Street connectivity and active mobility in emerging economies: disparities of socioeconomic features and travel behavior in sprawling versus compact urban neighbourhoods [original]

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Street connectivity and active mobility in emerging

economies: disparities of socioeconomic features

and travel behavior in sprawling versus compact

urban neighbourhoods

Melika Mehriar, Houshmand Masoumi & Inmaculada Mohino

To cite this article: Melika Mehriar, Houshmand Masoumi & Inmaculada Mohino (30

Aug 2024): Street connectivity and active mobility in emerging economies: disparities

of socioeconomic features and travel behavior in sprawling versus compact urban

neighbourhoods, International Planning Studies, DOI: 10.1080/13563475.2024.2393141

To link to this article: https://doi.org/10.1080/13563475.2024.2393141

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Street connectivity and active mobility in emerging economies:

disparities of socioeconomic features and travel behavior in

sprawling versus compact urban neighbourhoods

Melika Mehriar

, Houshmand Masoumi

a,b

and Inmaculada Mohino

Zentrum Technik und Gesellschaft, Technische Universität Berlin, Berlin, Germany;

Department of Transport and

Supply Chain Management, College of Business and Economics, University of Johannesburg, Johannesburg, South

Africa;

LoCUS Interdisciplinary Lab on Complex Urban & Regional Spatial Processes, Department of City and

Regional Planning, School of Architecture, Universidad Politécnica de Madrid, Madrid, Spain

ABSTRACT

Our knowledge of urban sprawl and its relationship with urban mode

choices in the context of developing countries is limited. The present

study aims to evaluate the influence of travel behaviour, socioeconomic

features of residents in sprawling and compact areas as two different

types of neighbourhoods Hamedan and Nowshahr, Iran. Also, this study

analysed the relationship between street connectivity with the use of

active mobility. Ordinary least squares (OLS) regression models were

generated for trips in these two cities, which is complemented by chi-

squared and Kruskal–Wallis tests to evaluate the similarities and

dissimilarities of travel behaviour and other socioeconomic

characteristics among the residents of compact and sprawling

neighbourhoods. A significant relationship was observed between street

connectivity and active mobility in all kinds of commuting and non-

commuting trips inside and outside of neighbourhoods when age,

gender, car ownership, and monthly income are controlled in the

models of the two cities.

ARTICLE HISTORY

Received 10 February 2022

Accepted 23 July 2024

KEYWORDS

Sprawling and compact

neighbourhoods; personal/

household characteristics;

travel behaviour; street

connectivity; active mobility

1. Introduction

Urban sprawl is recognized as a specific urban form driven by urbanization and its demand for

combining natural and artificial aspects of life. Sprawl characteristics include fragmented and dis-

connected development areas beyond cities, low-density areas with vacant lots within built-up

areas, long distances to other parts of cities, areas relying on cars and motorized transport

modes, and domination of single land uses such as residential areas separated from workplaces

and entertainment activities (Bengston, Fletcher, and Nelson 2004; Bhatta, Saraswati, and Bandyo-

padhyay 2010; Brueckner 2000; Bruegmann 2005; Cutsinger et al. 2005; Ewing 1997; Galster et al.

2001, 2004; Jaeger et al. 2010). The process of urbanization has altered land expansion patterns

worldwide, and its rapid spread has emerged as a new challenge, particularly for urban planners

and decision-makers. Recently, urban form and its mutual interdependences with other systems

in cities has become a controversial topic in literature on urban issues (Zhu and Leibowicz

2020). Urban It is crucial to enhance cities as liveable spaces for all, aiming to achieve sustainability

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this

article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

CONTACT Melika Mehriar [email protected] Zentrum Technik und Gesellschaft, Technische Universität Berlin,

Kaiserin-Augusta-Alle. 104, Berlin, 10553, Germany

INTERNATIONAL PLANNING STUDIES

https://doi.org/10.1080/13563475.2024.2393141

goals that encompass a higher quality of life, sustainable transportation options, ample open spaces

for social interactions, reduced greenhouse gas emissions, minimal conversion of agricultural and

green lands into built-up areas, decreased time spent in traffic congestion, and decreased social and

ethnic divisions. Tackling the current problems of urban spaces and creating sustainable cities

require an understanding of socioeconomic structures, perceptions of residents concerning their

living and working spaces, and their daily travel habits. The patterns and structures of urban sprawl,

as a particular form of urban development, differ in various parts of the world. Consequently, there

is limited consensus on its definition, measurement, and relationship to other structures and

phenomena. The contextual grasp of the relationship between urban sprawl and urban travel behav-

iour, demography, and perceptions of residents is particularly limited and unclear with respect to

Middle Eastern cities.

