Urban Sprawl, Socioeconomic Features, and Travel Patterns in Middle East Countries: A Case Study in Iran

Prediction of Urban Sprawl by Integrating Socioeconomic Factors in the Batticaloa Municipal Council, Sri Lanka

Due to extensive population growth, urbanization increases urban development and sprawl in the world’s cities. Urban sprawl is a socioeconomic phenomenon that has not extensively incorporated socioeconomic factors in the prediction of most of the urban sprawl models. This study aimed to predict the urban sprawl pattern in 2030 by integrating socioeconomic and biophysical factors. NDBI, Cramer’s V, logistic regression, and CA-Markov analyses were used to classify and predict built-up patterns. The built-up area is the dominant land use, which had a gradual growth from 1990 to 2020. A total of 20 socioeconomic and biophysical factors were identified as potentials in the municipality, affecting the urban sprawl. Policy regulation was the most attractive driver with a positive association, and land value had a high inverse association. Three prediction scenarios for urban sprawl were achieved for 2030. Higher sprawling growth is expected in scenario 3, compared with scenarios 1 and 2. Scenario 3 was simulated with biophysical and socioeconomic factors. This study aids in addressing urban sprawl at different spatial and temporal scales and helps urban planners and decision makers enhance the development strategies in the municipality. Predicted maps with different scenarios can support evaluating future sprawling growth and be used to develop sustainable planning for the city.

The Neighborhood Effect on Keeping Non-Commuting Journeys within Compact and Sprawled Districts

The neighborhood effect on keeping non-commuting trips inside neighborhoods has not yet been investigated in developing countries. The modeling of non-commuting trips inside neighborhoods helps understand how to avoid unnecessary journeys by car into different parts of the city. This paper, therefore, attempts to clarify (1) the similarities and differences in the socioeconomic characteristics and the perceptions of people in sprawled and compact neighborhoods, (2) correlations between, on the one hand, the choice of destinations of non-commuting trips for shopping and entertainment activities and, on the other, the socioeconomic features, travel behavior, and perceptions of residents in the two large Pakistani cities of Lahore and Rawalpindi, (3) the similarities and differences in the determinants of non-commuting destinations inside neighborhoods in compact and sprawled districts. The paper develops four Binary Logistic (BL) regression models, with two models for each type of neighborhood. The findings show that trips to shopping areas inside compact districts are correlated with a sense of belonging to the neighborhood, frequency of public transport use, residential location, and mode choice of non-commuting trips to destinations both inside and outside the neighborhood. On the other hand, the number of non-commuting trips, mode choice for non-commuting trips outside the neighborhood, frequency of public transport use, the attractiveness of shops, and monthly income (please see the Note) are significant determinants for trips to the shopping area in sprawled districts. Age, gender, possession of a driver’s license, income, number of non-commuting trips, mode choice for non-commuting trips outside of the neighborhood, car ownership, and attractiveness of shops in a neighborhood are correlated with trips to entertainment locations inside the neighborhood in compact districts. Finally, the attractiveness of shops, quality of social and recreational facilities, a sense of belonging to a neighborhood, choice of residential location, gender, age, possession of a driver’s license, number of cars in the household, and income are determinants of trips to entertainment locations in sprawled districts. A chi-square test confirms the differences across gender, daily activity, monthly income, frequency of public transport use, residential location choice, and the quality of social and recreational facilities for sprawled and compact districts in Pakistan.

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

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.

Residential Location Choice in Istanbul, Tehran, and Cairo: The Importance of Commuting to Work

The determinants of residential location choice have not been investigated in many developing countries. This paper examines this topic, including the influence of urban travels on house location decision-making in the Middle East and North Africa (MENA). Based on 8284 face-to-face interviews in Istanbul, Tehran, and Cairo, the dummy variable of residential location choice, including two categories of mobility reasons and other factors, was modeled by binary probit regression modeling. By means of receiver-operating characteristic analysis, the cutoff value of commuting distance and the time passed from the last relocation was estimated. Finally, the significant difference between the value of these two variables for people with different house location reasons were tested by Mann–Whitney U-test. The results show that the eight variables of shopping-entertainment mode choice in faraway places, frequency of public transit trips, neighborhood attractiveness perception, age, number of driving licenses in household, commuting distance, number of accessed facilities, and the (walkable) accessibility of facilities influence the residential self-selections. People who chose their current home based on mobility commute a daily mean distance of 8596 m and relocated less than 15.5 years ago, while those who chose their home based on other reasons, such as socioeconomics or personal reasons, commute longer and moved to a new house more than 15.5 years ago. This shows how the attitudes of people about residential location have changed in the MENA region, but there are still contextual differences to high-income countries.

Correlation between Land Use and the Transformation of Rural Housing Model in the Coastal Region of Syria

The phenomenon of urban sprawl has caused radical changes in the spatial structure of cities and rural areas all around the world. Syria is among the developing countries that have experienced this phenomenon. Some of the resulted processes of urban sprawl like urbanization and counter-urbanization have had a clear impact on the land use and lifestyle in both cities and the countryside of different regions in Syria. This research focuses on the coastal region and the spatial changes that affected the nature of social life, such as the rapid growth of the population, the expansion of cities, and the new developments, which in turn have led to considerable changes in the relationship and scale of the house, garden, and landscape. The research studies the development of the rural housing model in the coastal region and its relation to the surrounding landscape. It tracks three phases of the housing unit’s development and conducts a comparative study on four villages using a questionnaire to evaluate the performance of those units. The results of this research show significant change in the relationship between rural and urban areas resulting from the new residential developments, as well as the relationship of land use and the historic plot structure and that of the garden and the house into the overall character of the landscape.