Analysis and modelling of urban sprawl process and its spatiotemporal patterns in tripoli metropolitan area, Libya

Urban development is a spatially dynamic phenomenon that indicates population increase, expansion of built-up areas, economic growth, increased importance level of cities, and so on. Urban expansion is characterized and affected by the interactions of many factors in time and space at various scales; for instance, political, economic,social, and cultural. In recent years, urban development in developing countries has been faster than that in developed countries. Hence, controlling the urbanization process and creating sustainable development in emerging countries require accurate information about urban expansion processes and their spatial patterns. Moreover,urban expansion (sprawl/growth) analysis using current and historical data is a necessary process in urban spatial studies and future urban planning. Tripoli metropolis, which has not been studied before, was chosen as the area in which to conduct this research, discover its urban sprawl patterns, and assess well-established urban modeling techniques in such North African city. Several data were used to conduct spatial analysis, modeling, and predictions for urban expansion such as Land-Sat image 1984, Land-Sat image 1996, Spot 5 image 2002, Spot 5 image 2010,road networks, population data, digital contour map, and topographic map. The general aim of this research is to spatiotemporally analyze, assess, and simulate the urban sprawl of Tripoli metropolis by using remotely sensed data through geographic information systems GIS. This study adopted several techniques to investigate,analyze, and assess urban sprawl as a process and as a pattern from different perspectives(such as overall urban development, urban expansion in each district,urban expansion direction, urban expansion in time periods and variations) to provide a comprehensive picture about urban expansion situations in the study area. The techniques used for urban sprawl detection and assessment were as follows: (1) Built-up area and population measures, (2) Comparison of observed and theoretical expected built-up area expansion,(3) Urban Expansion Intensity Index,(4) Shannon’s entropy model, (5) Degree of freedom model, and (6) Landscape metrics. In the context of urban sprawl analysis, this research also presents a perspective that considers the effect of growth direction and distance from the central business district (CBD). To simulate the urban sprawl process and predict probable sprawl patterns in the future, several modeling approaches were applied, including (1) bivariate statistical model of Frequency Ratio (FR), (2) a novel bivariate statistical urban model of Evidential Belief Functions (EBF), (3) Multivariate Logistic Regression (LR) Model, and (4) Common Integrated Cellular Automata Markov Chain (CA-Markov) model. Furthermore, this study proposed a novel integrated hybrid model based on Multivariate Decision-Tree-based Chi-squared Automatic Interaction Detection (CHAID-DT), Markov Chain (MC), and Cellular Automata (CA) models. Results showed that the FR and EBF models provide good understanding of the role of every class within each individual urban driving factor in the urban sprawl progression process. The approach used in both models was based on net real urban expansion rather than on the whole built-up area, and it successfully reflected the urban sprawl behaviors and the dynamicity of the urbanization process. The overall influence of each urban driving factor affecting the sprawl in Tripoli was also effectively determined by the LR model. However, modeling results showed that the main drawback of the FR, EBF, and LR models is that they cannot determine exactly where the urban expansion will occur and its quantity. The common CA–Markov chain model predicted the future quantitative demand of urbanized areas and explicitly presented the spatial patterns of urban land-use changes, but it could not consider the urban driving factors. On the contrary, the novel hybrid model incorporated many urban driving factors, such as social, economic, and biophysical factors, and the modeling results clearly explained the interactions of urban factors and probable urban expansions. Moreover, the proposed integrated model overcame the shortcomings of each individual model. The well-known robust Relative Operating Characteristic (ROC) technique was used to validate all the generated probability maps. The validation results of ROC for the applied models of FR, EBF,LR, and CHAID-DT were 84.5%, 83.2%, 86%, and 94.9%, respectively. Kappa statistic index of agreement technique was also used to check the validity of the CA– Markov chain model and the proposed hybrid models in terms of quantity and location. The validation demonstrated the following accuracy levels: Kappa standard index of 0.8584, Kappa location index of 0.886, and Kappa no index of 0.881 for the CA–Markov chain model, while the novel hybrid models had a Kappa standard index of 0.8941, Kappa location index of 0.9227, and Kappa no index of 0.9110. Finally, this research provided a comprehensive spatiotemporal analysis and simulation of urban sprawl from different aspects in Tripoli metropolitan area, Libya. The techniques used and their outcomes provide a good understanding of urban sprawl in Tripoli and will enrich the literature on spatial urban studies as well as urban sprawl modeling and analysis. The proposed hybrid model shows robust behavior in urban simulation and a higher accuracy level than that of currently used models.

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