IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i21p14350-d961387.html
   My bibliography  Save this article

Comparative Study of Approaches for Detecting Crime Hotspots with Considering Concentration and Shape Characteristics

Author

Listed:
  • Zhanjun He

    (School of Computer Science, China University of Geosciences, Wuhan 430074, China
    Artificial Intelligence School, Wuchang University of Technology, Wuhan 430223, China
    State Key Laboratory of Geo-Information Engineering, Xi’an 710054, China)

  • Rongqi Lai

    (School of Computer Science, China University of Geosciences, Wuhan 430074, China)

  • Zhipeng Wang

    (School of Computer Science, China University of Geosciences, Wuhan 430074, China)

  • Huimin Liu

    (Department of Geographic Information, Central South University, Changsha 410083, China)

  • Min Deng

    (Department of Geographic Information, Central South University, Changsha 410083, China)

Abstract

Hotspot detection is an important exploratory technique to identify areas with high concentrations of crime and help deploy crime-reduction resources. Although a variety of methods have been developed to detect crime hotspots, few studies have systematically evaluated the performance of various methods, especially in terms of the ability to detect complex-shaped crime hotspots. Therefore, in this study, a comparative study of hotspot detection approaches while simultaneously considering the concentration and shape characteristics was conducted. Firstly, we established a framework for quantitatively evaluating the performance of hotspot detection for cases with or without the ”ground truth”. Secondly, accounting for the concentration and shape characteristics of the hotspot, we additionally defined two evaluation indicators, which can be used as a supplement to existing evaluation indicators. Finally, four classical hotspot-detection methods were quantitatively compared on the synthetic and real crime data. Results show that the proposed evaluation framework and indicators can describe the size, concentration and shape characteristics of the detected hotspots, thus supporting the quantitative comparison of different methods. From the selected methods, the AMOEBA (A Multidirectional Optimal Ecotope-Based Algorithm) method was more accurate in describing the concentration and shape characteristics and was powerful in discovering complex hotspots.