Iranian cities have confronted transformation since 1920 (Masoumi 2014). The process of mod-

ernization and the arrival of automobiles played crucial roles in reshaping the structures of Iranian

cities. Traditional Iranian cities had human-oriented neighbourhoods. Residential neighbourhoods

have facilities like mosques, bakeries, grocery stores, and primary schools in some cases. As a result,

traditional cities, especially the main cores of Iranian cities, were not designed for automobiles.

Masoumi, Hosseini, and Gouda (2018) studied the urban-form typologies of large cities in the

Middle East and North Africa (MENA) and discussed the two most important top-down interven-

tions in the urban form of cities, the first of which occurred in the 1930s when the government tried

to provide wider street networks, thereby cutting the traditional fabric. The next intervention hap-

pened in the 1970s and 1980s when cities faced rapid urbanization and urban sprawl (Masoumi,

Terzi, and Serag 2019).

The present study aims to provide insights into street networks and compact forms of neigh-

bourhoods to promote active mobility. In other words, this paper focuses on exploring the relation-

ship between street connectivity, urban forms and travel behaviour in Middle Eastern cities to

achieve sustainable development. Additionally, it compares different urban topologies of compact

and sprawled neighbourhoods based on demographic and economic characteristics, travel patterns,

and residents’ perceptions in less-studied areas to understand how these differences manifest in var-

ious urban forms.

Furthermore, this study compared contextual results in the relationship between different urban

forms such as patterns of land use and street networks to travel patterns, especially the relationship

between living in sprawl areas and using active mobility such as cycling and walking in daily com-

muting and non-commuting trips, as well as different structures of personal/household character-

istics including gender, age, having job and driving licence, income, and the number of cars for

household in sprawled-out and compact urban forms. This can help researchers to gain a deeper

understanding of urban forms in different socioeconomic contexts, which can pave the way for

decision-makers and urban planners to implement strategies based on the relevant urban context.

In other words, this paper aims to find the relationship between connectivity, biking, and walking as

a dominant mode choice in compact and sprawled areas to promote active mobility as a sustainable

mode choice. Thus, the understanding about street connectivity and its relationship with active

mobility can give better insights to urban planners and policy-makers for considering connectivity

as an influential factor in new designs and regeneration plans. Nevertheless, the main contribution

of this paper is to analyse the correlation of intersection density as an indicator for street network

connectivity (Chin et al. 2008; Hamidi and Ewing 2020; Handy et al. 2002) with active mobility and

compare personal/household characteristics of different urban forms in less or non-studied con-

texts. Although previous studies on urban sprawl in developing countries have only analysed the

phenomenon, the present study focused on evaluating socio-demographic characteristics and beha-

viours among residents in compact versus sprawling neighbourhoods for the purpose of determin-

ing the characteristics related to different types of neighbourhood in Middle Eastern cities. The

physical structures of neighbourhoods such as street networks may be related to residents’ mobility

behaviour such as using sustainable modes of transit for commuting and non-commuting purposes.

2 M. MEHRIAR ET AL.

Although some studies have highlighted the relationship between street connectivity and urban

mobility (Blitz and Lanzendorf 2020; Hamidi and Ewing 2020; Koohsari et al. 2017; Rosso, Auchin-

closs, and Michael 2011; Thao and Ohnmacht 2020), little attention has been paid to the shortcom-

ings in the developing countries. In this study, intersection density was used as a variable for

assessing street connectivity. Intersection density is a relatively new variable used in a few studies

to assess street connectivity in the developed countries (Stangel and Guinn 2011). This method is

less common in the studies of Middle Eastern cities, and its relationship to active mobility has less

been considered in different studies. Considering different socioeconomic and geographical con-

texts allows decision-makers and urban planners to create efficient policies and strategies according

to the real needs and conditions in their context, rather than simply borrowing general attitudes

from the developed countries. To tackle the problems related to urban sprawl and achieve sustain-

able development objectives, urban planners and decision-makers should have a better understand-

ing of urban sprawl and its relation to street connectivity, land use structure, socioeconomics, and

residents’ perceptions in different socioeconomic contexts.

