Muhammad Salem

During the last few decades, sustainable development (SD) has increasingly received attention globally. Therefore, international organizations and researchers sought to assess progress towards SD at different territorial levels. However, most of the studies were conducted at the city level and a very small number of studies has conducted at the urban periphery territory. This study aims to fill the current research gap through assessing the progress towards SD in the urban periphery of Greater Cairo (GC) in Egypt between 1996-2017. Eight composite indicators have been employed to assess the progress towards SD in this territory. These composite indicators were constructed based on the 14 individual indicators associated with sustainable development goals. The results showed meaningful progress achieved in the peripheral municipalities of GC, particularly in infrastructure and education indicators, while the economic and environmental indicators have deteriorated, particularly after the civic revolution of 2011. In addition, the study found a development gap between the urban periphery and the main urban agglomeration in GC, particularly in the infrastructure aspect. These results highlight the deficiencies that exist in the urban periphery of GC which help decision-makers to prepare appropriate policies to improve SD in such territory.

ABSTRACT Land use/land cover (LULC) has changed dramatically in the peri-urban area (PUA) of greater Cairo (GC) since the Egyptian revolution of 2011. This study analyzes LULC change in the PUA of GC using two Landsat images from 2010 and 2018. The spatial trends of LULC change and visualizations of the gains and losses in LULC were analyzed using TerrSet software. The driving forces of LULC change from 2010?2018 were quantified using the logistic regression model (LRM). The results revealed that the processes of LULC change and urban sprawl have directly impacted the natural resources, particularly agricultural land. In addition, the study showed that the population growth and land value have the highest regression coefficients, 0.660 and 0.292, respectively, and were the most significant driving forces of LULC from 2010?2018. The outcomes of this study are important for decision-makers to adopt appropriate strategies for sustainable land use in this area.

Over the last four decades, the loss of agricultural land has been observed in Egypt at high rates. However, the highest rates of losses have occurred since the January 25th revolution in 2011. Greater Cairo (GC), which is the largest metropolitan in Egypt, has witnessed a massive loss of agricultural land since the 2011 revolution. However, until now, no study or official report has revealed the volume of agricultural land losses in this region. This study quantifies agricultural land losses around the GC using Landsat satellite images from 2010 and 2018. Supervised classification was performed using the maximum likelihood algorithm in QGIS software. A post-classification comparison method was applied to detect the land use/land cover changes between the classified images; then, the loss of agricultural land was quantified using Arc GIS software. Visualizations of the gains and losses in agricultural land and the spatial trends of agricultural land losses were created using TerrSet software. The results show that 9600 ha of agricultural land were converted to urban use during 2010–2018, which means that the annual rate of agricultural land loss has tripled and now reaches approximately 1200 ha per year. Decay of executive authority, rapid population growth, real estate market speculation and fragmentation of agricultural land were the main driving factors of agricultural land losses during this period. The results of this research may help decision makers understand the current high rate of agricultural land loss. Hence, appropriate strategies may be adopted to prevent future losses of this valuable land.

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Slums are a global urban challenge, particularly in big cities in most developing countries where they are growing faster than governments control. However, detection of slums is a big challenge for such countries due to fast growing there and difficulty of field survey. To address this challenge, this study uses a novel method to detect slums from very high-resolution (VHR) satellite images using machine learning algorithms and roads network derived from OpenStreetMap. This method has been applied to Fustat Area in the center of Cairo, Egypt where slums highly exist. The result of this study has detected eight slums with areas that ranged from 2.4 ha to 28.3 ha. The accuracy of the result has been verified by the kappa index which showed a high accuracy of 0.93. The results of this study are important for planners and decision makers to help them in developing such areas.

The peri-urban area (PUA) of the Greater Cairo Region (GCR) in Egypt has witnessed a rapid urban expansion during the last few years. This urban expansion has led to the loss of wide, areas of agriculture lands and the annexation of many peripheral villages into the boundary of the GCR. This study analyzed the driving factors causing the urban expansion in the GCR during the period 2007–2017 using the logistic regression model (LRM). Eight independent variables were applied in this model: distance to the nearest urban center, distance to the nearest center of regional services, distance to water streams, distance to the main agglomeration, distance to industrial areas, distance to nearest road, number of urban cells within a 3 × 3 cell window and population density. The analysis was conducted using LOGISTICREG module in Terrset software. This research showed that the population density and distance to the nearest road have the highest regression coefficients, 0.540 and 0.114, respectively, and were the most significant driving factors of urban expansion during the last 10 years (2007–2017). Moreover, based on the results of the LRM, a probability map of urban expansion in the PUA was created, which shows that most urban expansion would be around the existing urban areas and near roads. The relative operating characteristic (ROC) value of 0.93 indicates that the probability map of urban expansion is valid.

1 ABSTRACT Since the early 1980s, the Greater Cairo Metropolitan Region (GCMR) has witnessed a rapid urban expansion that has been mainly concentrated in the peri-urban areas (PUAs). Most of this expansion was against urban planning laws and has presented a critical challenge to the urban environment. It has also led to spatial fragmentation and loss of enormous agriculture lands. This research analyses the urban expansion in the PUAs of the GCMR, during the period (2001-2017) using GIS and remote sensing. In addition to presenting a set of driving factors of this expansion, which were extracted from the literature review and previous studies. The results of this research show that the urban expansion rate during the mentioned period reached to 461 hectares per year. Moreover, the population growth and accessibility were the most significant driving factors in the PUA of the GCR.

The Greater Cairo Metropolitan Region (GCMR) in its seeking to sustainable development (SD) by the year of 2050 facing the serious challenge of around 65 percent of Cairenes live in unplanned settlements. In this respect, the authors examined the effect of urban characteristics of unplanned settlements on SD in the Egyptian context, focusing on the type of unplanned growth on agricultural land. The output of the analysis were fourfold. First of all, we provide a brief overview of previous research on the main types of unplanned settlements in GCMR and the sustainability definition according to the Egyptian context. Secondly, we had a discussion with the local government during our field survey in GCMR to determine the study samples, the main urban characteristics, and the sustainability evaluation criteria in the Egyptian context. Thirdly, through the comparative analysis and geographic information system (GIS), we examined how the character of urban development affected per capita four urban measures in a cross-section of two settlements, one represented the unplanned settlements and other as a comparative planned sample to determine the real gap. Finally, by using the evaluation matrix, the help and block items are estimated for each measure of urban characteristics, providing substantive evidence on how the four measures of urban characteristics have been affected by the urban sprawl.