Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods

While the rural population is decreasing day by day, the urban population is increasing rapidly. Urban growth, which occurs as a result of this increase, is sprawling toward natural and environmental areas in urban fringes, and constitutes the main source of many environmental, physical, social, and...

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Main Author: Ismail Ercument Ayazli
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/11/20/5579
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spelling doaj-ecb8e1c85d384ae7ad35f1ba06c0023d2020-11-25T02:03:41ZengMDPI AGSustainability2071-10502019-10-011120557910.3390/su11205579su11205579Monitoring of Urban Growth with Improved Model Accuracy by Statistical MethodsIsmail Ercument Ayazli0Department of Geomatics Engineering, Sivas Cumhuriyet University, 58140 Sivas, TurkeyWhile the rural population is decreasing day by day, the urban population is increasing rapidly. Urban growth, which occurs as a result of this increase, is sprawling toward natural and environmental areas in urban fringes, and constitutes the main source of many environmental, physical, social, and economic problems. In order to overcome these problems, the direction and rate of urban growth should be determined with simulation models. In this context, many urban growth models have been developed since the 1990s; the SLEUTH urban growth model is one of the most popular among them and has been used in many projects around the world. The brute force calibration process in which the best fit values of growth coefficients are determined is the most important stage of simulation models. The coefficient ranges are initially defined as being between 0 and 100 and are then narrowed in this step according to 13 separate regression scores, which are used to specify the characterization of urban growth. Consensus has not yet been reached as to which metrics should be used for calculating the best fit values, but the Lee−Sallee and Optimum SLEUTH Metric (OSM) methods have been mostly used in past studies. However, in rapidly growing study areas, these methods cannot truly explain urban growth properties. The main purpose of this paper is to precisely calibrate urban growth simulation models. Therefore, Exploratory Factor Analysis (EFA) was used to calculate the growth coefficients, as a new statistical approach for calibration, in this study. The district of Sancaktepe, Istanbul, which experienced population growth of 80% between 2008 and 2018, was selected as the study area in order to test the achievement of the EFA method, and two urban growth simulation models were generated for the years 2030 and 2050. According to the results, despite the fact that there is little effect of urban growth in the short term, more than 70% of forests and agricultural lands are at risk of urbanization by 2050.https://www.mdpi.com/2071-1050/11/20/5579geostatistical modelingexploratory factor analysisurban geographyurban growthsimulation
collection DOAJ
language English
format Article
sources DOAJ
author Ismail Ercument Ayazli
spellingShingle Ismail Ercument Ayazli
Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods
Sustainability
geostatistical modeling
exploratory factor analysis
urban geography
urban growth
simulation
author_facet Ismail Ercument Ayazli
author_sort Ismail Ercument Ayazli
title Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods
title_short Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods
title_full Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods
title_fullStr Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods
title_full_unstemmed Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods
title_sort monitoring of urban growth with improved model accuracy by statistical methods
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2019-10-01
description While the rural population is decreasing day by day, the urban population is increasing rapidly. Urban growth, which occurs as a result of this increase, is sprawling toward natural and environmental areas in urban fringes, and constitutes the main source of many environmental, physical, social, and economic problems. In order to overcome these problems, the direction and rate of urban growth should be determined with simulation models. In this context, many urban growth models have been developed since the 1990s; the SLEUTH urban growth model is one of the most popular among them and has been used in many projects around the world. The brute force calibration process in which the best fit values of growth coefficients are determined is the most important stage of simulation models. The coefficient ranges are initially defined as being between 0 and 100 and are then narrowed in this step according to 13 separate regression scores, which are used to specify the characterization of urban growth. Consensus has not yet been reached as to which metrics should be used for calculating the best fit values, but the Lee−Sallee and Optimum SLEUTH Metric (OSM) methods have been mostly used in past studies. However, in rapidly growing study areas, these methods cannot truly explain urban growth properties. The main purpose of this paper is to precisely calibrate urban growth simulation models. Therefore, Exploratory Factor Analysis (EFA) was used to calculate the growth coefficients, as a new statistical approach for calibration, in this study. The district of Sancaktepe, Istanbul, which experienced population growth of 80% between 2008 and 2018, was selected as the study area in order to test the achievement of the EFA method, and two urban growth simulation models were generated for the years 2030 and 2050. According to the results, despite the fact that there is little effect of urban growth in the short term, more than 70% of forests and agricultural lands are at risk of urbanization by 2050.
topic geostatistical modeling
exploratory factor analysis
urban geography
urban growth
simulation
url https://www.mdpi.com/2071-1050/11/20/5579
work_keys_str_mv AT ismailercumentayazli monitoringofurbangrowthwithimprovedmodelaccuracybystatisticalmethods
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