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Using Estimated Missing Spatial Data with the 2-Median Model

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  • Daniel Griffith

Abstract

Spatial operations research problems seek “best” locations, often points of minimum aggregate weighted distance, requiring georeferenced data as input. Frequently maps of such data are incomplete, with holes in their geographic distributions. Spatial statistical procedures are available to complete these data sets with best estimates of the missing values. Impacts such imputations have on 2-median facility location–allocation solutions are explored. The sampling distribution of the spatial mean and standard distance of these medians are studied. Population density is used as the weight attribute in determining location-allocation solutions because it can be accurately described with a relatively simple spatial statistical model. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • Daniel Griffith, 2003. "Using Estimated Missing Spatial Data with the 2-Median Model," Annals of Operations Research, Springer, vol. 122(1), pages 233-247, September.
    • Handle: RePEc:spr:annopr:v:122:y:2003:i:1:p:233-247:10.1023/a:1026106825798
    DOI: 10.1023/A:1026106825798
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    Cited by:

    1. Daniel Griffith & Yongwan Chun, 2015. "Spatial Autocorrelation in Spatial Interactions Models: Geographic Scale and Resolution Implications for Network Resilience and Vulnerability," Networks and Spatial Economics, Springer, vol. 15(2), pages 337-365, June.
    2. Daniel A. Griffith, 2021. "Articulating Spatial Statistics and Spatial Optimization Relationships: Expanding the Relevance of Statistics," Stats, MDPI, vol. 4(4), pages 1-18, October.
    3. Jiyong Chen & Dabo Chen & Aiping Yao, 2020. "Trade development between China and countries along the Belt and Road: A spatial econometric analysis based on trade competitiveness and complementarity," Pacific Economic Review, Wiley Blackwell, vol. 25(2), pages 205-227, May.
    4. Carlos García-Alonso & Leonor Pérez-Naranjo & Juan Fernández-Caballero, 2014. "Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms," Annals of Operations Research, Springer, vol. 219(1), pages 187-202, August.

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