City-region or city? That is the question: modelling sprawl in Isfahan using geospatial data and technology

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Title: City-region or city? That is the question: modelling sprawl in Isfahan using geospatial data and technology
Authors: Rabiei-Dastjerdi, HamidrezaAmini, SaeidMcArdle, GavinHomayouni, Saeid
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Date: 4-Jan-2022
Online since: 2022-03-23T14:47:08Z
Abstract: Urban sprawl is a universal phenomenon and can be seen as a city’s low-density and haphazard development from the centre to suburban areas, and it has different adverse environmental effects at local and regional scales, including increasing the cost of infrastructure. Geospatial data and technology can be used to measure urban sprawl and predict urban expansion. This technology can shed light on the characteristics, causes, and consequences of urban expansion. Unlike other studies, the methodology proposed in this paper works on a regional level rather than an individual city. In this article, Land Use Land Cover changes and the magnitude and direction of city-region sprawl in the Isfahan Metropolitan area were modelled using a multi-temporal analysis of remote sensing imagery. Shannon’s Entropy was used to quantify city-region dispersion during the last fifty years. A Multi-Layer Perceptron Neural Network and Markov Chain Analysis were then used to forecast future city-region sprawl based on past patterns and physical constraints. The results revealed that this region has been suffering from sprawl during this period in different directions. Moreover, it will continue in specific directions due to several economic, political, demographic, environmental, and (urban) planning factors. In addition, the size and speed of city-region sprawl were higher than core city sprawl. The proposed approach can be generalized for other city-regions with a similar spatial structure.
Funding Details: European Commission Horizon 2020
Type of material: Journal Article
Publisher: Springer
Journal: GeoJournal
Copyright (published version): 2021 the Authors
Keywords: City-region sprawlRemote sensingNeural networkMarkov chainIsfahanMaximum likelihood classificationCellular automata modelLand cover changeUrban sprawlSpatiotemporal patternsClimate ChangeMarkov
DOI: 10.1007/s10708-021-10554-8
Language: en
Status of Item: Peer reviewed
ISSN: 0343-2521
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Appears in Collections:Computer Science Research Collection

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