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

Abstract

The present study aimed to investigate different socioeconomic factors as well as the perceptions and travel behaviors associated with urban sprawl in two cities of different sizes in Iran, as a developing country in the Middle East. Four Weighted Least Squares (WLS) regression models were developed for Hamedan and Nowshahr, as examples of large and small cities in Iran, respectively. The findings showed different correlations related to urban sprawl between Iranian cities and high-income countries in terms of socioeconomic and travel behavior determinants. Urban sprawl around home in Hamedan was positively correlated with the number of cars and driving licenses in households, the use of a private car for trips, and less use of public transport. Urban sprawl around homes in Nowshahr was related to an increased number of cars, the use of private cars for non-commuting trips, less sense of belonging to the neighborhood, and lower income. Additionally, urban sprawl around workplaces was correlated with main daily activity, number of non-commuting trips, mode of choice for non-commuting, and residential location choice in Hamedan a swell as monthly income, daily shopping area, frequency of public transport use, quality of recreational facilities, length of time for living in the current home, and commuting distance in Nowshahr.

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

“…Twelve independent factors were derived based on previous literature and discussion with experts to be included in the model [4,14,15,37,40,46,47]. Then, two factors (economic activities and urban master plans) were excluded from the study due to a lack of data for the study area.…”

“…Therefore, a number of studies have focused on examining and analyzing the driving factors behind urban expansion in many cities [11][12][13]. A literature review has shown that the driving forces of urban expansion vary from city to city [4,14,15] in both the Global South and the Global North. In the Global South, for instance, Salem et al found that the population density and the proximity to main roads are the most significant factors affecting urban expansion in Cairo, Egypt [16].…”

The Relations between Street Network Configuration and Travel Behavior in Pakistan; the Optimal Level of Street Connectivity for a More Active Mobility

“…Molaei et al (2021) found a positive correlation between the variables of street connectivity and walking time in Rasht, Iran [28]. Mehriar et al (2020) looked at two differently-sized cities in Iran and showed correlations of urban sprawl and street-length density with travel behavior [29]. Asheampong (2020) investigated the spatial structure of urban forms and its relationship with travel patterns in metropolitan areas of Kumasi, Ghana; the result showed a strong relationship between urban forms (compact vs. sprawled) and the use of active modes of transport in commuting trips [30].…”

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

“…On a regional scale, when studying the reasons for low occupancy rates in the new cities in Egypt, it has been revealed that the six factors of current inhabitants, the estimated size of the target group, the size of new cities, total number of housing units, distance to nearby old city core, and distance to Greater Cairo are correlated with nation-wide location choices [17]. We also know that residential location choice is positively correlated with urban sprawl around the workplaces of people (quantified by Shannon Entropy) in the large city of Hamedan, Iran [18]. Choice of house location due to nearby workplace is significantly correlated with the level of urban sprawl (higher Shannon Entropy values) around workplaces.…”

“…Most of the studies on the region have utilized statistical analysis methods determining either aggregate or disaggregate data, but they are limited by the data derived from questionnaires e.g., [15][16][17]. An exception is, e.g., the work of Mehriar et al (2020), who quantified the street network configuration and connectivity and brought the related variables into their models [18]. Nevertheless, their models still did not target residential self-selection precisely, since this variable was only an independent one, of which the correlation with urban sprawl was measured.…”