Spatial Structure Analysis and Geographic Clustering Of Suicide in Iran at the County Level
Subject Areas : Research on Iranian social issues
zohre shahbazi
1
*
,
Mehdi Mobaraki
2
1 - Assistant Professor, Department of Social Psychology, Institute for Humanities and Social Studies (IHSS) in ACECR, Tehran, Iran.
2 - Assistant Professor, Department of Social Development Research, ACECR, Arak, Iran.
Keywords: Suicide, Spatial Analysis, Geographic Clustering, LISA, Hotspot Analysis, Iran.,
Abstract :
Spatial Structure Analysis and Geographic Clustering
Of Suicide in Iran at the County Level
Zohre Shahbazi*
Mehdi Mobaraki**
Suicide, as a major public health and social concern, is not evenly distributed across geographic space and tends to exhibit distinct spatial patterns. The aim of this study is to examine the spatial structure and identify geographic clustering patterns of suicide rates at the county level in Iran in 2023. This research adopts a quantitative, cross-sectional spatial analysis approach, with counties serving as the unit of analysis. The study covers 450 counties across the country, and the main variable is the suicide rate per 100,000 population. After data cleaning and spatial linkage to county-level geographic boundaries, spatial analyses were conducted. Exploratory Spatial Data Analysis (ESDA) techniques were employed, including Global Moran’s I to assess overall spatial autocorrelation, Local Indicators of Spatial Association (LISA) to identify local clustering patterns, and Getis–Ord Gi* statistics to detect spatial hotspots and cold spots. The results of Global Moran’s I indicate a statistically significant positive spatial autocorrelation in suicide rates across Iranian counties, suggesting a non-random spatial distribution. LISA analysis reveals the presence of distinct high–high and low–low clusters, as well as spatial outliers, highlighting substantial local heterogeneity in suicide rates. In addition, hotspot analysis identifies statistically significant concentrations of high and low suicide rates in specific geographic areas, some of which overlap with the clusters detected by LISA. Overall, the findings demonstrate pronounced spatial inequality and geographic concentration in suicide rates at the county level in Iran, underscoring the importance of incorporating spatial perspectives into suicide research and prevention policies. While the study is descriptive in nature and does not address causal mechanisms, its results provide a spatially explicit foundation for future explanatory studies and regionally targeted public health interventions.
Keywords: Suicide, Spatial Analysis, Geographic Clustering, LISA, Hotspot Analysis, Iran.
Introduction
Over the past three decades, suicide has gradually emerged as one of the major social health challenges in Iran. Although suicide is often examined through national averages and macro-level indicators, growing evidence suggests that it is not distributed evenly across geographical space. Instead, suicide demonstrates clear spatial disparities, with some regions experiencing substantially higher levels of risk than others. In this context, suicide should not be understood solely as an individual act or psychological phenomenon; rather, it is deeply intertwined with structural, economic, social, and spatial conditions.
Contemporary scholars in sociology, social epidemiology, and human geography increasingly emphasizes that space is not merely the setting in which social phenomena occur, but an active dimension in the production and reproduction of inequality and social harm. Factors such as unemployment, poverty, regional deprivation, social disintegration, weakened social capital, migration pressures, and unequal access to opportunities may accumulate geographically and create localized concentrations of social vulnerability. Consequently, the risk of suicide may become spatially clustered rather than randomly dispersed.
International research has repeatedly demonstrated that suicide tends to follow identifiable spatial patterns. Studies conducted in countries such as the United States, China, Japan, South Korea, Canada, and India have shown that suicide rates often exhibit significant spatial autocorrelation and cluster formation. High-risk clusters are frequently associated with economic marginalization, social isolation, unequal development, weak community cohesion, and structural disadvantages.
In Iran, recent empirical studies have similarly highlighted the uneven spatial distribution of suicide. Existing evidence suggests that western provinces and certain northern regions experience relatively higher levels of suicide risk. However, despite these advances, most Iranian studies remain limited to the provincial scale and therefore fail to capture intra-provincial heterogeneity. In addition, many domestic studies rely primarily on descriptive mapping and have not systematically employed advanced spatial statistical techniques to identify statistically significant spatial clusters.
As a result, there is still no comprehensive national-level study that systematically examines the spatial structure of suicide at the county level using standardized spatial analytical methods. This gap is particularly important from a policy perspective. When policy interventions rely solely on national or provincial averages, local disparities and high-risk areas remain hidden, leading to generalized and often ineffective prevention strategies. Spatial analysis, by contrast, can help identify geographically concentrated areas of risk and provide a stronger basis for targeted, place-based interventions.
