Fuzzy Analysis for Modeling Regional Delineation and Development: The Case of the Sardinian Mining Geopark

Manca, Germana; Curtin, Kevin M.

doi:10.1111/j.1467-9671.2011.01300.x

Cited by 8 publications

(2 citation statements)

References 57 publications

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“…Resilience assessment involves analyses of spatial data associated with urban, environmental, social, economic and institutional data. Such data is neither uniform nor precise [33]. Furthermore, resilience assessment includes verbal inputs from diverse participants with varying expertise, interests and backgrounds.…”

Section: Materials and Methodologymentioning

confidence: 99%

The Resilience Performance Index, a Fuzzy Logic Approach to Assess Urban Resilience

Gaber¹,

El-Kader²,

Okba³

2022

IJSDP

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Urban resilience is recently a prominent issue due to rapid urbanization and increasing challenges and stressors affecting cities. Assessment of urban resilience is an essential step in enhancing resilience performance since regular assessment informs resilience action plans, determines areas of deficiencies, and provides spatial and temporal comparisons. However, resilience assessment is a complex process that requires intensive data and resources due to the multi-dimensional and dynamic nature of resilience, and the imprecision of resilience data. In this context, the research aims to develop The Resilience Performance Index (RPI), through setting a conceptual framework, defining relevant resilience indicators, and finally modelling resilience performance using The Fuzzy Logic Approach, aiming to combine resilience analysis with artificial intelligence (AI) tools and dynamic modelling methods. The RPI assesses both qualitative and quantitative resilience indicators obtained through records, census data or structured questionnaires. Indicators’ values are modelled through a designed fuzzy logic system to obtain the resilience performance score. The developed index is applied on New Damietta city to inform resilience action plans in the Nile Delta region. The RPI addresses the complexity of resilience assessment and ambiguity of resilience data through an easy applicable, user friendly approach without the need for complex mathematical and statistical methods.

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Section: Materials and Methodologymentioning

confidence: 99%

The Resilience Performance Index, a Fuzzy Logic Approach to Assess Urban Resilience

Gaber¹,

El-Kader²,

Okba³

2022

IJSDP

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“…The fuzzy c-means method was also used in the paper by Nowak (2008), the main aim of which was to analyze the diversity level of 45 Polish sub-regions with regard to their economic activity level characterized by means of socio-economic variables. Manca and Curtin (2012), having applied the fuzzy c-means method, distinguished clusters of regions in the area of the Sardinia geopark for the purposes of regional development planning. Economic, social and geographical variables constituted the basis for the conducted classification.…”

Section: Fuzzy Cluster Analysismentioning

confidence: 99%

Analysis of smart specialization in European regions using fuzzy classification

Markowska¹,

Kusterka-Jefmańska²,

Jefmański³

2016

Argumenta Oeconomica

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The major areas of specialization assessment cover for example export, R&D expenditure, employment rate, high-tech products (e.g. patents). Many measures indicated in the subject literature for specified areas cannot be currently determined at regional level due to data unavailability.The article attempts to identify the areas and directions of changes in the smart specialization development of the European regions. An analytical approach, based on the application of fuzzy classification in the analysis of dynamic data, was used for this purpose. The analysis covered 265 European regions at NUTS 2 level, described by variables characterizing employment shares in economic sectors. The level of membership to the separated classes was estimated for each region in particular years of the adopted research period 2000-2010, which facilitated analyzing the directions of their location changes in the European area divided into classes. The obtained results allowed for the positive verification of the proposed hypotheses about the usefulness of fuzzy classification in the identification of areas and directions for smart specialization changes in regions establishing the European regional area. Both, the regions characterized by the changing sector system and the ones maintaining their sector specialization profiles in the adopted research period, were identified in the course of the conducted analyses.

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Utilizing fuzzy set theory to assure the quality of volunteered geographic information

2016

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Fuzzy Analysis for Modeling Regional Delineation and Development: The Case of the Sardinian Mining Geopark

Cited by 8 publications

References 57 publications

The Resilience Performance Index, a Fuzzy Logic Approach to Assess Urban Resilience

The Resilience Performance Index, a Fuzzy Logic Approach to Assess Urban Resilience

Analysis of smart specialization in European regions using fuzzy classification

Utilizing fuzzy set theory to assure the quality of volunteered geographic information

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