Analyzing the Driving Factors Causing Urban Expansion in the Peri-Urban Areas Using Logistic Regression: A Case Study of the Greater Cairo Region

Salem, Muhammad; Naoki, Tsurusaki; Divigalpitiya, Prasanna

doi:10.3390/infrastructures4010004

Cited by 50 publications

(24 citation statements)

References 36 publications

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“…The strong influence of the factor of proximity to main roads on urban expansion in Delhi was similar to its influence in other cities, such as Cairo in Egypt and Atlanta in Georgia [56][57][58]. This result is also in line with previous results presented by Sharma in 2013, who found that the ribbon growth pattern along roads was the dominant pattern of urban expansion when monitoring the urban dynamics in Delhi from 1998 to 2011 [26].…”

Section: The Logistic Regression Analysissupporting

confidence: 89%

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

et al. 2021

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During the last three decades, Delhi has witnessed extensive and rapid urban expansion in all directions, especially in the East South East zone. The total built-up area has risen dramatically, from 195.3 sq. km to 435.1 sq. km, during 1989–2020, which has led to habitat fragmentation, deforestation, and difficulties in running urban utility services effectively in the new extensions. This research aimed to simulate urban expansion in Delhi based on various driving factors using a logistic regression model. The recent urban expansion of Delhi was mapped using LANDSAT images of 1989, 2000, 2010, and 2020. The urban expansion was analyzed using concentric rings to show the urban expansion intensity in each direction. Nine driving factors were analyzed to detect the influence of each factor on the urban expansion process. The results revealed that the proximity to urban areas, proximity to main roads, and proximity to medical facilities were the most significant factors in Delhi during 1989–2020, where they had the highest regression coefficients: −0.884, −0.475, and −0.377, respectively. In addition, the predicted pattern of urban expansion was chaotic, scattered, and dense on the peripheries. This pattern of urban expansion might lead to further losses of natural resources. The relative operating characteristic method was utilized to assess the accuracy of the simulation, and the resulting value of 0.96 proved the validity of the simulation. The results of this research will aid local authorities in recognizing the patterns of future expansion, thus facilitating the implementation of effective policies to achieve sustainable urban development in Delhi.

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Section: The Logistic Regression Analysissupporting

confidence: 89%

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

et al. 2021

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“…Therefore, the urban densities in the peripheral municipalities are considered very high. On the other hand, the quantitative analysis of the crowding data indicated that reason for this decline most probably because of the increasing numbers of the vacant houses in the PUA due to real estate speculation in these areas where the percentages of vacant houses reach 22.7 % of the total number of apartments [59].…”

Section: Built Environmentmentioning

confidence: 99%

Assessing Progress Towards Sustainable Development in the Urban Periphery: A Case of Greater Cairo, Egypt

Salem¹,

Naoki²,

Divigalpitiya³

et al. 2020

IJSDP

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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.

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“…The goodness of the MLR fit is expressed by the relative operating characteristic (ROC) coefficient, according to which values of the coefficient are between 0.5 (random fit) and 1 (perfect fit) (Pontius Jr & Batchu, 2003;Pontius & Schneider, 2001). A multicollinearity test, variance inflation factor (VIF), was performed to verify that there are no driving forces measuring the same phenomenon among all the independent variables (Belsley, 1991;Montgomery & Runger, 1994;Salem et al, 2019). The presence of multicollinearity between the variables is expressed by VIF values greater than 4.…”

Section: Transition Potential and Calibration Processmentioning

confidence: 99%

Modeling urban sprinkling with cellular automata

Saganeiti

Mustafà

Teller

et al. 2021

Sustainable Cities and Society

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This paper presents a spatiotemporal analysis to simulate and project urban sprinkling with coupled cellular automata (CA) and multinomial logistic regression (MLR) model. Our case study, the Basilicata region, south of Italy, is characterized by urban sprinkling-literally "a small amount of urban territory distributed in scattered particles". The region is witnessing a decoupled growth between demographic trend and urban expansion. We applied a coupled approach based on CA and MLR for urban sprinkling modeling and simulation. From three regional building datasets (1989, 1998 and 2013) building density maps were created and used to calibrate and validate the model and to project future urban expansion. Built-up causative factors were identified through an analysis of 19 articles that were compared and discussed according to their main features (methods, case studies, drivers, urbanization dynamics and demographic growth). The transition probability for the first period (1989-1998) was calibrated with MLR for built-up causative factors and with the multiobjective genetic algorithm (MOGA) for CA neighborhood effects. The calibrated model was used to simulate the 2013 urban pattern which was compared with the actual map of 2013 (validation). We then used our calibrated model to simulate urban expansion in 2030. The results of the 2030 forecast show the largest variations in class 1 (low density built-up patches) that correspond to urban sprinkling.

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Analyzing the Driving Factors Causing Urban Expansion in the Peri-Urban Areas Using Logistic Regression: A Case Study of the Greater Cairo Region

Cited by 50 publications

References 36 publications

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

Assessing Progress Towards Sustainable Development in the Urban Periphery: A Case of Greater Cairo, Egypt

Modeling urban sprinkling with cellular automata

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