Accordingly, the present study aims to analyze the spatial structure and geographical clustering of suicide across Iranian counties in 2023. The central research question is whether suicide rates in Iranian counties exhibit significant spatial patterns and whether meaningful high-risk and low-risk clusters can be identified across the national territory?
Theoretical Framework
This study draws upon an integrated theoretical framework combining classical sociology, contemporary social health theories, and social geography. Among classical sociological perspectives, Durkheim’s theory of suicide remains one of the most influential foundations for understanding suicide as a social phenomenon. Durkheim argued that suicide rates are shaped not merely by individual characteristics but by broader patterns of social integration and social regulation.
Contemporary reinterpretations of Durkheim emphasize that these mechanisms also operate spatially. Regions characterized by economic instability, unemployment, social fragmentation, migration pressures, and weakened social institutions may experience higher levels of anomie and social disorganization, thereby increasing vulnerability to suicide. In contrast, areas with stronger social capital, collective efficacy, social trust, and supportive community networks may demonstrate greater resilience and lower suicide rates.
Theories of social geography and spatial justice further argue that space itself is socially produced, and that unequal spatial distribution of resources, services, opportunities, and risks contributes to differentiated social outcomes. From this perspective, spatial concentrations of deprivation and inequality can generate geographically concentrated forms of social harm, including suicide.
The ecological perspective associated with the Chicago School also contributes to this analysis by emphasizing the role of neighborhood transition, population instability, weakened informal control, and erosion of local social networks in generating spatial concentrations of social problems.
Based on this theoretical framework, the present study assumes that suicide is shaped through a multi-level process. At the macro level, structural pressures such as economic inequality and social instability increase the likelihood of self-harming behavior. At the mesa level, forms of social regulation such as social capital, social cohesion, and collective efficacy mediate or intensify these pressures. At the spatial level, geographical concentration of resources and deprivation creates spatial dependency and cluster formation. Therefore, it is expected that suicide rates across Iranian counties will display statistically significant spatial autocorrelation and identifiable geographical clusters.
Methodology
The present study employs quantitative cross-sectional spatial analytical design. Analysis unit consists of 450 counties across Iran in the year 2023. The primary variable examined in this study is the suicide rate per 100,000 population at the county level.
Data on suicide cases were obtained from official institutional sources, while population data were derived from the Statistical Center of Iran. Following data cleaning and harmonization of spatial codes, the statistical data were linked to county-level geographical layers.
To investigate the spatial structure of suicide, a set of exploratory spatial analysis techniques was employed. First, the spatial distribution of suicide rates was examined descriptively through thematic mapping. Subsequently, Global Moran’s I statistics were used to assess the existence of spatial autocorrelation at the national level. This index measures whether similar values tend to cluster geographically.
After confirming the presence of spatial autocorrelation, Local Indicators of Spatial Association (LISA) were applied to identify local spatial clusters and spatial outliers. This technique allows classification of counties into high-high clusters, low-low clusters, high-low outliers, and low-high outliers.
Additionally, the Getis-Ord Gi* statistic was employed to identify statistically significant hot spots and cold spots of suicide rates. All analyses were conducted using a spatial weight matrix based on geographical contiguity. Statistical significance was assessed at the 0.05 level. Spatial analyses and data processing were carried out using GeoDa software.
Findings
Descriptive findings revealed substantial variation in suicide rates across Iranian counties. The mean suicide rate was 0.69 per 100,000 population, while the maximum recorded rate reached 3.03. Several counties reported zero cases, which may partly reflect small population sizes or differences in reporting mechanisms.
Initial examination of the spatial distribution map demonstrated that suicide rates were not evenly distributed across the country. Certain areas exhibited noticeably higher or lower rates than the national average, suggesting potential spatial clustering.
Results of the Global Moran’s I analysis confirmed the existence of statistically significant positive spatial autocorrelation. The Moran’s I value was 0.3269, with a Z-score of 11.18 and a significance level below 0.01. These findings indicate that counties with similar suicide rates tend to be geographically adjacent and that the spatial distribution of suicide in Iran is non-random.
The LISA analysis further revealed distinct local clustering patterns. A total of 46 counties were identified as high-high clusters, meaning that counties with high suicide rates were surrounded by neighboring counties with similarly high rates. These high-risk clusters were concentrated primarily in western and southwestern regions of Iran. Counties located in provinces such as Ilam, Kermanshah, Lorestan, Khuzestan, and Kohgiluyeh and Boyer-Ahmad formed major parts of these high-risk spatial clusters.
