Locally weighted linear combination in a vector geographic information system

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.

[1]  David J Hulchanski.  The three cities within Toronto : income polarization among Toronto's neighbourhoods, 1970-2005 , 2007 .

[2]  Claus Rinner,et al.  The Role of Maps in Neighborhood-level Heat Vulnerability Assessment for the City of Toronto , 2010 .

[3]  Jacek Malczewski,et al.  Local Weighted Linear Combination , 2011, Trans. GIS.

[4]  G. W. Fischer.  Range Sensitivity of Attribute Weights in Multiattribute Value Models , 1995 .

[5]  Terry A. Slocum.  Thematic Cartography and Visualization , 1998 .

[6]  Stephen J. Carver,et al.  Integrating multi-criteria evaluation with geographical information systems , 1991, Int. J. Geogr. Inf. Sci..

[7]  W. Goldstein.  Judgments of relative importance in decision making: Global vs local interpretations of subjective weight , 1990 .

[8]  M. Kwan.  The Uncertain Geographic Context Problem , 2012 .

[9]  David O'Sullivan,et al.  Geographic Information Analysis , 2002 .

[10]  Arika Ligmann-Zielinska,et al.  A Framework for Sensitivity Analysis in Spatial Multiple Criteria Evaluation , 2008, GIScience.

[11]  Joel Schwartz,et al.  Mapping Community Determinants of Heat Vulnerability , 2008, Environmental health perspectives.

[12]  Claus Rinner,et al.  The Spatial Dimensions of Multi-Criteria Evaluation - Case Study of a Home Buyer's Spatial Decision Support System , 2006, GIScience.

[13]  Jacek Malczewski,et al.  GIS‐based multicriteria decision analysis: a survey of the literature , 2006, Int. J. Geogr. Inf. Sci..

[14]  Stefano Tarantola,et al.  Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators , 2005 .

[15]  Jacek Malczewski,et al.  On the Use of Weighted Linear Combination Method in GIS: Common and Best Practice Approaches , 2000, Trans. GIS.

[16]  Piotr Jankowski,et al.  Integrating Geographical Information Systems and Multiple Criteria Decision-Making Methods , 1995, Int. J. Geogr. Inf. Sci..

[17]  Piet Rietveld,et al.  Spatial Dimensions in Multicriteria Analysis , 2019, Spatial Multicriteria Decision Making and Analysis.

[18]  Jacek Malczewski,et al.  GIS and Multicriteria Decision Analysis , 1999 .

[19]  G. Brent Hall,et al.  A method for examining the spatial dimension of multi-criteria weight sensitivity , 2004, Int. J. Geogr. Inf. Sci..