The remainder of this paper is as follows. Section 2 gives a brief literature review of the studies,

findings, and statistical methods concerning the characteristics of urban forms and their built-up

environment, and how they are interrelated to personal/household characteristics and urban travel

habits in both developed and developing countries. Section 3 presents the methodology. The results

obtained from the statistical methods are explained in Section 4. The findings are compared with

those of the previous literature to highlight the importance of different socioeconomic contexts

in Section 5. Finally, a sketch of similar and different characteristics of residents in different

urban forms and the relationship between street connectivity and active mobility in Iranian cities

as the case studies of Middle Eastern cities.

2. Literature review

Although studies on urban sprawl as a specific urban form and its relationship to transportation

mode choice, urban mobility, and socioeconomic features have been conducted in high-income

countries, knowledge in this area is limited for emerging economies. Urban scholars have discussed

various characteristics of urban form, such as fragmentation or compactness, density, multiple or

single land-use areas, concentration of facilities, street connectivity, walkable distances, design of

neighbourhoods and the impacts of these on sustainable development in developed countries

(Kaza 2020; Shi et al. 2020; Vijayakumar and Sangeetha 2021). Kaza (2020) assessed the relationship

between urban form characteristics and energy consumption in the US by considering demo-

graphics, economic, and land use features. The results indicated a significant correlation between

compact urban areas and lower per capita transportation energy use in the US metropolitan

area. In addition, Shi et al. (2020) showed the effective relationship of urban form connectivity

by reducing CO2 emission regarding the changes in a travel behaviour in Chinese cities. Some

studied the effect of age restrictions and preferences of residential neighbourhood type (Zegras

et al. 2008). This study focused on the impacts of age groups and urban design in suburban neigh-

bourhoods in Boston, USA, and indicated that the auto dependence neighbourhood type plays a

negative impact on walking among the age-restricted group. The effects of social and demographic

factors on the level of residential satisfaction, security, stability, and neighbourhood environment of

compact areas with high density and centrality have been studied in British cities to emphasize

compactness as a factor for socially sustainable cities (Bramley et al. 2009). Di Feliciantonio and

Salvati (2015) investigated the relationship between the socioeconomic patterns of residents and

urban form structures such as compact, semi-compact, and sprawling neighbourhoods in three

metropolitan areas in the Mediterranean region. Based on the results of the study conducted on

urban diffusion of the Mediterranean region, it is necessary to evaluate urban sprawl contextually

since residential and socioeconomic features of urban sprawl are different based on the context.

Regarding the relationship between socioeconomic status, neighbourhood design, and weight-

INTERNATIONAL PLANNING STUDIES 3

related behaviour in Canadian adults, McCormack et al. showed that the structure of the built

environment can influence health status. The socioeconomic and territorial features that influence

land consumption and sprawl patterns were analysed among European countries (Salvati et al.

2018). Xie, Hubbard, and Himes evaluated the neighbourhood level of socioeconomic status and

its association with less urbanized areas in the USA. Mohino, Ureña, and Solís (2019) analysed

the influence of education among highly and non-highly skilled workers in both daily commuting

and business travel in the Castilla-La Mancha region of Spain (Mohino, Ureña, and Solís 2019).

Some have highlighted the relationship between urban forms and mobility among high-income

countries in the Western world (Abreu e Silva, Golob, and Goulias 2006; Bento et al. 2001; Carne

2000; Cervero 2002; Liu, Murayama, and Ichinose 2021; Milakis, Vastos, and Barbopoulos 2008;

Naess 2003; Newman and Kenworthy 2006; Stead and Marshall 2001; Xu 2020). Camagni, Gibelli,

and Rigamonti (2002) assessed the impact of urban form expansion on commuting time and mode

choice in Milan’s metropolitan area and indicated that the sprawling pattern of development is cor-

related with length and time of work trips (Camagni, Gibelli, and Rigamonti 2002). Ewing and Cer-

vero (2010) conducted a meta-analysis on urban-development literature to assess the impacts of the

built environment on urban travel (Ewing and Cervero 2010). Stevenson et al. (2016) investigated

the relationship between urban form, transport, and population health in Melbourne, Boston,

