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Maximising coverage of spatial demand for service


  • Daoqin Tong
  • Alan T. Murray


How to represent geographic space has long been an issue in location modelling. Facilities, demand and/or the region of interest are often abstracted using aggregated points. However, substantial errors can be introduced, and obtained solutions could be dependent on the degree of aggregation. To address this, geographic representation of space is undergoing renewed research interest in spatial analysis and modelling. In this article the maximal coverage problem is studied, with a particular focus on demand coverage representation. Due to the limitations of existing modelling approaches for examining the coverage of space, there exist significant discrepancies between what is modelled and actual geographic coverage. In order to accurately reflect the mechanism of maximal coverage for spatial objects (points, lines or polygons), we introduce a new model explicitly accounting for joint service provided by multiple facilities. The new model can be viewed as an extension of existing approaches, but also a generalisation. An application to warning siren siting is carried out to demonstrate the merits of this new approach. Copyright (c) 2008 the author(s). Journal compilation (c) 2008 RSAI.

Suggested Citation

  • Daoqin Tong & Alan T. Murray, 2009. "Maximising coverage of spatial demand for service," Papers in Regional Science, Wiley Blackwell, vol. 88(1), pages 85-97, March.
  • Handle: RePEc:bla:presci:v:88:y:2009:i:1:p:85-97

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    References listed on IDEAS

    1. Alan T. Murray & Morton E. O'Kelly, 2002. "Assessing representation error in point-based coverage modeling," Journal of Geographical Systems, Springer, vol. 4(2), pages 171-191, June.
    2. Sándor P. Fekete & Joseph S. B. Mitchell & Karin Beurer, 2005. "On the Continuous Fermat-Weber Problem," Operations Research, INFORMS, vol. 53(1), pages 61-76, February.
    3. Bilal Farhan & Alan Murray, 2006. "Distance decay and coverage in facility location planning," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 40(2), pages 279-295, June.
    4. F. Robert Dwyer & James R. Evans, 1981. "A Branch and Bound Algorithm for the List Selection Problem in Direct Mail Advertising," Management Science, INFORMS, vol. 27(6), pages 658-667, June.
    5. S. L. Hakimi, 1964. "Optimum Locations of Switching Centers and the Absolute Centers and Medians of a Graph," Operations Research, INFORMS, vol. 12(3), pages 450-459, June.
    6. Bennett, Vivienne L. & Eaton, David J. & Church, Richard L., 1982. "Selecting sites for rural health workers," Social Science & Medicine, Elsevier, vol. 16(1), pages 63-72, January.
    7. S. L. Hakimi, 1965. "Optimum Distribution of Switching Centers in a Communication Network and Some Related Graph Theoretic Problems," Operations Research, INFORMS, vol. 13(3), pages 462-475, June.
    8. Carrizosa, E. & Munoz-Marquez, M. & Puerto, J., 1998. "The Weber problem with regional demand," European Journal of Operational Research, Elsevier, vol. 104(2), pages 358-365, January.
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    Cited by:

    1. Chevalier, Philippe & Thomas, Isabelle & Geraets, David & Goetghebeur, Els & Janssens, Olivier & Peeters, Dominique & Plastria, Frank, 2012. "Locating fire stations: An integrated approach for Belgium," Socio-Economic Planning Sciences, Elsevier, vol. 46(2), pages 173-182.
    2. repec:eee:transe:v:107:y:2017:i:c:p:38-59 is not listed on IDEAS
    3. Murray, Alan T. & Wei, Ran, 2013. "A computational approach for eliminating error in the solution of the location set covering problem," European Journal of Operational Research, Elsevier, vol. 224(1), pages 52-64.
    4. Murray, Alan T. & Feng, Xin, 2016. "Public street lighting service standard assessment and achievement," Socio-Economic Planning Sciences, Elsevier, vol. 53(C), pages 14-22.

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