Suggested Citation

  • Zhanjun He & Rongqi Lai & Zhipeng Wang & Huimin Liu & Min Deng, 2022. "Comparative Study of Approaches for Detecting Crime Hotspots with Considering Concentration and Shape Characteristics," IJERPH, MDPI, vol. 19(21), pages 1-16, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14350-:d:961387
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/21/14350/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/21/14350/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xie, Zhixiao & Yan, Jun, 2013. "Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach," Journal of Transport Geography, Elsevier, vol. 31(C), pages 64-71.
    2. Lan Huang & Martin Kulldorff & David Gregorio, 2007. "A Spatial Scan Statistic for Survival Data," Biometrics, The International Biometric Society, vol. 63(1), pages 109-118, March.
    3. Rashidi, Parinaz & Wang, Tiejun & Skidmore, Andrew & Vrieling, Anton & Darvishzadeh, Roshanak & Toxopeus, Bert & Ngene, Shadrack & Omondi, Patrick, 2015. "Spatial and spatiotemporal clustering methods for detecting elephant poaching hotspots," Ecological Modelling, Elsevier, vol. 297(C), pages 180-186.
    4. Arthur Getis & J. Keith Ord, 2010. "The Analysis of Spatial Association by Use of Distance Statistics," Advances in Spatial Science, in: Luc Anselin & Sergio J. Rey (ed.), Perspectives on Spatial Data Analysis, chapter 0, pages 127-145, Springer.
    5. Juan Duque & Jared Aldstadt & Ermilson Velasquez & Jose Franco & Alejandro Betancourt, 2011. "A computationally efficient method for delineating irregularly shaped spatial clusters," Journal of Geographical Systems, Springer, vol. 13(4), pages 355-372, December.
    6. David Weisburd & John E. Eck, 2004. "What Can Police Do to Reduce Crime, Disorder, and Fear?," The ANNALS of the American Academy of Political and Social Science, , vol. 593(1), pages 42-65, May.
    7. Kulldorff, Martin & Tango, Toshiro & Park, Peter J., 2003. "Power comparisons for disease clustering tests," Computational Statistics & Data Analysis, Elsevier, vol. 42(4), pages 665-684, April.
    8. Julian Besag & James Newell, 1991. "The Detection of Clusters in Rare Diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 154(1), pages 143-155, January.
    9. Malleson, Nick & Andresen, Martin A., 2016. "Exploring the impact of ambient population measures on London crime hotspots," Journal of Criminal Justice, Elsevier, vol. 46(C), pages 52-63.
    10. Tony H. Grubesic & Ran Wei & Alan T. Murray, 2014. "Spatial Clustering Overview and Comparison: Accuracy, Sensitivity, and Computational Expense," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 104(6), pages 1134-1156, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. HAEDO, Christian & MOUCHART , Michel & ,, 2013. "Specialized agglomerations with areal data: model and detection," LIDAM Discussion Papers CORE 2013060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Wan, You & Pei, Tao & Zhou, Chenghu & Jiang, Yong & Qu, Chenxu & Qiao, Youlin, 2012. "ACOMCD: A multiple cluster detection algorithm based on the spatial scan statistic and ant colony optimization," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 283-296.
    3. Silva, Ivair R. & Duczmal, Luiz & Kulldorff, Martin, 2021. "Confidence intervals for spatial scan statistic," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    4. Tonglin Zhang & Ge Lin, 2008. "Identification of local clusters for count data: a model-based Moran's I test," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(3), pages 293-306.
    5. Wei Wang & Sheng Li & Tao Zhang & Fei Yin & Yue Ma, 2023. "Detecting the spatial clustering of exposure–response relationships with estimation error: a novel spatial scan statistic," Biometrics, The International Biometric Society, vol. 79(4), pages 3522-3532, December.
    6. Costa, Marcelo Azevedo & Assunção, Renato Martins & Kulldorff, Martin, 2012. "Constrained spanning tree algorithms for irregularly-shaped spatial clustering," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1771-1783.
    7. Mohammad Meysami & Joshua P. French & Ettie M. Lipner, 2023. "Flexible-Elliptical Spatial Scan Method," Mathematics, MDPI, vol. 11(17), pages 1-22, August.
    8. Porter, Michael D. & Brown, Donald E., 2007. "Detecting local regions of change in high-dimensional criminal or terrorist point processes," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2753-2768, February.
    9. Shino Shiode & Narushige Shiode, 2022. "Network-Based Space-Time Scan Statistics for Detecting Micro-Scale Hotspots," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    10. Demattei[diaeresis], Christophe & Molinari, Nicolas & Daures, Jean-Pierre, 2007. "Arbitrarily shaped multiple spatial cluster detection for case event data," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3931-3945, May.
    11. Trisalyn Nelson & Barry Boots, 2005. "Identifying insect infestation hot spots: an approach using conditional spatial randomization," Journal of Geographical Systems, Springer, vol. 7(3), pages 291-311, December.
    12. Ning Zhang & Ying Mao, 2021. "Spatial Effects of Environmental Pollution on Healthcare Services: Evidence from China," IJERPH, MDPI, vol. 18(4), pages 1-21, February.
    13. Seungwoo Han, 2022. "Spatial stratification and socio-spatial inequalities: the case of Seoul and Busan in South Korea," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-14, December.
    14. Mehmet Ronael & Tüzin Baycan, 2022. "Place-based factors affecting COVID-19 incidences in Turkey," Asia-Pacific Journal of Regional Science, Springer, vol. 6(3), pages 1053-1086, October.
    15. Thomas M. Koutsos & Georgios C. Menexes & Andreas P. Mamolos, 2021. "The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    16. Cláudia M. Viana & Dulce Freire & Patrícia Abrantes & Jorge Rocha, 2021. "Evolution of Agricultural Production in Portugal during 1850–2018: A Geographical and Historical Perspective," Land, MDPI, vol. 10(8), pages 1-18, July.
    17. Gainbi Park & Zengwang Xu, 2022. "The constituent components and local indicator variables of social vulnerability index," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 95-120, January.
    18. Felipe Santos‐Marquez & Carlos Mendez, 2021. "Regional convergence, spatial scale, and spatial dependence: Evidence from homicides and personal injuries in Colombia 2010–2018," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(4), pages 1162-1184, August.
    19. Jianwei Qi & Yayan Lu & Fang Han & Xuankai Ma & Zhaoping Yang, 2022. "Spatial Distribution Characteristics of the Rural Tourism Villages in the Qinghai-Tibetan Plateau and Its Influencing Factors," IJERPH, MDPI, vol. 19(15), pages 1-21, July.
    20. Yaxin Fan & Xinyan Zhu & Bing She & Wei Guo & Tao Guo, 2018. "Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14350-:d:961387. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.