Spatial Patterns of Crime and Its Relationship with The Physical Environment: Chicago Case

Year 2023, Volume: 16 Issue: 3, 1597 - 1619, 15.09.2023

Elif Kırpık

https://doi.org/10.35674/kent.1244009

Abstract

Although the environmental criminology, which relates crime to environmental factors and argues that the environment is not a passive determinant of the onset, continuation or termination of crime, has been on the agenda of urban studies, the relationships between elements of the physical environment and crime have not yet been sufficiently studied through exploratory spatial statistics. In the light of crime theories such as Broken Windows Theory, Crime Pattern Theory and Crime Prevention Through Environmental Design approach, this study aims to define and understand crime patterns by producing crime maps, visualizing spatial distributions, and testing the relationship between recurrent crimes in space and physical environmental elements.
With the field study carried out in Chicago, the spatial patterns and relationships between crime types and physical environment elements were analyzed using exploratory spatial statistical methods. All secondary data used in this research are open data and all analyses were carried out using Geographical Information Systems. Exploratory spatial data analyses using GIS are Average Nearest Neighbor, Optimized Hotspot Analysis, Spatial Autocorrelation (Global Moran's I) and Geographically Weighted Regression.
The analyses conducted in this study provided supporting evidence for theories of crime. The findings revealed that crimes tend to occur in close proximity to one another and cluster in specific neighborhoods and regions. This spatial concentration of crime supports the notion that criminals choose their locations intentionally or randomly. Furthermore, the study established a direct relationship between physical environmental elements and crime. Various physical factors such as inadequate street lighting, vacant and abandoned buildings, and sanitation code complaints were found to significantly contribute to the occurrence of crimes. These findings confirm the hypothesis that the deterioration of the physical environment can influence and contribute to increased criminal activity. Overall, the results of this study align with established theories of crime and provide empirical evidence for the significance of the physical environment in shaping criminal behavior.

Keywords

Suç Örüntüsü Teorisi, Rutin Aktiviteler Teorisi, Mekan kriminolojisi, Keşfedici Mekânsal Veri Analizleri, Coğrafi Bilgi Sistemleri, Crime Pattern Theory, Routine Activity Theory, Criminology of place, Exploratory spatial data analysis, Geographic information systems

Suçun Mekânsal Örüntüleri ve Fiziksel Çevreyle İlişkisi: Şikago Örneği

Abstract

Suçu çevresel faktörlerle ilişkilendiren ve çevrenin suçun başlangıcı, devamı veya sona ermesinde aktif bir etken olduğunu savunan çevresel kriminoloji, kent çalışmalarının gündeminde olmasına rağmen, fiziksel çevre unsurları ile suç arasındaki ilişkiler henüz keşfedici mekânsal istatistiki yöntemler aracılığıyla yeterince çalışılmamıştır. Bu çalışma, Kırık Camlar Teorisi, Suç Örüntüleri Teorisi ve Çevresel Tasarım Yoluyla Suç Önleme yaklaşımı gibi suç teorileri ve yaklaşımları ışığında, suçun mekânsal dağılımlarını görselleştirip suç haritaları üretmeyi ve mekânda tekrar eden suçlar ile fiziksel çevre unsurları arasındaki ilişkiyi test ederek suç örüntülerini tanımlamayı ve anlamayı amaçlamaktadır.Şikago'da gerçekleştirilen vaka çalışması ile suç türleri ve fiziksel çevre unsurları arasındaki mekânsal örüntüler ve ilişkiler, keşfedici mekânsal istatistiki yöntemler kullanılarak analiz edilmiştir. Bu araştırmada kullanılan tüm ikincil veriler açık veridir ve tüm analizler Coğrafi Bilgi Sistemleri kullanılarak gerçekleştirilmiştir. Çalışma kapsamında Ortalama En Yakın Komşu Analizi, Optimize Edilmiş Sıcak Nokta Analizi, Mekânsal Otokorelasyon (Global Moran's I) ve Coğrafi Ağırlıklı Regresyon analizleri kullanılmıştır.
Bu çalışmada kullanılan analizler bahsi geçen suç teorilerini ve yaklaşımlarını destekleyici kanıtlar sağlamıştır. Bulgular, suçların birbirine yakın yerlerde meydana gelme eğiliminde olduğunu ve belirli mahalle ve bölgelerde kümelendiğini ortaya koymuştur. Suçun bu mekânsal yoğunlaşması, suçluların yerlerini dağınık seçmek yerine kasıtlı veya rastgele seçtikleri fikrini desteklemektedir. Ayrıca, çalışma fiziksel çevre unsurları ile suç arasında doğrudan bir ilişki kurmuştur. Yetersiz sokak aydınlatması, boş ve terk edilmiş binalar ile temizlik şikayetleri gibi çeşitli fiziksel faktörlerin suçların oluşumuna ve devamlılığına önemli ölçüde katkıda bulunduğu kanıtlanmıştır. Bu bulgular, fiziksel çevrenin bozulmasının suç faaliyetlerinin artmasını etkileyebileceği ve buna katkıda bulunabileceği hipotezini doğrulamaktadır. Genel olarak, bu çalışmanın sonuçları mevcut suç teorileri ve yaklaşımları ile uyumludur ve suç davranışını şekillendirmede fiziksel çevrenin önemine dair ampirik kanıtlar sunmaktadır.

