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Locally weighted linear combination in a vector geographic information system

Author

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  • Brad Carter
  • Claus Rinner

Abstract

Weighted linear combination is a multi-criteria decision analysis technique that can be used by decision-makers to select an optimal location from a collection of alternative locations. Its local form takes into account the range of attribute values within a user-defined neighbourhood in accordance with the range-sensitivity principle. This research explores locally weighted linear combination in a vector-based geographic information system. A custom application in ArcGIS 10 allows the user to select a neighbourhood definition from a standard set including contiguity, distance, and k-nearest neighbours, for which local weights are generated. A case study on vulnerability to heat-related illness in Toronto is used to illustrate the technique. The impact of local weighting on the heat vulnerability index is examined using visual analysis of the spatial patterns of heat vulnerability under the global and local approaches, as well as the sensitivity of the local approach to the selected neighbourhood definition. A trade-off analysis of the local weights is also presented. The combination of socio-demographic and environmental determinants in a locally weighted index results in patterns of heat vulnerability that could support targeted hot weather response at a micro-geographic level within urban neighbourhoods. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Brad Carter & Claus Rinner, 2014. "Locally weighted linear combination in a vector geographic information system," Journal of Geographical Systems, Springer, vol. 16(3), pages 343-361, July.
    • Handle: RePEc:kap:jgeosy:v:16:y:2014:i:3:p:343-361
    DOI: 10.1007/s10109-013-0194-3
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    References listed on IDEAS

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    1. Fischer, Gregory W., 1995. "Range Sensitivity of Attribute Weights in Multiattribute Value Models," Organizational Behavior and Human Decision Processes, Elsevier, vol. 62(3), pages 252-266, June.
    2. Goldstein, William M., 1990. "Judgments of relative importance in decision making: Global vs local interpretations of subjective weight," Organizational Behavior and Human Decision Processes, Elsevier, vol. 47(2), pages 313-336, December.
    3. M. Saisana & A. Saltelli & S. Tarantola, 2005. "Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 307-323, March.
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    Citations

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    Cited by:

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    2. Karim Solaimani & Fatemeh Shokrian & Shadman Darvishi, 2023. "An Assessment of the Integrated Multi-Criteria and New Models Efficiency in Watershed Flood Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 403-425, January.
    3. Ferretti, Valentina & Geneletti, Davide, 2020. "Does the spatial representation affect criteria weights in environmental decision-making? Insights from a behavioral experiment," Land Use Policy, Elsevier, vol. 97(C).
    4. Mehmet Ali Dereli & Emre Tercan, 2021. "Comparison of GIS-based surrogate weighting methods for multi-directional landfill site selection in West Mediterranean Planning Region in Turkey," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(3), pages 3438-3457, March.
    5. Qiang Niu & Li Yu & Qiao Jie & Xian Li, 2020. "An urban eco-environmental sensitive areas assessment method based on variable weights combination," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(3), pages 2069-2085, March.
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    More about this item

    Keywords

    Heat vulnerability; Local WLC; Range sensitivity; Spatial multi-criteria decision analysis; C61; D81; I18; R22;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • R22 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Other Demand

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