Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/19480
Title: | Spatiotemporal Modeling of Urban Growth Predictions Based on Driving Force Factors in Five Saudi Arabian Cities | Contributor(s): | Alqurashi, Abdullah (author); Kumar, Lalit (author) ; Al-Ghamdi, Khalid (author) | Publication Date: | 2016 | Open Access: | Yes | DOI: | 10.3390/ijgi5080139 | Handle Link: | https://hdl.handle.net/1959.11/19480 | Abstract: | 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. | Publication Type: | Journal Article | Source of Publication: | ISPRS International Journal of Geo-Information, 5(8), p. 1-19 | Publisher: | MDPI AG | Place of Publication: | Switzerland | ISSN: | 2220-9964 | Fields of Research (FoR) 2008: | 090903 Geospatial Information Systems 090905 Photogrammetry and Remote Sensing |
Fields of Research (FoR) 2020: | 401302 Geospatial information systems and geospatial data modelling 401304 Photogrammetry and remote sensing |
Socio-Economic Objective (SEO) 2008: | 960610 Urban Land Evaluation 960911 Urban and Industrial Land Management |
Socio-Economic Objective (SEO) 2020: | 180603 Evaluation, allocation, and impacts of land use | Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Journal Article |
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