London, Copenhagen, Sao Paolo, and Delhi and found that compact cities support walking and

the option of using less-motorized modes, which is considered as a valuable factor in increasing

health status. Ewing et al. (2018) focused on the effect of compactness on traffic congestion in

US urbanized areas. Urban form structures and local land-use effects have played a significant

role in reducing travelled household vehicle miles and CO2 emissions in major urban areas in

the United States (Lee and Lee 2020). In addition, attention has been paid to street networks,

especially street connectivity and active mobility (Frank et al. 2007; Hackl et al. 2019; Kerr et al.

2007; Marshall and Garrick 2010; Oakes, Forsyth, and Schmitz 2007). Hackl et al. (2019) used

regression models for predicating active mobility in Austria by using spatial and environmental,

socio-demographic, and infrastructure determinants of active mobility, the results of which

confirmed the relationship of built environment characteristics of areas such as accessibility and

density with walking and cycling. Marshall and Garrick (2010) evaluated the relationship between

street network characteristics and mode choices in different neighbourhood types in the USA and

confirmed the significant relationship among different mode choices including walking, driving by

personal cars, public transit, and street connectivity. Thus, increased intersection density is corre-

lated with selecting walking and biking as a dominant mode (Marshall and Garrick 2010). Marshall,

Piatkowski, and Garrick (2014) indicated the positive influence of compact street networks with

smaller street-lengths and more node connections with better health and higher physical activity

in the USA. In another study, the positive relationship between urban form characteristics includ-

ing diversity of land uses, intersection density, and accessibility to shops was confirmed (Lee,

Zegras, and Ben-Joseph 2013).

Street network configuration, as a built environment characteristic, plays an important role in

transportation behaviour. Scholars in developed countries have studied both the micro-level

(streetscape features) and macro-scale (street network design and land use) characteristics of

built urban areas. Ozbil et al. (2019) examined the associations between street-level design, local

qualities of the street environment, land-use structures, street network configuration, and walking

as the dominant mode choice. The study found significant relationships between macro-scale

characteristics of the built environment and walking, with weaker correlations observed for

micro-scale attributes. Sarkar et al. (2015) investigated the micro-scale correlates of walking dis-

tance in London, finding that the density of street trees and the normalized difference vegetation

index (NDVI) were positively associated with walking distance. In Beijing, China, neighbourhood

design factors such as high density, diversity, and walking-friendly street design were shown to

impact three types of walking (Zhao and Wan 2020). The results of studies on American cities

showed that three characteristics of street network configurations including street connectivity,

4 M. MEHRIAR ET AL.

street network density, and street patterns were significantly associated with active mobility (Mar-

shall and Garrick 2010). Another investigation analysed the impacts of street design on walkability

in Dutch cities; grid street network correlates with active mobility in the Netherlands (Nourian et al.

2018). The morphological characteristics of a street network including street slope were associated

with active mobility in mid-sized cities in Italy (Scorza and Fortunato 2024). Saelens, Sallis, and

Frank (2003) indicated that neighbourhood design characteristics such as population density, con-

nectivity, and mixed structure of land use are determined using walking and biking as dominant

mode choice in commuting and non-commuting trips. Although the body of research is rich in

developed countries, there is a shortcoming in this field in developing countries. Mehriar et al.

(2021) investigated the relationship between active mobility and street connectivity in large Pakis-

tani cities. There is a positive correlation between connectivity (smaller street-length density) and

active mobility in commuting and non-commuting trips in Lahore and Rawalpindi, Pakistan (Meh-

riar et al. 2021).

Few studies have emphasized the above-mentioned areas in developing and emerging economies

although a large body of research has been conducted on the relationship between urban form,

land-use design, and built environment with urban travel patterns and transportation, socioeco-

nomic characteristics, and residents’ attitudes in North American and European countries. Lima

(2001) investigated how social-spatial segregation is related to urban forms and locational values

in a Brazilian city. Distribution of people in city centres, inner urban areas, and peripheral areas

occurred according to income in Belem, Brazil. Thus, the low-income groups have less opportunity

for accessing urban facilities in inner-city areas (Lima 2001). Chinese cities must confront with

rapid urbanization, industrialization, and economic growth, which have influenced urban expan-

sion. Hence, urban sprawl and its relationships to travel behaviour, socioeconomic structures,

and CO

emissions have attracted a lot of attention (Guo et al. 2020; Shi et al. 2020; Tao et al.

