Urban sprawl has become a very complex process, because it has many factors affecting its directions and values. The study of relative research shows that the driving forces that lead and redirect future urban sprawl require the application of a statistical method. In our study, logistic regressions were used to analyze and class the driving forces for urban sprawl. Identifying the driving forces, which is the most important step in predicting the future of urban sprawl in 2037, was performed using the cellular automata models. This study takes the Aswan area as a case study in the period from 2001 to 2013 by analyzing the official detailed plan and Google Earth historical imagery. Almost all data was prepared for logistic regression analysis using ArcGIS software and IDRISI^® Selva. In our study, a hybrid model of the Markov chain and logistic regression models was applied to identify future urban sprawl in 2037. The findings of this paper simulate the increase in urban area over 24 years from 1.85 to 2.59 km². These findings highlight the growing risks of urban sprawl and the difficulties opposing the sustainable urban development plans officially proposed for this area.

Since the 1950s, The Greater Cairo Metropolitan Region (GCMR) has witnessed an unprecedented rate of urban sprawl that has been mainly concentrated in arable lands against urban planning regulations, and has presented a critical challenge to the urban environment and serious corrosion for arable lands. Thus, the need to identify the driving factor of sprawl is crucial to understand the future of the GCMR urban environment and to overcome the serious challenges of rapid urbanization. We focused on the Giza governorate as a critical area in the GCMR and divided it into three sub-sectors to collect data and analyse. A primary list of driving factors was identified by literature review. Later this list was narrowed down to seven factors after interviews with local urban experts and consideration of the availability of data. Next, a logistic regression analysis was used to evaluate those factors with data derived from existing maps and remotely sensed data for the period of 2004–2013. An operating characteristic (ROC) evaluation of the logistic regression analysis gave high accuracy rates for the entire study area. The findings of the research revealed decreasing significance of the CBD and Nile River as drivers of sprawl. The most significant factors according to the analysis were neighbourhood factors, local urban centres, and accessibility factors of distances to urban uses and major roads. The research suggests more future urban expansion by the existing urban cores and along major roads, leading to more informal urban settlements. It also points to the possibility of persistent deterioration in the urban built environment and agricultural lands. Thus, these findings should be applied to actual urban planning policies, and development regulations should be strengthened to protect the urban environment from further deterioration.

Any urban land-use system is a non-linear, complex and dynamic phenomenon. Dynamic change within a land-use system will be affected by social, cultural, economic, political and natural factors. In many developing countries, like Egypt, uncontrolled urban sprawl and land-use change (US-LUCs), has resulted in an unsustainable urbanization model. This paper employs an integrated model to monitor and analyse the cause of US-LUCs, within a metropolitan region, as a means of projecting future change that can act as a stimulus and catalyst for action. This integrated model is built by combining Markov Chain, Cellular Automata and Logistic Regression. It has been designed to address the weakness of these existing models working on their own, thereby improving the quality of spatio-temporal simulations and predictions of US-LUCs. This integrated model is applied to the monitoring, evaluation and prediction of US-LUCs in the Greater Cairo Metropolitan Region (GCMR) up to 2035.

Around the world, cities are on the front lines of sustainable development. They are responsible for more than 70% of global carbon emissions. Many of these cities are experiencing dangerous levels of pollution, underemployment, and health disparities. Since 2015, 193 countries have endorsed the 17 Sustainable Development Goals (SDGs), intended to help address a wide range of challenges affecting cities and ultimately secure the resources for their next generations. All states are expected to present the national progress towards the SDGs through a Voluntary National Review (VNR). Despite the importance of the cities within this framework, only a handful of them worldwide have actively begun to review and assess progress towards these SDGs on a city scale. This paper seeks to develop a Voluntary Local Review (VLR) framework to assess and evaluate the progress of cities towards contributing to the SDGs. This framework has been developed by localizing the international and national frameworks to measure the performance of cities as they advance towards achieving the SDGs. Such a framework can serve as a tool for benchmarking progress on different aspects of sustainable development and help urban planners and policymakers prioritize policies and actions to improve urban quality of life. This framework is applied to monitor and evaluate the progress of the city of Buraidah in Saudi Arabia, as it strives towards achieving the targets of SDG11 (“Make cities and human settlements inclusive, safe, resilient and sustainable”).