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Spatial clustering of events on a network

Author

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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.
(This abstract was borrowed from another version of this item.)

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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    Cited by:

    1. Kinga Ivan & József Benedek & Silviu Marian Ciobanu, 2019. "School-Aged Pedestrian–Vehicle Crash Vulnerability," Sustainability, MDPI, vol. 11(4), pages 1-12, February.
    2. Miao, Congcong & Chen, Xiang & Zhang, Chuanrong, 2024. "Assessing network-based traffic crash risk using prospective space-time scan statistic method," Journal of Transport Geography, Elsevier, vol. 119(C).
    3. 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.
    4. 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.
    5. 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.
    6. Yu, Wenhao & Ai, Tinghua & Shao, Shiwei, 2015. "The analysis and delimitation of Central Business District using network kernel density estimation," Journal of Transport Geography, Elsevier, vol. 45(C), pages 32-47.
    7. 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.
    8. 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.
    9. 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.
    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.
    11. 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.
    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.
    13. 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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