2020). For example, Guo et al. (2020) studied the relationship between urban form features like

street density, building density, and floor area ratio with land surface temperature. Tao et al.

(2020) evaluated the relationship between different urban forms including compact and sprawled

areas with pollution.

Acheampong (2020) focused on the relationships between urban form structures and socioeco-

nomic features, travel habits, and mode choices using the Kumasi metropolitan area in Africa and

concluded that living in suburban locations, employment outside the home, long commuting dis-

tances, and higher income can influence the choice of motorized transport mode and private car use

in commuting. Therefore, living in sprawled areas is correlated with driving by cars. Blanco and

Apaolaza (2018) evaluated the mobility of different socioeconomic groups in the Buenos Aires

metropolitan region and indicated income and accessibility are considered effective factors in the

pattern of urban mobility in the Buenos Aire metropolitan area. While Guerra et al. (2018) analysed

urban travel pattern variables, urban form variables, and socioeconomic conditions by employing a

multinomial logit model in Mexico, this study indicated that residents in the compact areas with

higher density and accessibility levels drive less than those in the sprawled areas (Guerra et al.

2018). In another study, Mehriar et al. (2021) reported a relationship between street-length density

as an indicator for assessing urban sprawl with mode choices of residence in Pakistani cities by

using regression models. Further, they determined the optimal level of connectivity for having

more active mobility in commuting and non-commuting trips (Mehriar et al. 2021). Although

these studies confirmed the association between urban forms, street network patterns, and travel

behaviour among developing countries, different spatial, demographical, cultural, economic, and

social structures of cities in the developing world can influence behavioural factors like choosing

a transportation mode or residential areas. Socioeconomic, perceptional, and spatial determinants

of travel behaviour can vary in different contexts. Thus, more studies should be conducted for filling

the gap in spatial and socioeconomic determinants of travel habits in developing countries to reach

a better understanding of transportation in less developed countries. Although more attention has

been paid to built-up environment characteristics by which the patterns of land development and

INTERNATIONAL PLANNING STUDIES 5

urban sprawl have increased recently, there is still a lack of information from the developing world

compared with North American and high-income European countries. This is especially true for

Middle Eastern countries, which have experienced rapid urbanization, transformations from agri-

cultural to service economies, modernization, and urban land expansion in recent decades. The

differences in resident mobility such as choosing motorized or non-motorized transport modes

according to different types of urban form including sprawling and compact districts is an even

less-studied topic among Middle Eastern cities.

However, there are few studies on urban sprawl, land-cover changes, and negative aspects of

urban sprawl in Iran and other Middle Eastern countries (Al shawabkeh et al. 2019; Osman, Divi-

galpitiya, and Arima 2016; Rizzo 2014). Recently, some reported the relationship between urban

forms and mobility, as well as between mixed land use and driving by private car by employing

a Geographically Weighted Regression Model among older adults in Shiraz, Iran (Soltani et al.

2018). The urban sprawl modelled for two Iranian cities revealed that socioeconomic and travel

behaviour could predict the pattern of sprawl (Mehriar, Masoumi, and Mohino 2020). Some high-

lighted the driving forces and causes of urban sprawl in Iranian cities (e.g. Hosseini and Hajilou

2019; Masoumi, Hosseini, and Gouda 2018; Mehriar 2019). In addition, some reported the process

of urban sprawl as an increased sign of economic development in Iranian cities (Masoumi 2014;

Shahraki et al. 2011).

By considering the above-mentioned studies, developing the literature on urban sprawl in the

Middle East and its relationships to transportation systems and socioeconomic status, as well as

between street connectivity and urban mobility is necessary, which can facilitate effective decisions

and strategies to tackle the negative points of non-sustainable mode choices, social marginalization,

and greenhouse gas emissions. Studying urban sprawl and the attributes of different urban forms

can be helpful for decision-makers and urban planners to improve decisions related to travel behav-

iour and land-use structures in various socioeconomic contexts.

