Research Article
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Analysis and visualization of crime data using GIS technology: Understanding crime patterns and distribution

Year 2023, Volume: 10 Issue: 2, 151 - 163, 01.11.2023
https://doi.org/10.9733/JGG.2023R0011.E

Abstract

Crime mapping is an important method for identifying crime patterns. Crime maps are widely used to visualize spatial distribution of crime and allocating security resources. A qualified cartographic representation is essential for the presentation of the results of crime analysis, so the preparation and enrichment of crime maps requires careful obedience to cartographic principles. This article presents a combined comprehensive and understandable mapping methods and techniques for crime analysis and crime mapping for Trabzon, Türkiye. Prior to the analysis and mapping process, crime data recorded between 2011 and 2015 was reclassified. In the next stage, editing of erroneous data, standardization and geocoding processes were applied. The spatial distribution of all crimes was analyzed and mapped with the Kernel Density Estimation method. The Hexagon grid mapping technique and Hotspot method were used for visualization of spatial data and temporal trends of criminal activity. The spatial and temporal distribution of burglary and assault crimes (the most common crimes of all crimes) was mapped with these techniques. Maps also provide detailed information on crime patterns for study area, and help the police department to develop safe city strategies and reduce crime activities.

References

  • Aliagaoglu, A., & Cildam, S. Y. (2016). Bandırma Şehrinde Suçlar (2006-2008): Coğrafi Bir Yaklaşım. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 19(35).
  • Beiji, Z., Mohammed, N., Chengzhang, Z., & Rongchang, Z. (2017). Crime hotspot detection and monitoring using video based event modeling and mapping techniques. International Journal of Computational Intelligence Systems, 10(1), 962.
  • Boba, R. L. (2005). Crime Analysis and Crime Mapping. Thousand Oaks, California, Sage Publications.
  • Chainey, S., & Ratcliffe, J. (2005). GIS and Crime Mapping. Chichester, John Wiley & Sons Inc.
  • Chainey, S., Tompson, L., & Uhlig, S. (2008). The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21(1), 4–28.
  • Eck, J., Chainey, S., Cameron, J., Leitner, M., & Wilson, R. (2005). Mapping Crime: Understanding Hot spots. Washington, DC: U.S. Department of Justice, National Institute of Justice.
  • Eken, S., & Kumru, P. (2014). Haritalar Üzerinde Suç Verilerinin Görüntülenmesi ve Analizinin Sağlanması: Kocaeli İli Örneği. KMÜ Sosyal ve Ekonomı̇k Araştırmalar Dergı̇si, 16(1), 67-72.
  • Ejemeyovwi, D. O. (2015). Crime mapping using time series analysis in Asaba, Delta State, Nigeria: a remote sensing and GIS approach. European Journal of Basic and Applied Sciences, 2(2), 52-71, ISSN: 2059-3058.
  • Feng, J., Dong, P., & Song, Y. (2016). A Spatio-Temporal Analysis of Urban Crime in Beijing: Based on Data for Property Crime. Urban Studies, 53(15), 3223-3245.
  • Gahlin, C., & Johansson, E. (2014). Crime Hotspots: An Evaluation of the KDE Spatial Mapping Technique (Thesis). Blekinge Institute of Technology, Faculty of Computing, Department of Computer Sciemce & Engineering, Karlskrona, Sweden.
  • Gayır, B., & Arslan, O. (2018). Orman Yangınlarının CBS Tabanlı Konumsal İstatistik Analizi: 2011-2015 Yılları Arasında Muğla Orman Bölge Sınırları İçerisinde Çıkan Yangınlar. Anadolu Orman Araştırmaları Dergisi, 4(1): 44-60.
  • Gerber, M. S. (2014). Predicting crime using Twitter and kernel density estimation. Decision Support Systems, 61, 115-125.
  • Glasner, P., & Leitner, M. (2017). Evaluating the Impact the Weekday Has on Near-Repeat Victimization: A Spatio-Temporal Analysis of Street Robberies in the City of Vienna, Austria. ISPRS International Journal of Geo-Information, 6(3).
