2010–2020), we aim to understand and forecast the spatio-temporal behaviour of emergency calls in an urban environment. We include putative geographical variables, as well as distances to relevant city landmarks, into the spatio-temporal point process modelling framework to measure the effect deterministic components exert on the intensity of emergency calls in Valencia. Our results show how landmarks attract or repel offenders and act as proxies to identify areas with high or low emergency calls. We are also able to estimate the weekly average growth and decay in space and time of the emergency calls. Our proposal is intended to guide mitigation strategies and policy. Full article

This paper presents an attempt at establishing an association between crime levels and prison populations across European countries. We observe that the situation in Central and Eastern European countries differs distinctly from the rest of Europe. Building on this, we offer justification that is methodologically based on correlations and regressions of country incarceration rates on crime rates, with reference to governance indicators. Our cross-sectional analysis uses data on crime and prisoner rates by offence from Eurostat and SPACE for the year 2018. The paper’s empirical analysis is preceded by a discussion of the challenges faced when attempting to compare crime between countries in Europe. A review of research focused on relationships between incarceration and crime follows, with the emphasis on the deterrence effect and the prison paradox. Typically, this stream of research uses microdata covering a single country or limited to a smaller geographic area. International comparisons are rare, and are usually based on time series and trend analyses. The quantitative approach applied here is based on recognizing two clusters of countries: the Central and Eastern European (CEE) cluster and the Western European (WE) cluster. We show that the observation of higher prisoner rates and lower crime rates for CEE countries is confirmed with regression analysis. Our study encompasses four types of offences: assault, rape, robbery, and theft. The final section of the paper presents an attempt to incorporate Worldwide Governance Indicators into the analysis of the association between incarceration and crime rates. The results confirm that crime rates in WE countries are distinctly higher than in CEE countries, while incarceration rates in WE are significantly lower than in CEE countries. We think this is due to a higher percentage of crimes being reported and the greater accuracy of police statistics in WE countries. The prison population in each country is largely determined by its criminal and penal policies, which differ substantially between CEE and WE countries (e.g., in terms of frequency of imposing prison sentences and the length of imprisonment). These tendencies result in higher incarceration rates in CEE countries, despite lower crime rates when compared to WE countries. Full article