Thus, the following objectives were considered in this study. The correlation of different physical

infrastructures including street connectivity and intersection density, and the choice of transport

modes, which is particularly true for different types of neighbourhood, such as traditional ones

and new development areas in this context.

Similarities and differences between socioeconomic attributes and travel patterns of residents

among compact neighbourhoods, new, and sprawling areas.

The aim of this paper is to address three research questions: (1) Are there any differences in

mobility habits or travel behaviours based on the degree of compactness in neighbourhoods in Ira-

nian cities? (2) Are personal/household attributes and/or resident’s perceptions different in com-

pact and sprawling neighbourhoods in these two Iranian cities? (3) Finally, is there a

relationship between the use of active modes of transportation in commuting and non-commuting

trips and street connectivity, while controlling for other important socioeconomic factors? To

achieve this aim, it is hypothesized that residents in Iran, as a case study of an emerging country,

exhibit different socio-demographic characteristics when living in sprawling neighbourhoods com-

pared to compact neighbourhoods. Specifically, it is hypothesized that features, such as age, gender,

income, daily activities, car ownership, as well as travel patterns such as mode choice for commut-

ing and non-commuting trips, the number of trips taken, and the frequency of public transport use,

vary between compact and sprawled neighbourhoods in Iranian cities. Additionally, it is hypoth-

esized that residents’ perceptions regarding the quality of entertainment and social facilities, attrac-

tiveness of shopping opportunities, and reasons for choosing their residential location differ

depending on the type of neighbourhood (sprawling or compact) in Iranian cities. Lastly, it is

hypothesized that there is a positive relationship between walking and cycling for daily trips and

street connectivity among Iranian cities. In other words, the structure of street networks and the

associated differences between compact and sprawling neighbourhoods have a significant impact

on residents’ mode choices.

6 M. MEHRIAR ET AL.

Case study

The present study used Hamedan and Nowshahr as the case studies. According to Iran’s National

Physical Plan, Hamedan is considered a large city (more than 500,000 inhabitants) and Nowshahr is

a small city (population less than 50,000) (Ghadiri, 2015). All Iranian cities, whether large or small,

have experienced increased trends of sprawl, which is the reason why this paper covers one of each

(Soltani, Hosseinpour, & Hajizadeh, 2017; Shahraki et al. 2011).

Hamedan is the capital of Hamedan province and is located in northwestern Iran. The popu-

lation was 655,859 in 2016, based on the official population and housing census. Hamedan has fol-

lowed a monocentric pattern for several decades. Bazar (the main commercial structure in Iranian

cities) and most workplaces are located in the first ring of Hamedan, as the historical core of the

city. Nowshahr has a population of 49,403 based on the official population and housing census.

It is located in the north of Iran, on the coast of the Caspian Sea. The proximity to the Caspian

Sea and its access to natural resources have caused Nowshahr to develop in discontinuous strips.

Hamedan has 2,831 km

with an average population density of 225 people per km

, Nowshahr cov-

ers 1718 km

, and 73 persons live in each square kilometre averagely in 2016. In both cities, public

mode choices include bus, minibus, car-pool, and taxi. Motorbikes, bicycles and cars are used as

private vehicles. Scooters and e-bikes are not used by Iranian as a mode choice. Although people

have employed some ride-sharing apps in Hamedan and Nowshahr, these apps include motorized

vehicles and there are no ride-sharing apps for cycling or scooters.

Masoumi, Hosseini, and Gouda (2018) classified the urban form in Iranian cities into three

types. The first is related to a traditional urban form, which has an organic urban fabric and a com-

pact urban texture, dead-ends, and narrow streets, and a dominant neighbourhood centre, which

was dominated until the 1930s. The second is the transitional urban form during 1930–1970s,

which has a semi-grid street network, less prominent neighbourhood centres, and a less compact

urban form compared to traditional urban forms, and straight, wide streets on the edge of neigh-

bourhoods. The last is related to new developments after the 1980s including completely gridded

streets, very weak or absent neighbourhood centres, and sprawling areas.

