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Spatial clustering of events on a network

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  • STEENBERGHEN, Thérèse
  • AERTS, Koen
  • THOMAS, Isabelle

Abstract

In this paper a methodology is proposed to compute spatial concentrations of point-based events on a network. The distance along the network is used as a measure of the spatial closeness of events. The network is divided into statistical units, based on a random distribution of points of measurement and corresponding network segments, which are the statistical units of reference. For each segment a dangerousness index is computed which indicates the distance-weighted number of traffic accidents in the neighbourhood. The statistical significance of clusters of accidents is tested using a Monte Carlo simulation. The methodology is applied to traffic accidents to detect dangerous locations on the road network of the city of Brussels in Belgium.
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Suggested Citation

  • STEENBERGHEN, Thérèse & AERTS, Koen & THOMAS, Isabelle, 2010. "Spatial clustering of events on a network," LIDAM Reprints CORE 2237, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:2237
    Note: In : Journal of Transport Geography, 18(3), 411-418, 2010
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    References listed on IDEAS

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    1. François Riguelle & Isabelle Thomas & Ann Verhetsel, 2007. "Measuring urban polycentrism: a European case study and its implications," Journal of Economic Geography, Oxford University Press, vol. 7(2), pages 193-215, March.
    2. Claire Dujardin & Isabelle Thomas & Henry Tulkens, 2007. "Quelles frontières pour Bruxelles ? Une mise à jour," Reflets et perspectives de la vie économique, De Boeck Université, vol. 0(2), pages 155-176.
    3. Atsuyuki Okabe & Toshiaki Satoh, 2006. "Uniform network transformation for points pattern analysis on a non-uniform network," Journal of Geographical Systems, Springer, vol. 8(1), pages 25-37, March.
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    Cited by:

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    2. Kinga Ivan & József Benedek & Silviu Marian Ciobanu, 2019. "School-Aged Pedestrian–Vehicle Crash Vulnerability," Sustainability, MDPI, vol. 11(4), pages 1-12, February.
    3. Young, Jason & Park, Peter Y., 2014. "Hotzone identification with GIS-based post-network screening analysis," Journal of Transport Geography, Elsevier, vol. 34(C), pages 106-120.
    4. Ulak, Mehmet Baran & Ozguven, Eren Erman & Spainhour, Lisa & Vanli, Omer Arda, 2017. "Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida," Journal of Transport Geography, Elsevier, vol. 58(C), pages 71-91.
    5. Kuo, Pei-Fen & Lord, Dominique & Walden, Troy Duane, 2013. "Using geographical information systems to organize police patrol routes effectively by grouping hotspots of crash and crime data," Journal of Transport Geography, Elsevier, vol. 30(C), pages 138-148.
    6. Anatolie Coșciug & Silviu Marian Ciobanu & József Benedek, 2017. "The Safety of Transnational Imported Second-Hand Cars: A Case Study on Vehicle-to-Vehicle Crashes in Romania," Sustainability, MDPI, vol. 9(12), pages 1-14, December.
    7. Grembek, Offer & Medury, Aditya & Orrick, Phyllis & Leung, Katherine & Ragland, David R. & Loukaitou-Sideris, Anastasia & Fink, Camille N.Y. & Resnick, Justin & Wong, Norman & Shafizadeh, Kevan & Khan, 2014. "A Comparative Analysis of Pedestrian and Bicyclist Safety Around University Campuses," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt57s5214g, Institute of Transportation Studies, UC Berkeley.
    8. Ke Nie & Zhensheng Wang & Qingyun Du & Fu Ren & Qin Tian, 2015. "A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China," Sustainability, MDPI, vol. 7(3), pages 1-16, March.
    9. Dai, Dajun, 2012. "Identifying clusters and risk factors of injuries in pedestrian–vehicle crashes in a GIS environment," Journal of Transport Geography, Elsevier, vol. 24(C), pages 206-214.
    10. Loidl, Martin & Traun, Christoph & Wallentin, Gudrun, 2016. "Spatial patterns and temporal dynamics of urban bicycle crashes—A case study from Salzburg (Austria)," Journal of Transport Geography, Elsevier, vol. 52(C), pages 38-50.
    11. Bono, Flavio & Gutiérrez, Eugenio, 2011. "A network-based analysis of the impact of structural damage on urban accessibility following a disaster: the case of the seismically damaged Port Au Prince and Carrefour urban road networks," Journal of Transport Geography, Elsevier, vol. 19(6), pages 1443-1455.
    12. 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.

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