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ISPRS Int. J. Geo-Inf. 2016, 5(8), 139; doi:10.3390/ijgi5080139

Spatiotemporal Modeling of Urban Growth Predictions Based on Driving Force Factors in Five Saudi Arabian Cities

School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia
Department of Geography, Umm Al-Qura University, Makkah 21955, Saudi Arabia
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 6 July 2016 / Revised: 1 August 2016 / Accepted: 2 August 2016 / Published: 8 August 2016
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This paper investigates the effect of four driving forces, including elevation, slope, distance to drainage and distance to major roads, on urban expansion in five Saudi Arabian cities: Riyadh, Jeddah, Makkah, Al-Taif and Eastern Area. The prediction of urban probabilities in the selected cities based on the four driving forces is generated using a logistic regression model for two time periods of urban change in 1985 and 2014. The validation of the model was tested using two approaches. The first approach was a quantitative analysis by using the Relative Operating Characteristic (ROC) method. The second approach was a qualitative analysis in which the probable urban growth maps based on urban changes in 1985 is used to test the performance of the model to predict the probable urban growth after 2014 by comparing the probable maps of 1985 and the actual urban growth of 2014. The results indicate that the prediction model of 2014 provides a reliable and consistent prediction based on the performance of 1985. The analysis of driving forces shows variable effects over time. Variables such as elevation, slope and road distance had significant effects on the selected cities. However, distance to major roads was the factor with the most impact to determine the urban form in all five cites in both 1985 and 2014. View Full-Text
Keywords: urban probability; driving forces; logistic regression; GIS; remote sensing urban probability; driving forces; logistic regression; GIS; remote sensing

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Alqurashi, A.F.; Kumar, L.; Al-Ghamdi, K.A. Spatiotemporal Modeling of Urban Growth Predictions Based on Driving Force Factors in Five Saudi Arabian Cities. ISPRS Int. J. Geo-Inf. 2016, 5, 139.

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