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Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods

Department of Geomatics Engineering, Sivas Cumhuriyet University, 58140 Sivas, Turkey
Sustainability 2019, 11(20), 5579; https://doi.org/10.3390/su11205579
Received: 18 September 2019 / Revised: 7 October 2019 / Accepted: 7 October 2019 / Published: 10 October 2019
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.
Keywords: geostatistical modeling; exploratory factor analysis; urban geography; urban growth; simulation geostatistical modeling; exploratory factor analysis; urban geography; urban growth; simulation
MDPI and ACS Style

Ayazli, I.E. Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods. Sustainability 2019, 11, 5579.

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