In contrast, 62 counties were identified as low-low clusters, indicating concentrations of low suicide rates surrounded by similarly low-risk neighboring areas. These low-risk clusters were primarily located in eastern, southeastern, and parts of northern Iran, including regions within Sistan and Baluchistan, Razavi Khorasan, Golestan, and the Caspian coastal areas.
The analysis also identified spatial outliers. High-low counties exhibited high suicide rates despite being located among lower-risk neighboring counties, while low-high counties displayed the opposite pattern. These spatial anomalies suggest that local contextual factors may produce distinct patterns even within broader high-risk or low-risk regions.
Results of the Getis-Ord Gi* hot spot analysis largely confirmed the findings of the LISA analysis. Significant hot spots were concentrated in western and southwestern Iran, while cold spots were predominantly observed in eastern and southeastern regions. The considerable overlap between high-high clusters and hot spots indicates relative stability and persistence of spatial concentration patterns.
Discussion and Conclusion
The findings of this study demonstrate that suicide in Iran follows a clear and statistically significant spatial structure. The existence of positive spatial autocorrelation and the identification of high-risk and low-risk clusters indicate that geographical context plays a substantial role in shaping suicide patterns.
From a theoretical perspective, the concentration of high-risk clusters in specific regions can be interpreted through concepts such as social anomie, structural deprivation, weakened social integration, and spatial concentration of inequality.
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* Corresponding Author: Assistant Professor, Department of Social Psychology, Institute for Humanities and Social Studies (IHSS) in ACECR, Tehran, Iran.
https://orcid.org/0009-0001-2637-652x
Shahbazi@acecr.ac.ir
** Assistant Professor, Department of Social Development Research, ACECR, Arak, Iran.
https://orcid.org/0000-0003-0515-7114
Mobaraki@acecr.ac.ir
جهانی دولت¬آباد، اسماعیل و اسماعیل، عشایری، طاها، محمد شیری (1402) «بررسی رابطۀ شاخصهای کلان اقتصادی با نرخ خودکشی در استانهای کشور: تحلیل ثانویۀ آمارهای 1390-1400»، پژوهشهای راهبردی مسائل اجتماعی، سال دوازدهم، شماره 3، صص 41-62.
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Berkman, L. F., Glass, T., Brissette, I., & Seeman, T. E. (2000) From social integration to health: Durkheim in the new millennium. Social Science & Medicine, 51(6), 843–857.
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Rostami, M., Ja lilian, A., Mahdavi, S. A., & Bagheri, N. (2021) Spatial heterogeneity in gender and age of fatal suicide in Iran. J Res Health Sci, 22(1), 1–8. https://doi.org/10.34172/jrhs.2022.76.
Rouzrokh, P., Abbasi Feijani, F., & Moshiri, Y. (2025) The Pooled Prevalence of Attributed Factors of Suicide in Iran: A Systematic Review and Meta-analysis. Arch Iran Med, 44–60. https://doi.org/10.34172/aim.31276.
Sampson, R. J. (2012) Great American City: Chicago and the Enduring Neighborhood Effect. University of Chicago Press. https://doi.org/10.7208/chicago/9780226734569.001.0001.
Soja, E. W. (2010) Seeking spatial justice. in Neapolis: University of Minnesota Press. 978-0-8166-6674-9.
Von Hoene, E., Roess, A., Leah, M. A., Yang, R., & Anderson, T. (2025). Identifying Geographic Disparities in Suicide Determinants Across United States Counties with Spatial Modeling. Proceedings of the International Cartographic Association, 7(14), 1–8. https://doi.org/10.5194/ica-proc-6-146-2025.
Wang, Y., Wang, L., Li, Z., et al. (2021).Spatiotemporal clustering of suicide mortality in mainland China, 2013–2018. BMC Public Health https://doi.org/10.1186/s12889-021-11293-7.
WHO (2021) LIVE LIFE: An implementation guide for suicide prevention in countries. https://www.who.int/publications/i/item/9789240026629.
Yang, Xie, Zhang Jie & Chen Xiao (2022) The identification, logic and enlightenments of intra-urban place communities in China, Scientific Reports,12(247), https://doi.org/10.1038/s41598-021-03917-1.
Yoshioka, E., Sharon, J. B. H., Yukihiro, S., & Yasuaki, S. (2020) Geography of suicide in Japan: Spatial patterning and rural–urban differences. Social Psychiatry and Psychiatric Epidemiology, 56, 731–746. https://doi.org/10.1007/s00127-020-01872-6.
Zangeneh, A., Khademi, N., & Farahmand Moghadam, N. (2023) Spatiotemporal clustering of suicide attempt in Kermanshah, West-Iran. Public Mental Health, 14. https://doi.org/10.3389/fpsyt.2023.1174071.