Analysis of Criminal Spatial Events in GIS for predicting hotspots

Mohammed, Abbas; Baiee, Wadhah R.

doi:10.1088/1757-899x/928/3/032071

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…Therefore, in our next research phase, we will consider the impact of education on criminal activities [ 32 , 33 , 34 ]. In addition, due to the availability and limitations of the current data, this study was unable to conduct a longer time sequence analysis of the dynamics of criminal activities.…”

Section: Discussionmentioning

confidence: 99%

Identifying the Spatio-Temporal Characteristics of Crime in Liangshan Prefecture, China

Cheng

Rao

Tang

et al. 2022

IJERPH

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Crime prevention and governance play critical roles in public security management. Liangshan Yi Autonomous Prefecture in Sichuan Province has a high crime rate, and spatio-temporal analysis of crime in this region could assist with public security management. Therefore, Liangshan Prefecture was selected as the research object in this study. The spatial crime data were obtained from China Judgments Online, and property crime, violent crime, and special crime (i.e., pornography, gambling, drugs, and guns) were analyzed. The findings were as follows. In terms of time characteristics (month, day, and hour), property crime tended to occur in autumn and winter, in the early month, on Wednesdays and Fridays, and at early morning. Violent crime tended to occur in winter and spring, on Mondays and Thursdays, and at night. Special crime occurred in spring and autumn, on Tuesdays, and in the daytime. In terms of spatial features, the central region of Liangshan Prefecture was the focal area for crime. There were obvious low-aggregation areas in the western region for special crime. The eastern region exhibited a high incidence of various crimes. Regarding the spatio-temporal evolution characteristics from 2013 to 2019, there were some obvious hotspots of violent and property crime in downtown and surrounding townships of Xichang City, which is the capital of Liangshan Prefecture. During the study period, the incidence of special crime has an obvious downward trend which shows that there are more new cold spots.

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Section: Discussionmentioning

confidence: 99%

Identifying the Spatio-Temporal Characteristics of Crime in Liangshan Prefecture, China

Cheng

Rao

Tang

et al. 2022

IJERPH

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“…The research approach and GIS techniques used in this study are well documented in the literature. For example, Jana and Sar, 52 Yang, 53 Schaffter, 54 and Mohammed and Baiee, 55 produced kernel density maps using the Getis-Ord Gi* statistics function available in the ArcGIS software to calculate optimised hotspot analysis. Yu and Su investigated the performance of kernel density estimation methods, finding that KDE performed better than empirical methods and could be favourably applied in a complex noise environment.…”

Section: Legendmentioning

confidence: 99%

Understanding crime using GIS and the context of COVID-19: the case of Saldanha Bay Municipality

2022

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South Africa faces high levels of crime. The Saldanha Bay Municipality, the setting of this study, is laden with poverty, unemployment and gangsterism that deprive quality of life and contribute to social ills. While crime management and prevention strategies require information regarding crime trends, this information for the Saldanha Bay Municipality area is limited. Hence, the study aimed to illustrate the spatial distribution and trends of crime in the Saldanha Bay Municipality, focusing on the period January 2017 to June 2020, and to indicate the recent impact of COVID-19 on these crime trends. The results of the study are presented by means of graphs and tables, and hotspot mapping was done using the ArcGIS Getis-Ord Gi* statistics tool. These results indicate that crime has increased over the past three years and that criminal activities are linked to urban hubs where most people stay and work. In terms of the effect of the COVID-19 pandemic and the lockdown regulations on crime, it is interesting to note the variations in crime rates during the first three months of lockdown (from April 2020 to June 2020) when compared to the rest of the period under investigation. Amongst the five towns investigated, the town of Vredenburg which has the highest population total and was ranked highest in terms of crime rates prior to the lockdown, moved from first to third, behind Langebaan and St Helena Bay. Similarly, Saldanha Bay with the second highest population total moved down to fourth. Hopefield was still the town with the lowest mean crime rate.

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“…Crime and violence have been increasing at alarming rates in both the type and number of illegal activities committed (Cortés et al, 2018;Cabral et al, 2018;Bansal et al, 2022). This trend has become, in recent years, a determining factor in explaining low levels of development and growth and is a direct consequence of high levels of inequality and poverty (Mohammed & Baiee, 2020;Mojsoska et al, 2021), or increase in unemployment (Soemarsono et al, 2021), especially in emerging regions like Latin America and the Caribbean (UNODC, 2019; Cruz & Vorobyeva, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…On the other hand, several studies point out the geographical heterogeneity in the incidence of criminal activity, representing significant differences between the states or regions in a given country (Cabral et al, 2018;Saucedo & Berry, 2019;Moreno & Saucedo, 2020;Mohammed & Baiee, 2020;Abrams, 2021;Bernardo et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Characterising Crime Incidence in Mexico: A Hierarchical Linkage Clustering Analysis

Preciado

Ríos

2022

IJIE

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We aim to characterise the incidence and distribution of crime in 32 states of Mexico. A hierarchical grouping with average linkage is implemented with crime information from DataMéxico, segmented by state and type of crime from 2015 to 2020. Based on the proportional number of crimes over the population of each state, through the elbow method and the average linkage with a Cophenetic correlation coefficient, we validate the number of clusters. Subsequently, a principal component analysis (PCA) is performed to identify each state’s contribution to the clusters proposed. The main results reveal criminal activity can be characterised by three groups. Drug trafficking is the crime that leads the first group, which in turn generates subgroups of interrelated crimes, such as crimes against the family, sexual abuse and harassment, and falsehood, to name a few. These crimes are committed homogeneously in most of the states of the country. Correspondingly, domestic violence and theft lead clusters two and three, and present significant concentration levels since four states accumulate 62% and 55% of crime incidence respectively. The results also provide an overview of how a particular crime can trigger the presence of others.

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Analysis of Criminal Spatial Events in GIS for predicting hotspots

Cited by 5 publications

References 11 publications

Identifying the Spatio-Temporal Characteristics of Crime in Liangshan Prefecture, China

Identifying the Spatio-Temporal Characteristics of Crime in Liangshan Prefecture, China

Understanding crime using GIS and the context of COVID-19: the case of Saldanha Bay Municipality

Characterising Crime Incidence in Mexico: A Hierarchical Linkage Clustering Analysis

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