A sociotechnical approach to causes of urban blight using fuzzy cognitive mapping and system dynamics

Lousada, Ana L.D.; Ferreira, Fernando; Meidutė‐Kavaliauskienė, Ieva; Spahr, Ronald W.; Sunderman, Mark A.; Pereira, Leandro

doi:10.1016/j.cities.2020.102963

Cited by 20 publications

(22 citation statements)

References 57 publications

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“…Currently, city planning far exceeds only an economic focus. Attitudes regarding planning and developing strategies for urban areas must take into account social and environmental aspects, addressing the triple bottom line (TBL) (Lousada et al, 2021 ; Wise, 2016 ). This perspective promotes sustainable development, where environmental protection becomes a central challenge for all governments and, more critically, for all markets in the twenty-first century (Costa et al, 2021 ; Elkington, 2017 ; Fernandes et al, 2018 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…A few previous models addressing blight have limitations regarding blight’s clarification, conceptualization, causes, and analyses of cause-and-effect relationships among decision criteria (Ferreira et al, 2018 ; Lousada et al, 2021 ; Pinto et al, 2021 ). Our study, given these limitations and blight’s complexity and subjectivity, uses multiple criteria decision analysis (MCDA) to combine structuring and evaluation methods that address a myriad of factors associated with blight.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing causes of urban blight using cognitive mapping and DEMATEL

et al. 2022

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Blight is a concept not commonly discussed. However, blight is a problem that exists in the lives of many people, especially if they reside in urban areas. Blight originates whenever properties are neglected, contributing to both a functional and social depreciation process and ultimately leading to uninhabitable dwellings. Despite being blighted, these properties and surrounding neighborhoods often are occupied by families who fail to have sufficient income to afford residences that meet minimum standards or to live in neighborhoods free from drug trafficking and prostitution or other forms of crime. Blight may spread rapidly, thus, experts must, in a timely manner, analyze its causes, which are essential to preventing and mitigating blight problems. The purpose of this study is to seek an understanding of blight and identify its causal factors. The generic methods commonly applied in previous blight research present limitations that this study aims to overcome by using cognitive mapping and the decision making trial and evaluation laboratory (DEMATEL) technique. This dual methodology provides a more transparent and less restrictive approach for analyzing and complying with the dynamics of cause-and-effect relationships among variables. Group debate involving a panel of specialists in this field identified six causation clusters based on the experts’ experience and knowledge. The resulting framework and its application were validated both by these specialists and the head of the Territorial and Environmental Assessment and Monitoring Division of Cascais City Council Strategic Planning Department, Portugal.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing causes of urban blight using cognitive mapping and DEMATEL

et al. 2022

Self Cite

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“…The Kuyucu and Unsal (2010) shows that the property/tenure structure of an area plays the most important role in the urban transformation projects [26]. As degree of enforcement can prompt people to engage with support services and achieve outcomes [27][28][29][30][31][32][33][34][35][36][37][38], that they themselves consider beneficial.…”

Section: Literature Reviewmentioning

confidence: 99%

The Urban Blight Costs in Taiwan

Fan

et al. 2020

Sustainability

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Urban blight is not only an eyesore for city residents, but also a threat to health, psychological well-being, and safety. It not only represents substantial economic decline, but also spreads through urban space. As well as the loss of personal property value, urban blight also harms public interests in the public domain. This study finds that danger and age are the two main factors of urban blight. Ignoring these two factors causes housing prices to fall. The decline in population due to long-term economic stagnation and the exodus of residents and industries, coupled with the long-term decline in income and spending on maintenance of old houses, has led to major visual and physical economic blight. This investigation adopts the hedonic model to analyze the correspondence of house prices with urban blight, based on real estate prices and related township variables announced by the government in Taiwan in 2017, and applies the spatial regression model to investigate the direct and indirect effects of real estate prices. The following conclusions can be drawn from the analytical results. 1. The spatial lag model finds that urban blight has a spatial spillover effect. 2. The government must not disregard the blight, due to its detrimental effect on housing prices and spatial diffusion effect. 3. The factors that affect the blight are age of residents, age of buildings, poverty, and danger.

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“…9 A review of literature shows that, because of the abilities of FCMs such as fuzzy reasoning and high compatibility and flexibility, FCMs have been successfully used in various fields. [10][11][12][13][14][15][16][17][18] In conventional FCMs (CFCMs), the weights assigned to links remain fixed till the end of the computations (whether in classifying, predicting, control, or other applications). 2 In FCMs based on the opinions of experts, it has been common for experts to express opinions using type-1 (T1) fuzzy sets (FSs) and rules.…”

Section: Introductionmentioning

confidence: 99%

“…The nature of a problem determines the type and the number of FCM nodes; which are often specified by experts. [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] As for control problems, one may use the experiences of control engineers in designing FCMs. 28 However, this approach might not be efficient since one might face the following difficulties (1) not be able to find experts for each control applications, (2) the complexity of the resulting FCM might be too high and thus might not feasible for real-time applications, and (3) the stability of the resulting FCM cannot be shown which is an essential requirement in any control system.…”

Section: Introductionmentioning

confidence: 99%