Spatial analysis of drug trafficking crimes in urban environments (case study: Kermanshah metropolis)

Criminology has evolved in recent decades and has focused on the environmental dimension. Environmental criminology emphasizes the impact of the physical and urban environment on crime. Crime affects social groups and urban neighborhoods in terms of social and spatial dimensions. Accordingly, the aim of the present study is spatial analysis of drug trafficking crimes in Kermanshah metropolis. The present study is descriptive-analytical in terms of its applied purpose and method. The statistical population includes the legal boundaries of Kermanshah metropolis in 1403. The sample size includes 328 cases of drug-related crimes. To determine the centers of drug abuse crimes, the standard deviation ellipse and the mean center of the Moran index, kernel density, were used. GIS and Crime Analysis software were used to analyze the data. The results of the study indicate that the center of the average drug trafficking crime in Kermanshah city located in the Shaterabad neighborhood. The ellipse of the standard deviation of this crime has a north-south elongation. The Moran index of drug trafficking is 0.93, which confirms its clustering. In terms of kernel density estimation, the most important centers related to drug trafficking crime were in the neighborhood of Jafarabad, Rashidi, Tazeabad, Bagh Ferdows, Bagh Abrisham, Javanshir, Chenani, Masir Naft, Zourabad, Shirin Park, Dieselabad, Dowlatabad, Three Roads of the Company, Chah Saheb-e-Zaman, Laleh Park, Markazi Square, Chaghagolan, Aryashar, Kayhanshahr, Imam Hossein Square, Hekmatabad, Farhangian Phase 2, and Three Roads of Shariati, respectively. As a result, crime in the Kermanshah metropolis has occurred in clusters. In fact, crime hotspots in the Kermanshah metropolis have coincided with informal settlement neighborhoods.
 
Extended Abstract
 
Introduction
Recently, the analysis of the spatial pattern of crime has provided insights into the impacts of violence on cities and their populations, including qualitative interpretations of the perception of violence. In turn, place-based approaches in criminology have evolved, increasingly focusing on smaller spatial scales—from neighborhoods to street networks, segments, and intersections. These features contribute to the spatial modeling of crime. Understanding spatial conditions at these micro-scales can explain crime concentrations, with implications for crime prevention and reduction initiatives. Spatial clustering and crime hotspots have been linked to small-scale spatial characteristics that have been empirically understood through high-resolution analysis. The expansion of poverty and informal settlements has become a growing concern in the metropolis of Kermanshah. Available statistics indicate that there are 36 socially vulnerable areas and 13 informal neighborhoods in Kermanshah, each facing numerous issues and challenges. These neighborhoods include Nowkan, Bagh-e Abrisham, Chegheglan, Chega Kabud, Hekmatabad, Shatarabad, Jafarabad, Anahita, Sadeghiyeh Town, Dowlatabad, Chaman, Darreh Deraz, and Koliabad. The population of these 13 neighborhoods is estimated to be between 250,000 and 300,000 people, which accounts for approximately one-third of the total population of the Kermanshah metropolis. Kermanshah accommodates about 50 percent of the province’s total population of two million, and its municipality is divided into eight administrative districts. At the same time, nearly 300,000 residents live in poor and marginalized neighborhoods, facing a range of social problems such as divorce, suicide, and addiction. Accordingly, the aim of the present study is to conduct a spatial analysis of drug trafficking offenses in the metropolis of Kermanshah.
 
Methodology
This study adopts a descriptive-analytical methodology. To identify and delineate crime hotspots within the city of Kermanshah, statistical and visualization-based analyses were conducted using Geographic Information System (GIS) tools. Drawing on prior research, four established techniques for crime hotspot mapping were referenced and operationalized. Initially, a point pattern map of crime incidents in Kermanshah was developed to explore spatial concentrations of criminal activity. Subsequently, spatial statistical clustering methods—including Moran’s I and Kernel Density Estimation (KDE)—were utilized to evaluate the degree of spatial randomness or clustering in the distribution of crime across the city.
 
Results and Discussion
Crime in the metropolitan area of Kermanshah exhibits a clustered spatial pattern. In fact, crime hotspots in the city largely coincide with informal settlements. This suggests that violence and crime in urban areas do not occur randomly or spontaneously; rather, they are the inevitable outcomes of social exclusion and inequality. Residents of informal settlements are more vulnerable to such risks, particularly the urban poor who reside in violence-prone neighborhoods. Socioeconomic inequalities contribute to the spatial concentration of organized criminal gangs within these informal settlements. The high levels of poverty in such areas—primarily due to unemployment—undermine residents' ability to meet basic needs such as food, healthcare, and education. As a result, slum dwellers often inhabit small, unstable housing units lacking essential services. These conditions, in turn, lead to a greater propensity for social deviance and criminal behavior.
 
Conclusion
The findings indicate that in Kermanshah, the prevalence of inadequate, low-quality housing and insufficient urban infrastructure in informal settlements significantly contributes to the emergence of crime-prone areas. The rapid and unplanned migration to these neighborhoods undermines residents’ sense of place attachment, exacerbating spatial disorder and social instability. These conditions highlight the urgent need for targeted urban policies and strategic interventions. Future research and planning efforts should prioritize spatial crime hotspot analysis, integrate safe city strategies, and assess the effectiveness of law enforcement and municipal services to mitigate crime. Moreover, understanding the multifaceted drivers of crime hotspot formation and enhancing predictive spatial crime models will be essential for sustainable urban crime management in Kermanshah.