First, two neighbourhoods from each city were selected based on an assessment of aerial photos

of the two cities. During the initial assessment, the short distance to the city centre and a dense

urban texture were considered as preliminary criteria for selecting compact neighbourhoods in

both cities. Sprawling neighbourhoods were subsequently chosen based on the location in the per-

ipheral areas of cities with comparatively longer distances to the urban core. In other words, the

‘compact neighbourhoods’ in Hamedan and Nowshahr were transitional urban forms with more

compactness levels than new urban areas. In addition, the sprawling ones were new development

areas with the fragmented pattern. Then, the Shannon entropy was computed for each neighbour-

hood to confirm the selection. Shannon entropy is a scientific metric for measuring urban sprawl

(Bhatta, Saraswati, and Bandyopadhyay 2010; Nazarnia, Harding, & Jaeger, 2019), which was cal-

culated for each participant who indicated the nearest intersection to home in a map attached to

the questionnaire. Afterwards, home locations were converted to built-up and non-built-up raster

shape files of Hamedan and Nowshahr in Arc-GIS (Version 10.3), and the disaggregated Shannon

entropy was calculated using spatial analysis tools. Shannon entropy has an amount between zero

and one, in which 1 means that the district has a sprawled form. It is important to check the differ-

ences in Shannon entropy as well as land-use variables including link density, intersection density,

and the link-node ratio between compact and sprawling neighbourhoods to make sure these neigh-

bourhoods were correctly selected for the purpose of this study. The Kruskal–Wallis tests were used

for these variables, where the p-value of <0.05 indicates a significant difference between Shannon

entropy and other land-use variables in the two different compact and sprawling neighbourhoods

in both Hamedan and Nowshahr. These neighbourhoods were emerged at different times and have

different structures. The mean of Shannon entropy for compact neighbourhoods in Hamedan and

Nowshahr are 0.11 and 0.13, respectively, while it is 0.28 and 0.36 for sprawling neighbourhoods.

INTERNATIONAL PLANNING STUDIES 7

Table 1 presents the differences among land-use variables in compact and sprawling neighbour-

hoods in Hamedan and Nowshahr.

Figure 1 shows the case study areas in Hamedan and Nowshahr. In this paper, sprawling neigh-

bourhoods refer to the neighbourhoods developed after 1980s in the peripheral areas of cities with

less amount of built-up density (Shannon entropy) and less connected street network.

3. Methodology

3.1. Data and variables

This study utilized a dataset obtained from a survey conducted in winter 2019 by the residents in

four neighbourhoods with compact and sprawling urban forms in Hamedan and Nowshahr as the

case studies of a large and small city, respectively. The data were collected from 954 validated ques-

tionnaires by face-to-face interviews in Persian language. First, 250 questionnaires were completed

for each neighbourhood, but 46 invalid questionnaires were removed from the total dataset of

Hamedan after validation.

Table 1. Differences among land-use variables in compact and sprawling neighbourhoods in Hamedan and Nowshahr.

Shannon entropy Link density Intersection density Link-node ratio

Hamedan

Chi-square 3.988 155.879 304.013 206.767

df 1 1 1 1

P-value 0.046 <0.001 <0.001 <0.001

Nowshahr

Chi-square 87.944 372.133 372.814 34.748

df 1 1 1 1

P-value <0.001 <0.001 <0.001 <0.001

Figure 1. Selected neighbourhoods in Hamedan and Nowshahr. Mehriar, Masoumi, and Mohino (2020).

8 M. MEHRIAR ET AL.

The samples included 500 questionnaires for Nowshahr and 454 for Hamadan, which is a good

representation of the neighbourhood level for both cities according to statistical estimations of the

sample size (Cochran 1963). Nowshahr has a confidence interval of 4.36 based on a confidence

level of 95% and the population of 49,403. Hamedan shows a confidence level of 95% with a confi-

dence interval of 4.6 based on the population of 655,859 people. Variables were categorized into socio-

economic characteristics, mobility, and participants’ perceptions about their neighbourhoods.

The socioeconomic variables at personal and household levels included gender, age, main daily

activity in individual level, possession of a driver’s licence, the number of driver’s licences in the

household, the number of cars in the household, and gross monthly income for household.