  • Gupta, R. Rajitha, K., Basu, S., & Mittal, S. K. (2012). Application of GIS in Crime Analysis: A Gateway to Safe City. Proceedings of the 14th Annual International Conference and Exhibition on Geospatial Information Technology and Applications, India.
  • Gurbuz, M., & Karabulut, M. (2007). Adana Beş Ocak Polis Karakolu Sorumluluk Bölgesinde Çocuk Suçlarının Coğrafi Bilgi Sistemleri ile Haritalandırılması ve Analizi. Çukurova Üniversitesi Sosyal Bilimler Dergisi, 16(1), 331-346.
  • Gurbuz, M., Karabulut, M., & Temir, Ö. (2013). Kayseri’de Oto ve Otodan Hırsızlık Suçlarının CBS ile Haritalandırılması ve Analizi. KSÜ Sosyal Bilimler Dergisi, 10(1).
  • Gwinn, S. L., Bruce, C., Cooper J. P., & Hick, S. (2008). Exploring Crime Analysis: Readings on Essential Skills (2nd ed). Overland Park, KS: International Association of Crime Analysts.
  • Hajela, G., Chawla, M., & Rasool, A. (2021). Crime hotspot prediction based on dynamic spatial analysis. Electronics and Telecommunications Research Institute (ETRI), 43(6), 1058-1080.
  • Hart, T. C., & Zandbergen, P. A. (2014). Kernel density estimation and hotspot mapping: Examining the influence of interpolation method, grid cell size, and bandwidth on crime forecasting, Policing An International Journal of Police Strategies and Management, 37(2).
  • Hirschfield, A., & Bowers, K. (2001). Mapping and analysing crime data. London and New York, Taylor & Francis.
  • Hu, Y., Miller, H. J., & Li, X. (2014). Detecting and analyzing mobility hotspots using surface networks. Transactions in GIS, 18(6), 911–935.
  • Hu, Y., Wang, F., Guinc, C., & Zhub, H. (2018). A spatio-temporal kernel density estimation framework for predictive crime hotspot mapping and evaluation. Applied Geography, 99, 89–97.
  • International Association of Crime Analysts (IACA) Standards, Methods, & Technology (SMT) Committee. (2013). Identifying High Crime Areas (White Paper 2013-02), Overland Park, KS: Author.
  • Kalinic, M., & Krisp, J. M. (2018). Kernel Density Estimation (KDE) vs. Hot-Spot Analysis - Detecting Criminal Hot Spots in the City of San Francisco. Proceedings of the 21st Conference on Geo-information Science, Lund, Sweden.
  • Kumar, G. R. P., & Somashekar, R. K. (2012). GIS Based Crime Mapping and Analysis: A Case Study of Mudugiri Town Police Station Jurisdiction, Tumkur District, Karnataka, India. GIS crime mapping and analysis, 1(1).
  • McCullagh, M. J. (2006). Detecting Hot spots in Time and Space. ISG06.de Smith MJ.
  • Mohler, G. (2014). Marked point process hot spot maps for homicide and gun crime prediction in Chicago. International Journal of Forecasting, 30, 491-497.
  • Paynich, R., & Hill, B. (2010). Fundamentals of Crime Mapping. Sudbury, MA: Jones and Barlett.
  • Pobuda, M. (2018). Analyze Crime Using Statistics and the R-ArcGIS Bridge.
  • https://learn.arcgis.com/en/projects/analyze-crime-using-statistics-and-the-r-arcgis-bridge/lessons/install-the-r-arcgis-bridge-and-start-statistical-analysis.htm (Accessed: 25 October 2021).
  • Ratcliffe, J. H. (2004). The hotspot matrix: A framework for the spatio‐temporal targeting of crime reduction. Police practice and research, 5(1), 5-23.
  • Turkish Statistical Institute (TUİK) (2021). Statistics Data Portal.
  • Wang, X., Gerber, M. S., & Brown, D. E. (2012). Automatic crime prediction using events extracted from twitter posts. In International conference on social computing, behavioral-cultural modeling, and prediction (pp. 231-238), Berlin, Heidelberg: Springer Berlin Heidelberg.
  • Wolff, M., & Asche, H. (2019). A 3D Geovisualization Approach to Crime Mapping. Proceedings of the 24th International Cartographic Conference, Santiago, Chile.

