Multiple Criteria Decision Analysis and Geographic Information Systems

Malczewski, Jacek

doi:10.1007/978-1-4419-5904-1_13

Cited by 71 publications

(57 citation statements)

References 153 publications

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“…MCE is a well-known approach in GIS (Malczewski 2010) to determine suitability of a particular land unit based on the decision-making process considering a set of physical an socio-economic driving factors of development (Chow and Sadler 2010). The major environmental and socio-economic contributors of urban and urban related land development in the study area were determined by an intensive literature review and then getting opinions from three groups of professionals who worked in the Department of Town and Urban Planning Malaysia, the Malaysian Palm Oil Board (MPOB), and the Department of Agriculture Malaysia.…”

Section: Spatial Transitionsmentioning

confidence: 99%

Predicting the Effects of Urban Development on Land Transition and Spatial Patterns of Land Use in Western Peninsular Malaysia

Nourqolipour

Shariff

Balasundram

et al. 2014

Appl. Spatial Analysis

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Analyzing the effects of urban development on dynamic and spatial patterns of land use is vital to establish more efficient land management policies. However, in Malaysia, such effects are usually explained without quantitative metrics. This research quantified the future impact of urban expansion on the dynamic of land use by developing the area-independent dynamic metric. The metric was calculated based on summarizing the cross tabulation matrices of change in an urbanizing area at west coast of Peninsular Malaysia. Another two land use measures involving vulnerability to gain and Appl. Spatial Analysis vulnerability to loss were used to evaluate tendency of land classes to transition. The effects of urban development on spatial patterns of land use were quantified using two landscape metrics involving the Edge Density (ED) and Area-Weighted Mean Patch Fractal Dimension (AWMPFD). Analyses were carried out on a set of spatial land use data including observed 1997, 2002, and 2008, as well as a simulated near future land change for the year 2020 under a spatio-temporal land use model. Results showed that urban development practices would influence the dynamic of land transition in the near future. Urban growth would experience a fast-growing dynamic and high vulnerability to gain than loss while the dynamic and vulnerability of forest/wetland covers would decrease in terms of loss. Moreover, agriculture practices tend to be hindered by further urban development in the coming years. Another important finding was that urban development process would influence the spatial patterns of land use in the near future.

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Section: Spatial Transitionsmentioning

confidence: 99%

Predicting the Effects of Urban Development on Land Transition and Spatial Patterns of Land Use in Western Peninsular Malaysia

Nourqolipour

Shariff

Balasundram

et al. 2014

Appl. Spatial Analysis

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“…On the other hand, MCDA offer a rich collection of methods for supporting decision making procedures. The multidisciplinary field of GIS-MCDA has been widely and strongly adopted within the GIS community (Malczewski 2006(Malczewski , 2010. Quite correctly, the GIS community recognizes the benefits to be gained by incorporating MCDA into a suite of GIS capabilities.…”

Section: Contribution To Gisciencementioning

confidence: 99%

“…These problems have, to a large extent, been ignored by the GIS-MCDA community. If a primary purpose of GIS-MCDA is to process and synthesize large spatial datasets and value judgments, and to examine the implications of those value judgments for planning and policy making, then more careful consideration must be given to the assumptions underlying the MCDA procedures (Malczewski 2010).…”

Section: Challenges and Prospectsmentioning

confidence: 99%

Conclusions

Malczewski¹,

Rinner²

2015

Advances in Geographic Information Science

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The aim of this book was to provide a comprehensive account of theories, methods, and technologies involved in GIS-MCDA procedures. This was achieved by: (i) explaining the fundamental concepts of MCDA and integrating those concepts into GIS-based methods, (ii) overviewing GIS-MCDA methods and demonstrating their applications in a wide variety of decision situations, and (iii) presenting a set of information technologies and decision support tools available for integrating GIS and MCDA methods. Specifically, the discussion of relevant concepts, methods, and technologies was organized into three parts: Preliminaries (Part I), Spatial MCDA: Methods (Part II), and Spatial MCDA: Technologies (Part III).Part I examined the linkages between GIScience, spatial analysis, and decision support (Chap. 1). It then provided an overview of generic elements of MCDA and the basic concepts of GIS-MCDA including: value scaling, criterion weighting and combination rules (Chap. 2). Part I also outlined the development of GIS-MCDA research and applications (Chap. 3). We emphasized the spatial aspects of GIS-MCDA by making a distinction between spatially implicit and explicit evaluation criteria, criterion weights, and decision alternatives. This distinction was central for identifying two groups of MCDA methods integrated into GIS: conventional (spatially implicit) and spatially explicit MCDA methods.Part II discussed GIS-MCDA models and procedures including: multiattribute methods (such as the weighted linear combination, ideal point methods, the analytic hierarchy/network process, and outranking methods), and multiobjective optimization methods (such as generating non-dominated solutions, distance-based, and interactive methods) (Chaps. 4 and 5, respectively). In addition to the conventional methods, we presented a selection of basic heuristics (such as site suitability and site location heuristics, and greedy algorithms) and meta-heuristics (such as genetic algorithms, simulation annealing, tabu search, and swarm intelligence methods) for tackling complex decision situations (Chap. 6). We emphasized the relevance and

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“…This includes, e.g., graphs in the prediction problems in bio-informatics or texts and images in the retrieval problems. Nevertheless, it is worth to note the recent effort of the MCDA community to apply decision aiding methods to geographical (spatial) data (Malczewski 2010). …”

Section: Data Descriptionmentioning

confidence: 99%

Robust ordinal regression in preference learning and ranking

et al. 2013

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Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking.

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Multiple Criteria Decision Analysis and Geographic Information Systems

Cited by 71 publications

References 153 publications

Predicting the Effects of Urban Development on Land Transition and Spatial Patterns of Land Use in Western Peninsular Malaysia

Predicting the Effects of Urban Development on Land Transition and Spatial Patterns of Land Use in Western Peninsular Malaysia

Conclusions

Robust ordinal regression in preference learning and ranking

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