Keywords

Mekan kriminolojisi, Keşfedici Mekânsal Veri Analizleri, Coğrafi Bilgi Sistemleri, Criminology of place, Exploratory spatial data analysis, Geographic information systems, Crime theories, Crime studies, Environmental criminology, Suç teorileri, Suç çalışmaları, Çevresel kriminoloji

References

• Ayhan, İ., & Çubukcu, K. M. (2007). Suç ve kent ilişkisine ampirik bakış: Literatür taraması. Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 5, Article 5.
• Brantingham, P., & Brantingham, P. (2008). Crime pattern theory. In Environmental criminology and crime analysis (pp. 78–93). Willan.
• Campedelli, G. M., Favarin, S., Aziani, A., & Piquero, A. R. (2020). Disentangling community-level changes in crime trends during the COVID-19 pandemic in Chicago. Crime Science, 9(1), 21. https://doi.org/10.1186/s40163-020-00131-8
• Cinar, E. A., & Cubukcu, E. (2012). The Influence of social, economical and physical environmental factors on crime rates: A case study of the Bosphorus Conservation Area, Istanbul, Turkey. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 11(22), Article 22.
• City of Chicago. (2023a, May 30). Crimes—2001 to present. Chicago Data Portal. https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-Present/ijzp-q8t2
• City of Chicago. (2023b, May 31). CHI 311. 311 City Services. https://311.chicago.gov/
• Crowe, T. (2013). Crime prevention through environmental design (L. J. Fennelly, Ed.). Elsevier.
• Dayara, T., Thabtah, F., Abdel-Jaber, H., & Zeidan, S. (2022). Crime analyses using data analytics. International Journal of Data Warehousing and Mining (IJDWM), 18(1), 1–15. https://doi.org/10.4018/IJDWM.299014
• De Nadai, M., Xu, Y., Letouzé, E., González, M. C., & Lepri, B. (2020). Socio-economic, built environment, and mobility conditions associated with crime: A study of multiple cities. Scientific Reports, 10(1), 13871. https://doi.org/10.1038/s41598-020-70808-2
• Eck, J., & Weisburd, D. L. (2015). Crime places in crime theory (SSRN Scholarly Paper No. 2629856).
• Emig, M. N., Heck, R. O., & Kravitz, M. (1980). Crime analysis: A selected bibliography. US National Criminal Justice Reference Service.
• ESRI. (2023, May 30). About ArcGIS. ESRI Mapping & Analytics Software and Services. https://www.esri.com/en-us/arcgis/about-arcgis/overview
• ESRI. (2023, July 14). How Geographically Weighted Regression (GWR) works. ESRI Mapping & Analytics Software and Services. https://pro.arcgis.com/en/pro-app/2.9/tool-reference/spatial-statistics/how-geographicallyweightedregression-works.htm
• Felson, M. (2008). Routine activity approach. In Environmental Criminology and Crime Analysis (pp. 70–77). Willan.
• Felson, M., & Spaeth, J. L. (1978). Community structure and collaborative consumption: A routine activity approach. American Behavioral Scientist, 21(4), 614–624. https://doi.org/10.1177/000276427802100411
• Fotios, S. A., Robbins, C. J., & Farrall, S. (2021). The effect of lighting on crime counts. Energies, 14(14), 4099. https://doi.org/10.3390/en14144099
• Guerry, A. M. (1833). Essai sur la statistique morale de la France.
• Hou, K., Zhang, L., Xu, X., Yang, F., Chen, B., Hu, W., & Shu, R. (2023). High ambient temperatures are associated with urban crime risk in Chicago. The Science of the Total Environment, 856(Pt 1), 158846. https://doi.org/10.1016/j.scitotenv.2022.158846