Suç olaylarının CBS ile analizi ve görselleştirilmesi: Suçun kalıplarını ve dağılımını anlamak

Year 2023, Volume: 10 Issue: 2, 151 - 163, 01.11.2023
https://doi.org/10.9733/JGG.2023R0011.E

Abstract

Suç haritaları suçun mekânsal dağılımını görselleştirmek ve güvenlik kaynaklarını yönlendirmek için yaygın olarak kullanılmaktadır. Suç haritalama, suç olaylarının kalıplarının belirlenmesinde yaygın olarak kullanılan bir yöntemdir. Suç analiz sonuçlarının sunumu için nitelikli kartografik gösterimler gerekmektedir. Bu yüzden suç haritalarının zenginleştirilmesi ve hazırlanması sürecinde kartografik ilkelere uyulmalıdır. Bu çalışma, Trabzon ilinde suçların analizi ve haritalanması için bütünleşik, kapsamlı ve anlaşılır bir haritalama yöntemi ve tekniği sunmaktadır. Çalışma kapsamında öncelikli olarak 2011-2015 yılları arasında kayıt altına alınan suç verileri yeniden sınıflandırılmıştır. Bir sonraki aşamada suç verilerindeki hatalı veriler düzeltilmiş, veri standardizasyonu sağlanmış ve coğrafi kodlama işlemi uygulanmıştır. Suç olaylarının mekânsal dağılımı “Çekirdek Yoğunluk Tahmini” yöntemiyle analiz edilmiş ve haritalanmıştır. Suç olaylarının mekânsal ve zamansal eğilimleri görselleştirilmesi için ise “Altıgen Izgara Haritalama Tekniği” ve “Sıcak Nokta Yöntemi” kullanılmıştır. Bu teknikler ile hırsızlık ve şiddet suçlarının mekânsal ve zamansal dağılımı haritalanmıştır. Hazırlanan haritalar, çalışma alanındaki suç kalıpları hakkında ayrıntılı bilgi sağlamaktadır. Ayrıca emniyet birimleri tarafından güvenli şehir stratejileri geliştirmesine ve suç olaylarının azaltılmasına yardımcı olmaktadır.

