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Distance predicting functions and applied location-allocation models

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  • PEETERS, Dominique
  • THOMAS, Isabelle

Abstract

. Distances between demand points and potential sites for implementing facilities are essential inputs to location-allocation models. Computing actual road distances for a given problem can be quite burdensome since it involves digitalizing a network, while approximating these distances by l p -norms, using for instance a geographical information system, is much easier. We may then wonder how sensitive the solutions of a location-allocation model are to the choice of a particular metric. In this paper, simulations are performed on a lattice of 225 points using the k-median problem. Systematic changes in p and in the orientation of the orthogonal reference axes are used. Results suggest that the solutions of the k-median are rather insensitive to the specification of the l p -norm.
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  • PEETERS, Dominique & THOMAS, Isabelle, 2000. "Distance predicting functions and applied location-allocation models," LIDAM Reprints CORE 1458, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:1458
    DOI: 10.1007/PL00011453
    Note: In : Journal of Geographical Systems, 2, 167-184, 2000
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    3. Miao Du & Yuhua Zhao & Tao Fang & Linyu Fan & Minghua Zhang & Hong Huang & Kun Mei, 2022. "Evaluating the Inequality of Medical Resource Allocation Based on Spatial and Non-Spatial Accessibility: A Case Study of Wenzhou, China," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    4. Jiansheng Wu & Tengyun Yi & Han Wang & Hongliang Wang & Jiayi Fu & Yuhao Zhao, 2022. "Evaluation of Medical Carrying Capacity for Megacities from a Traffic Analysis Zone View: A Case Study in Shenzhen, China," Land, MDPI, vol. 11(6), pages 1-19, June.
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    6. Turkensteen, Marcel & Klose, Andreas, 2012. "Demand dispersion and logistics costs in one-to-many distribution systems," European Journal of Operational Research, Elsevier, vol. 223(2), pages 499-507.
    7. Nan Dong & Xiaohuan Yang & Hongyan Cai & Liming Wang, 2015. "A Novel Method for Simulating Urban Population Potential Based on Urban Patches: A Case Study in Jiangsu Province, China," Sustainability, MDPI, vol. 7(4), pages 1-20, April.
    8. Alan T. Murray & Antonio Ortiz & Seonga Cho, 2022. "Enhancing strategic defensive positioning and performance in the outfield," Journal of Geographical Systems, Springer, vol. 24(2), pages 223-240, April.
    9. Meihan Jin & Lu Liu & De Tong & Yongxi Gong & Yu Liu, 2019. "Evaluating the Spatial Accessibility and Distribution Balance of Multi-Level Medical Service Facilities," IJERPH, MDPI, vol. 16(7), pages 1-19, March.
    10. Nai Yang & Shiyi Chen & Weilu Hu & Zhongheng Wu & Yi Chao, 2016. "Spatial Distribution Balance Analysis of Hospitals in Wuhan," IJERPH, MDPI, vol. 13(10), pages 1-16, September.
    11. Huanhuan Zhu & Lin Pan & Yiji Li & Huiming Jin & Qian Wang & Xin Liu & Cong Wang & Peng Liao & Xinyang Jiang & Luo Li, 2021. "Spatial Accessibility Assessment of Prehospital EMS with a Focus on the Elderly Population: A Case Study in Ningbo, China," IJERPH, MDPI, vol. 18(19), pages 1-17, September.
    12. Ying Chen & Jiale Wu, 2022. "The Effect of the Referral System on the Accessibility of Healthcare Services: A Case Study of the Wuhan Metropolitan Development Zone," IJERPH, MDPI, vol. 19(16), pages 1-14, August.
    13. Gu, Zongni & Luo, Xiaolong & Tang, Mi & Liu, Xiaoman, 2023. "Does the edge effect impact the healthcare equity? An examination of the equity in hospitals accessibility in the edge city in multi-scale," Journal of Transport Geography, Elsevier, vol. 106(C).
    14. Min Cheng & Li Tao & Yuejiao Lian & Weiwei Huang, 2021. "Measuring Spatial Accessibility of Urban Medical Facilities: A Case Study in Changning District of Shanghai in China," IJERPH, MDPI, vol. 18(18), pages 1-18, September.

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