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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.

Suggested Citation

  • Steenberghen, Thérèse & Aerts, Koen & Thomas, Isabelle, 2010. "Spatial clustering of events on a network," Journal of Transport Geography, Elsevier, vol. 18(3), pages 411-418.
    • Handle: RePEc:eee:jotrge:v:18:y:2010:i:3:p:411-418
    DOI: 10.1016/j.jtrangeo.2009.08.005
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    References listed on IDEAS

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    1. 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.
    2. 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.
    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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    2. 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.
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    5. 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.
    6. Kinga Ivan & József Benedek & Silviu Marian Ciobanu, 2019. "School-Aged Pedestrian–Vehicle Crash Vulnerability," Sustainability, MDPI, vol. 11(4), pages 1-12, February.
    7. 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.
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    9. 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.
    10. 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.
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    12. 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.

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