References

  • Aliagaoglu, A., & Cildam, S. Y. (2016). Bandırma Şehrinde Suçlar (2006-2008): Coğrafi Bir Yaklaşım. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 19(35).
  • Beiji, Z., Mohammed, N., Chengzhang, Z., & Rongchang, Z. (2017). Crime hotspot detection and monitoring using video based event modeling and mapping techniques. International Journal of Computational Intelligence Systems, 10(1), 962.
  • Boba, R. L. (2005). Crime Analysis and Crime Mapping. Thousand Oaks, California, Sage Publications.
  • Chainey, S., & Ratcliffe, J. (2005). GIS and Crime Mapping. Chichester, John Wiley & Sons Inc.
  • Chainey, S., Tompson, L., & Uhlig, S. (2008). The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21(1), 4–28.
  • Eck, J., Chainey, S., Cameron, J., Leitner, M., & Wilson, R. (2005). Mapping Crime: Understanding Hot spots. Washington, DC: U.S. Department of Justice, National Institute of Justice.
  • Eken, S., & Kumru, P. (2014). Haritalar Üzerinde Suç Verilerinin Görüntülenmesi ve Analizinin Sağlanması: Kocaeli İli Örneği. KMÜ Sosyal ve Ekonomı̇k Araştırmalar Dergı̇si, 16(1), 67-72.
  • Ejemeyovwi, D. O. (2015). Crime mapping using time series analysis in Asaba, Delta State, Nigeria: a remote sensing and GIS approach. European Journal of Basic and Applied Sciences, 2(2), 52-71, ISSN: 2059-3058.
  • Feng, J., Dong, P., & Song, Y. (2016). A Spatio-Temporal Analysis of Urban Crime in Beijing: Based on Data for Property Crime. Urban Studies, 53(15), 3223-3245.
  • Gahlin, C., & Johansson, E. (2014). Crime Hotspots: An Evaluation of the KDE Spatial Mapping Technique (Thesis). Blekinge Institute of Technology, Faculty of Computing, Department of Computer Sciemce & Engineering, Karlskrona, Sweden.
  • Gayır, B., & Arslan, O. (2018). Orman Yangınlarının CBS Tabanlı Konumsal İstatistik Analizi: 2011-2015 Yılları Arasında Muğla Orman Bölge Sınırları İçerisinde Çıkan Yangınlar. Anadolu Orman Araştırmaları Dergisi, 4(1): 44-60.
  • Gerber, M. S. (2014). Predicting crime using Twitter and kernel density estimation. Decision Support Systems, 61, 115-125.
  • Glasner, P., & Leitner, M. (2017). Evaluating the Impact the Weekday Has on Near-Repeat Victimization: A Spatio-Temporal Analysis of Street Robberies in the City of Vienna, Austria. ISPRS International Journal of Geo-Information, 6(3).
  • Gupta, R. Rajitha, K., Basu, S., & Mittal, S. K. (2012). Application of GIS in Crime Analysis: A Gateway to Safe City. Proceedings of the 14th Annual International Conference and Exhibition on Geospatial Information Technology and Applications, India.
  • Gurbuz, M., & Karabulut, M. (2007). Adana Beş Ocak Polis Karakolu Sorumluluk Bölgesinde Çocuk Suçlarının Coğrafi Bilgi Sistemleri ile Haritalandırılması ve Analizi. Çukurova Üniversitesi Sosyal Bilimler Dergisi, 16(1), 331-346.
  • Gurbuz, M., Karabulut, M., & Temir, Ö. (2013). Kayseri’de Oto ve Otodan Hırsızlık Suçlarının CBS ile Haritalandırılması ve Analizi. KSÜ Sosyal Bilimler Dergisi, 10(1).
  • Gwinn, S. L., Bruce, C., Cooper J. P., & Hick, S. (2008). Exploring Crime Analysis: Readings on Essential Skills (2nd ed). Overland Park, KS: International Association of Crime Analysts.
  • Hajela, G., Chawla, M., & Rasool, A. (2021). Crime hotspot prediction based on dynamic spatial analysis. Electronics and Telecommunications Research Institute (ETRI), 43(6), 1058-1080.
  • Hart, T. C., & Zandbergen, P. A. (2014). Kernel density estimation and hotspot mapping: Examining the influence of interpolation method, grid cell size, and bandwidth on crime forecasting, Policing An International Journal of Police Strategies and Management, 37(2).
  • Hirschfield, A., & Bowers, K. (2001). Mapping and analysing crime data. London and New York, Taylor & Francis.
  • Hu, Y., Miller, H. J., & Li, X. (2014). Detecting and analyzing mobility hotspots using surface networks. Transactions in GIS, 18(6), 911–935.
  • Hu, Y., Wang, F., Guinc, C., & Zhub, H. (2018). A spatio-temporal kernel density estimation framework for predictive crime hotspot mapping and evaluation. Applied Geography, 99, 89–97.
  • International Association of Crime Analysts (IACA) Standards, Methods, & Technology (SMT) Committee. (2013). Identifying High Crime Areas (White Paper 2013-02), Overland Park, KS: Author.
  • Kalinic, M., & Krisp, J. M. (2018). Kernel Density Estimation (KDE) vs. Hot-Spot Analysis - Detecting Criminal Hot Spots in the City of San Francisco. Proceedings of the 21st Conference on Geo-information Science, Lund, Sweden.
  • Kumar, G. R. P., & Somashekar, R. K. (2012). GIS Based Crime Mapping and Analysis: A Case Study of Mudugiri Town Police Station Jurisdiction, Tumkur District, Karnataka, India. GIS crime mapping and analysis, 1(1).
  • McCullagh, M. J. (2006). Detecting Hot spots in Time and Space. ISG06.de Smith MJ.
  • Mohler, G. (2014). Marked point process hot spot maps for homicide and gun crime prediction in Chicago. International Journal of Forecasting, 30, 491-497.
  • Paynich, R., & Hill, B. (2010). Fundamentals of Crime Mapping. Sudbury, MA: Jones and Barlett.
  • Pobuda, M. (2018). Analyze Crime Using Statistics and the R-ArcGIS Bridge.
  • https://learn.arcgis.com/en/projects/analyze-crime-using-statistics-and-the-r-arcgis-bridge/lessons/install-the-r-arcgis-bridge-and-start-statistical-analysis.htm (Accessed: 25 October 2021).
  • Ratcliffe, J. H. (2004). The hotspot matrix: A framework for the spatio‐temporal targeting of crime reduction. Police practice and research, 5(1), 5-23.
  • Turkish Statistical Institute (TUİK) (2021). Statistics Data Portal.
  • Wang, X., Gerber, M. S., & Brown, D. E. (2012). Automatic crime prediction using events extracted from twitter posts. In International conference on social computing, behavioral-cultural modeling, and prediction (pp. 231-238), Berlin, Heidelberg: Springer Berlin Heidelberg.
  • Wolff, M., & Asche, H. (2019). A 3D Geovisualization Approach to Crime Mapping. Proceedings of the 24th International Cartographic Conference, Santiago, Chile.
There are 34 citations in total.

