Identification of deprivation degrees using two models of fuzzy-clustering and fuzzy logic based on regional indices: A case study of Fars province. (October 2016)

• Record Type: Journal Article
• Authors: Najjary, Zahra; Saremi, Hamidreza; Biglarbegian, Mohammad; Najari, Amirabbas
• Is Part Of: Cities. Volume 58(2016)
• Page Start: 115
• Page End: 123
• Publication Date: 2016-10
• Subjects: Deprivation index -- Iran -- Fuzzy-clustering -- Fuzzy; City planning -- Periodicals; Urban policy -- Periodicals; 711.4
• DOI: 10.1016/j.cities.2016.05.013
• Languages: English
• ISSNs: 0264-2751

Abstract

Abstract: Increases in deprivation and inequality within urban areas will result in unwanted negative impacts, such as de-population (immigration), suburbanization, and increases in crime. Hence, the mitigation of deprivation should be a primary consideration for policy makers when promoting sustainable development. A robust deprivation model is needed to analyze the effects of deprivation indices and related parameters. It is thus important to identify the significant deprivation parameters first. Subsequently, this paper attempts to derive proper deprivation indices and also proposes a new model to determine the degrees of deprivation of different cities in a province (called region). Eight deprivation indices (e.g. educational, cultural, health, welfare, housing, transportation, and service) are considered and to completely capture each index, four new parameters are designated. Next, a new hybrid model is proposed based on two techniques: fuzzy-clustering and fuzzy logic. Using the fuzzy-clustering method, cities are first classified into two groups of deprived and fully developed. To determine the degree of deprivation, we then develop a new system using fuzzy logic. The proposed fuzzy logic system feeds in the outputs from the fuzzy-clustering system and the deprivation of each city (for each index) is finally obtained. As a case study, 29 cities in the Fars province (Iran) were considered and the degree of deprivation for each city was identified. Results (deprivation degree) for each city and for each individual index were presented both quantitatively and qualitatively. The proposed model, unlike classical methods, has a non-binary view to deprivation, assigns a degree to deprivation for mitigating its negative effects, can be used for proper future planning, and is generic so that it can be easily applied to other cases as well. Highlights: We identify and propose new deprivation indices that are not specific to only one province; instead, they are general and applicable to different provinces in Iran: We look at fuzzy (overlapping boundaries) in identifying degree of deprivation, as opposed to have a binary (one extreme or another) perspective. This outlook is more suitable for urban planning especially in identifying deprivation. It is also theoretically sound because it's based on the theory of fuzzy sets and logic. We propose a new hybrid method consisting of fuzzy logic and fuzzy clustering in urban studies and planning. Fuzzy logic might have been proposed, fuzzy clustering is also new but their combination in urban studies is our novel contribution, which benefits from the two method.