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Regionalisation Tools for the Exploratory Spatial Analysis of Health Data

In: Recent Developments in Spatial Analysis

Author

Listed:
  • Steve Wise

    (University of Sheffield)

  • Robert Haining

    (University of Sheffield)

  • Jingsheng Ma

    (University of Sheffield)

Abstract

This paper considers issues associated with the construction of regions as part of a programme of exploratory spatial data analysis in the case of what Cressie (1991) refers to as “lattice data”. Lattice data arise where a study area has been partitioned into a set of zones or regions attached to each of which is a vector that describes the set of attributes for that zone. The focus of this paper will be the analysis of health data so the attributes in question may be health related but may also include demographic, socio-economic and environmental attributes.

Suggested Citation

  • Steve Wise & Robert Haining & Jingsheng Ma, 1997. "Regionalisation Tools for the Exploratory Spatial Analysis of Health Data," Advances in Spatial Science, in: Manfred M. Fischer & Arthur Getis (ed.), Recent Developments in Spatial Analysis, chapter 5, pages 83-100, Springer.
    • Handle: RePEc:spr:adspcp:978-3-662-03499-6_5
    DOI: 10.1007/978-3-662-03499-6_5
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    Citations

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    Cited by:

    1. Hyun Kim & Yongwan Chun & Kamyoung Kim, 2015. "Delimitation of Functional Regions Using a p-Regions Problem Approach," International Regional Science Review, , vol. 38(3), pages 235-263, July.
    2. Juan Carlos Duque & Raúl Ramos & Jordi Suriñach, 2007. "Supervised Regionalization Methods: A Survey," International Regional Science Review, , vol. 30(3), pages 195-220, July.
    3. Carlos Mendez & Erick Gonzales, 2021. "Human Capital Constraints, Spatial Dependence, and Regionalization in Bolivia: A Spatial Clustering Approach," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 44(87), pages 115-145.
    4. Pablo Quintana, 2022. "Una metodología de clustering para agrupar series temporales en regiones contiguas," Asociación Argentina de Economía Política: Working Papers 4589, Asociación Argentina de Economía Política.
    5. Bruno, Giuseppe & Genovese, Andrea & Piccolo, Carmela, 2017. "Territorial amalgamation decisions in local government: Models and a case study from Italy," Socio-Economic Planning Sciences, Elsevier, vol. 57(C), pages 61-72.
    6. Rui Fragoso & Conceição Rego & Vladimir Bushenkov, 2016. "Clustering of Territorial Areas: A Multi-Criteria Districting Problem," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(2), pages 179-198, December.
    7. Juan C Duque & Henry Laniado & Adriano Polo, 2018. "S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-25, November.
    8. Mack, Elizabeth A. & Maciejewski, Ross, 2015. "A profile of visual analytical toolkits for understanding the spatio-temporal evolution of broadband provision," Telecommunications Policy, Elsevier, vol. 39(3), pages 320-332.
    9. Maria da Conceição Rego & Rui Fragoso & Vladimir Bushenkov, 2014. "Clustering of Territorial Areas: A Multi-Criteria Districting Problem," ERSA conference papers ersa14p218, European Regional Science Association.
    10. Dongyoung Kim & Sungwon Jung & Yongwook Jeong, 2021. "Theft Prediction Model Based on Spatial Clustering to Reflect Spatial Characteristics of Adjacent Lands," Sustainability, MDPI, vol. 13(14), pages 1-14, July.
    11. Pablo Aníbal Quintana, 2021. "Métodos de clustering espacialmente restringidos: Un análisis al agrupamiento por nivel de estudio en la provincia de Mendoza," Asociación Argentina de Economía Política: Working Papers 4510, Asociación Argentina de Economía Política.
    12. Shortt, Niamh K. & Moore, Adrian & Coombes, Mike & Wymer, Colin, 2005. "Defining regions for locality health care planning: a multidimensional approach," Social Science & Medicine, Elsevier, vol. 60(12), pages 2715-2727, June.

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