• Jacobs, J. (1961). The death and life of great American cities (Reissue edition). Random House.
• Jeffery, C. R. (1977). Crime prevention through environmental design. Sage Publications.
• Kondo, M. C., Keene, D., Hohl, B. C., MacDonald, J. M., & Branas, C. C. (2015). A difference-in-differences study of the effects of a new abandoned building remediation strategy on safety. PLOS ONE, 10(7), e0129582. https://doi.org/10.1371/journal.pone.0129582
• Kooi, B. R. (2013). Assessing the correlation between bus stop densities and residential crime typologies. Crime Prevention and Community Safety, 15(2), 81–105. https://doi.org/10.1057/cpcs.2012.15
• Lavi, B., Tokuda, E. K., Moreno-Vera, F., Nonato, L. G., Silva, C. T., & Poco, J. (2022). 17K-Graffiti: spatial and crime data assessments in São Paulo city. Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Volume 4: VISAPP, 968–975. https://doi.org/10.5220/0010883300003124
• Newman, O. (1972). Defensible space; crime prevention through urban design. Macmillan Publishing.
• Quetelet, A. (1831). Recherches sur le penchant au crime aux différents âges. Hayez.
• Safat, W., Asghar, S., & Gillani, S. A. (2021). Empirical analysis for crime prediction and forecasting using machine learning and deep learning techniques. IEEE Access, 9, 70080–70094. https://doi.org/10.1109/ACCESS.2021.3078117
• Schertz, K. E., Saxon, J., Cardenas-Iniguez, C., Bettencourt, L. M. A., Ding, Y., Hoffmann, H., & Berman, M. G. (2021). Neighborhood street activity and greenspace usage uniquely contribute to predicting crime. Npj Urban Sustainability, 1(1), Article 1. https://doi.org/10.1038/s42949-020-00005-7
• Shaw, C. R. (1931). A delinquency area. In The natural history of a delinquent career (pp. 13–25). The University of Chicago Press. https://doi.org/10.1037/13522-002
• Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency and urban areas. University of Chicago Press.
• Sherman, L. W., Gartin, P. R., & Buerger, M. E. (1989). Hot spots of predatory crime: Routine activities and the criminology of place. Criminology, 27(1), 27–56. https://doi.org/10.1111/j.1745-9125.1989.tb00862.x
• Singleton, C. R., Winata, F., Parab, K. V., Adeyemi, O. S., & Aguiñaga, S. (2023). Violent crime, physical inactivity, and obesity: Examining spatial relationships by racial/ethnic composition of community residents. Journal of Urban Health: Bulletin of the New York Academy of Medicine, 100(2), 279–289. https://doi.org/10.1007/s11524-023-00716-z
• Sun, L., Zhang, G., Zhao, D., Ji, L., Gu, H., Sun, L., & Li, X. (2022). Explore the correlation between environmental factors and the spatial distribution of property crime. ISPRS International Journal of Geo-Information, 11(8), 428. https://doi.org/10.3390/ijgi11080428
• Taylor, R. B., & Harrell, A. (1996). Physical environment and crime. U.S. Department of Justice, Office of Justice Programs, National Institute of Justice.
• Wilson, J. Q., & Kelling, G. L. (1982). Broken windows. Atlantic Monthly, 249(3), 29–38.
• Wortley, R., & Mazerolle, L. (Eds.). (2008). Environmental criminology and crime analysis. Willan.
• Yang, M., Chen, Z., Zhou, M., Liang, X., & Bai, Z. (2021). The impact of COVID-19 on crime: A spatial temporal analysis in Chicago. ISPRS International Journal of Geo-Information, 10(3), Article 3. https://doi.org/10.3390/ijgi10030152