Details

Primary Language English
Subjects Geospatial Information Systems and Geospatial Data Modelling
Journal Section Research Article
Authors

Gamze Bediroğlu 0000-0003-2755-3206

H. Ebru Çolak 0000-0002-3000-1704

Publication Date November 1, 2023
Submission Date May 22, 2023
Published in Issue Year 2023 Volume: 10 Issue: 2

Cite

APA Bediroğlu, G., & Çolak, H. E. (2023). Analysis and visualization of crime data using GIS technology: Understanding crime patterns and distribution. Jeodezi Ve Jeoinformasyon Dergisi, 10(2), 151-163. https://doi.org/10.9733/JGG.2023R0011.E
AMA Bediroğlu G, Çolak HE. Analysis and visualization of crime data using GIS technology: Understanding crime patterns and distribution. hkmojjd. November 2023;10(2):151-163. doi:10.9733/JGG.2023R0011.E
Chicago Bediroğlu, Gamze, and H. Ebru Çolak. “Analysis and Visualization of Crime Data Using GIS Technology: Understanding Crime Patterns and Distribution”. Jeodezi Ve Jeoinformasyon Dergisi 10, no. 2 (November 2023): 151-63. https://doi.org/10.9733/JGG.2023R0011.E.
EndNote Bediroğlu G, Çolak HE (November 1, 2023) Analysis and visualization of crime data using GIS technology: Understanding crime patterns and distribution. Jeodezi ve Jeoinformasyon Dergisi 10 2 151–163.
IEEE G. Bediroğlu and H. E. Çolak, “Analysis and visualization of crime data using GIS technology: Understanding crime patterns and distribution”, hkmojjd, vol. 10, no. 2, pp. 151–163, 2023, doi: 10.9733/JGG.2023R0011.E.
ISNAD Bediroğlu, Gamze - Çolak, H. Ebru. “Analysis and Visualization of Crime Data Using GIS Technology: Understanding Crime Patterns and Distribution”. Jeodezi ve Jeoinformasyon Dergisi 10/2 (November 2023), 151-163. https://doi.org/10.9733/JGG.2023R0011.E.
JAMA Bediroğlu G, Çolak HE. Analysis and visualization of crime data using GIS technology: Understanding crime patterns and distribution. hkmojjd. 2023;10:151–163.
MLA Bediroğlu, Gamze and H. Ebru Çolak. “Analysis and Visualization of Crime Data Using GIS Technology: Understanding Crime Patterns and Distribution”. Jeodezi Ve Jeoinformasyon Dergisi, vol. 10, no. 2, 2023, pp. 151-63, doi:10.9733/JGG.2023R0011.E.
Vancouver Bediroğlu G, Çolak HE. Analysis and visualization of crime data using GIS technology: Understanding crime patterns and distribution. hkmojjd. 2023;10(2):151-63.