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Enhancing Classic Coverage Location Models

Author

Listed:
  • Alan T. Murray

    (Arizona State University, Tempe, Arizona, atmurray@asu.edu)

  • Daoqin Tong

    (University of Arizona, Tucson, Arizona)

  • Kamyoung Kim

    (Kyungpook National University, Buk-gu Daegu, Korea)

Abstract

An important area of regional science has long been location analysis and modeling. Its significance continues, now more formally known as location science, and has evolved because of the need to address complex facility siting problems and issues. This article focuses on classic coverage location problems, and how advances along theoretical and methodological fronts have enabled such problems to be viewed in new ways. Specifically, notions of implicit and explicit coverage, along with geographic information systems (GIS), provide the capacity to reconceptualize as well as better model intended planning goals and objectives. This article reviews covering problems and presents a comparative framework for both linkage and assessment. This research is significant because evolving models enable issues of frame independence, and the modifiable area unit problem, to be addressed, making planning and analysis more reliable and valuable.

Suggested Citation

  • Alan T. Murray & Daoqin Tong & Kamyoung Kim, 2010. "Enhancing Classic Coverage Location Models," International Regional Science Review, , vol. 33(2), pages 115-133, April.
    • Handle: RePEc:sae:inrsre:v:33:y:2010:i:2:p:115-133
    DOI: 10.1177/0160017609340149
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    References listed on IDEAS

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    1. Constantine Toregas & Ralph Swain & Charles ReVelle & Lawrence Bergman, 1971. "The Location of Emergency Service Facilities," Operations Research, INFORMS, vol. 19(6), pages 1363-1373, October.
    2. Kathleen Hogan & Charles ReVelle, 1986. "Concepts and Applications of Backup Coverage," Management Science, INFORMS, vol. 32(11), pages 1434-1444, November.
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    4. 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.
    5. Richard Church & Charles R. Velle, 1974. "The Maximal Covering Location Problem," Papers in Regional Science, Wiley Blackwell, vol. 32(1), pages 101-118, January.
    6. Kamyoung Kim & Alan T. Murray, 2008. "Enhancing Spatial Representation In Primary And Secondary Coverage Location Modeling," Journal of Regional Science, Wiley Blackwell, vol. 48(4), pages 745-768, October.
    7. ReVelle, Charles, 1989. "Review, extension and prediction in emergency service siting models," European Journal of Operational Research, Elsevier, vol. 40(1), pages 58-69, May.
    8. CONSTANTINE TOREGAS & CHARLES ReVELLE, 1972. "Optimal Location Under Time Or Distance Constraints," Papers in Regional Science, Wiley Blackwell, vol. 28(1), pages 133-144, January.
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    Citations

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    Cited by:

    1. Yıldız, Nurdan & Tüysüz, Fatih, 2019. "A hybrid multi-criteria decision making approach for strategic retail location investment: Application to Turkish food retailing," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    2. 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.
    3. 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.
    4. Ran Wei & Alan Murray & Rajan Batta, 2014. "A bounding-based solution approach for the continuous arc covering problem," Journal of Geographical Systems, Springer, vol. 16(2), pages 161-182, April.
    5. Caro Vela, María Dolores & Paralera Morales, Concepción, 2011. "Una propuesta para la localización de áreas de servicio y descanso adaptadas al transporte de mercancías peligrosas mediante un modelo de optimización; aplicación al territorio español = A Proposal fo," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 11(1), pages 17-32, June.

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