Fuzzy Sets Approach to Spatial Analysis and Planning, a Nontechnical Evaluation

Leung, Yee

doi:10.2307/490935

Cited by 16 publications

(7 citation statements)

References 9 publications

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“…For instance, fuzzy classification is widely applied to classify land uses and land covers from satellite imagery, and to model terrain and map soil types in physical geography (Wilson and Burrough, 1999). However, adoption in socioeconomic geography has been rather slow in coming (Leung, 1983;Openshaw and Openshaw, 1997;Thill and Sui, 1993).…”

Section: Social Science Applications Of Fuzzy Clustering Methodsmentioning

confidence: 99%

Delineating urban housing submarkets with fuzzy clustering

Hwang

Thill

2009

Environ. Plann. B

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It has long been argued that the housing market is spatially compartmentalized within a metropolitan area. The argument has important implications for explaining how the housing market works—should the status quo be seen as an equilibrium state? Or if no equilibrium is reached, how do loosely interlaced submarkets function both independently and interdependently? We note that the body of literature has leaned toward testing the distinctiveness of housing submarkets given a priori housing submarkets. However, there seems to be a lack of interest in developing methods for deriving housing submarkets empirically. Fuzzy clustering is well suited to this problem, given that the boundary of housing submarkets is not often sharply delineated. The study applies a fuzzy c-means (FCM) algorithm to identify housing submarkets in the Buffalo–Niagara Falls region. The study is distinct from other FCM applications in three respects. First, we reflect on issues tied to choosing parameters of fuzzy clustering. Second, we introduce overlap measures to characterize the relationship between the clusters produced. Third, we evaluate the performance of fuzzy clustering in terms of hedonic prediction accuracy. Results show that stratified hedonic models predict house price better than a market-wide hedonic model. Fuzzy clustering solutions also yield better prediction, compared with hard clustering.

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Section: Social Science Applications Of Fuzzy Clustering Methodsmentioning

confidence: 99%

Delineating urban housing submarkets with fuzzy clustering

Hwang

Thill

2009

Environ. Plann. B

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“…From theoretical approaches formulated for conceptualization of inexact spatial concepts (Leung 1983), a conceptualization of inexact concepts and phenomena characterizing SFM -as fuzzy subsets, of a universe of discourse namely SFM, having imprecise spatial connotations -is proposed. Consider an example of a forest community, where (for simplicity) SFM is defined by only two criteria, site productivity (for timber) and biodiversity.…”

Section: Possibility Schema For Sfm Using Criteria and Indicators: Anmentioning

confidence: 99%

“…Nilsson et al (2004) have observed widespread uncertainty in management for the multiple objectives; managers cannot be certain whether all data accurately reflect the current state of all forest values, how the forest will change, or how management practices will affect the forest. According to Leung (1983) complexity of human systems, Martin Herbert Kijazi nance of healthy 3 ecosystems; but "healthy" represents inexact condition -a level of this attribute -rather than its presence or absence.…”

Section: Introductionmentioning

confidence: 99%

Possibility schema for interdisciplinary forest management evaluation and decision-making

Kijazi¹

2005

The Forestry Chronicle

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Interdisciplinary planning and evaluation of forest management is necessary for sustainable forest management (SFM) schemes involving multiple values of multi-stakeholders. Often, multi-objective forest-planning and evaluation encounter complexity and uncertainty due to "inexactness"— i.e., fuzziness, ambiguity, imprecision and variability — of spatial behaviours of ecological and human systems. This paper develops the possibility schema — from fuzzy sets and theory of possibility — for representation and evaluation of inexact spatial concepts, configurations, and processes, associated with forest ecosystem and stakeholder values. A hypothetical case of interdisciplinary research utilizing criteria and indicators of SFM is used to illustrate the utility of the proposed possibility schema in interdisciplinary forest decision-making. The schema can be used for ex-ante appraisal and ex-post evaluation of forest programs. It can also be used for integration of interdisciplinary forest knowledge, including ecological and socio-economic models of SFM. Key words: decision-making, fuzzy sets, inexactness, interdisciplinary evaluation, multiple values, possibility theory, sustainable forest management, uncertainty

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“…Zimmerman (1991) described the use of fuzzy logic within a decision making process at the theoretical level. Leung (1983) identified spatial planning as a field in which the use of fuzzy logic could be successful. Steinhardt (1998) stated that by applying fuzzy logic to a landscape assessment of larger areas, their actual heterogeneity is better represented.…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy logic has previously been applied in spatial planning (Leung 1983). Zimmerman (1991) described the use of fuzzy logic within a decision making process at the theoretical level.…”

Section: Introductionmentioning

confidence: 99%

Dealing with the Uncertainty of Having Incomplete Sources of Geo-Information in Spatial Planning

Vullings

Blok

Wessels³

et al. 2012

Appl. Spatial Analysis

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The Dutch spatial planning legal act of 2008 was aimed at improving efficiency and effectiveness in the development, evaluation and monitoring of spatial planning policy (Ministry of VROM, 2006a). One of the main effects of this legal act was the widespread availability and use of digital spatial plans (Ministry of VROM 2006a, b). This reform led to the expectation that all digital spatial plans would be exchangeable and comparable. In practice, this exchange and comparison required carrying out complex procedures due to uncertainty caused by differences in the scope of spatial plans as well as their intended use. Furthermore the uncertainty resulted in a lack of confidence in spatial plans by policymakers and supporting GIS staff. Our overarching research question was: how can uncertainty caused by incomplete geo-information sources be dealt with? We proposed two techniques-fuzzy logic and visualisation-for policy makers to deal with uncertainty resulting from incomplete geo-information sources in spatial planning at the regional and national planning levels. We used two case studies in the Netherlands to illustrate the results of applying these techniques. The fuzzy set theory provides extra information by converting the discrete borders of continuous objects into fuzzy borders that improve the resemblance to the real object and thus make it more realistic. As shown in the second case study, visualisation also improves the degree of realism and thus provides additional information. Both case studies showed that providing additional information reduces the uncertainty felt by policymakers.

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Fuzzy Sets Approach to Spatial Analysis and Planning, a Nontechnical Evaluation

Cited by 16 publications

References 9 publications

Delineating urban housing submarkets with fuzzy clustering

Delineating urban housing submarkets with fuzzy clustering

Possibility schema for interdisciplinary forest management evaluation and decision-making

Dealing with the Uncertainty of Having Incomplete Sources of Geo-Information in Spatial Planning

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