The variables related to mobility and travel habits included the number of commuting and non-com-

muting trips per week, transport mode for commuting and non-commuting trips within and outside

of neighbourhoods, the frequency of public transport use, and commuting distance. The perception

and attitudes of participants were measured by documenting their preferred place for shopping within

or outside neighbourhoods, sense of belonging to their neighbourhoods, perceptions of the attractive-

ness of shops, favourite entertainment and recreational activities, quality of entertainment and rec-

reational facilities, and length of the time residents lived in their current home. All data related to

mobility and perception of residents were collected at an individual level. Further, the study contained

some questions on residential location choices such as distance to workplace and relatives, attractive-

ness of the environment, affordable prices for housing, and other economic reasons. Two maps were

attached to the questionnaires to indicate the nearest intersections to the participants’ homes and

workplaces. The nearest intersection was selected for analysis to avoid violating the privacy of the par-

ticipants. Shannon entropy, link density, intersection density, and link-node ratio were calculated to

confirm the differences between street and land-use structures in compact and sprawling neighbour-

hoods based on the pinned points on the attached maps. Table 2 shows all the variables employed in

the various analyses in this paper. The questionnaire was designed to collect the data in both levels of

individual and household information. For example, age, gender, mode choice, frequency of work and

non-work trips, and perception of the quality of facilities were gathered at the individual level, while

income and car ownership were collected at the household level.

3.2. Analysis methods

To address the first two research questions, Shapiro–Wilk and Kolmogorov–Smirnov tests were

conducted on both the Hamedan and Nowshahr datasets using IBM SPSS (Version 22) to assess

the normality distribution of the data. The p-values for all variables in both normality tests were

found to be less than 0.05, leading to the rejection of the null hypotheses. This indicates that the

datasets do not follow a normal distribution. Therefore, instead of using t-tests (which assume nor-

mal distribution), Kruskal–Wallis and chi-square tests were employed to answer the first two

research questions. The data were analysed using nonparametric methods. Two tests were selected

to address the research questions based on whether the variables were classified as ‘continuous’ or

‘categorical.’ Kruskal–Wallis and chi-square tests were used for continuous and categorical vari-

ables, respectively. A p-value of less than or equal to 0.05 indicates the rejection of the null hypoth-

esis and acceptance of the alternative hypothesis. In this study, accepting the alternative hypothesis

means that there is a significant difference between living in compact and sprawling neighbour-

hoods regarding socioeconomic features, travel behaviour, and perception of residence in the

two Iranian cities. The independent chi-square test examines the presence of a relationship between

two categorical variables. A null hypothesis in a chi-squared test assumes no significant difference

between the observed and expected value for categorical variables. Based on the objective of this

study, chi-square can be used for determining whether there is any statistical difference among cat-

egorical variables between living in a compact and a sprawling neighbourhood. The null hypothesis

is rejected if p-values are less than 0.05. Chi-square shows a significant relationship between the

dependent and independent variables (in our case, the difference between living in sprawling or

INTERNATIONAL PLANNING STUDIES 9

compact neighbourhoods and categorical variables), but it does not indicate how strong this

relationship is. Hence, proportional reduction in error (PRE) describes which predictions obtained

by the chi-squared test are more appropriate. PRE value is a statistical criterion that indicates the

extent to which knowledge about the independent variable can help us predict the dependent vari-

able. Therefore, the stronger relationship between the variables leads to higher number of PRE.

Table 2. Variables.

Variables Description Categories

Socioeconomic variables

Gender Male, female (personal) Categorical

Age (Personal) Continuous

Main daily activity Only student,

work and study (personal)

Categorical

Driver’s licence (Personal) Categorical

Car ownership No car,

1 car, 2 cars, or more (household)

Categorical

Financial dependency status No, yes (personal) Categorical

Gross monthly income

(household) Continuous

Urban mobility variables

Number of commute trips (Personal) Continuous

Mode of transportation for commuting

trips

Personal car, taxi, taxi app, walking, bus/minibus/van, motorcycle,

informal public transport, service/shuttle, bicycle (personal)

Categorical

Number of trips for shopping or

entertainment

(Personal) Continuous

Daily shopping location Outside, inside (Personal) Categorical

Mode choice for shopping/entertainment

inside the neighbourhood

Personal car, taxi, taxi app, walking, bus/minibus/van, motorcycle,

informal public transport, service/shuttle, bicycle (Personal)

Categorical

Mode choice for shopping/entertainment

outside the neighbourhood

Personal car, taxi, taxi app, walking, bus/minibus/van, motorcycle,

informal public transport, service/shuttle, bicycle (Personal)

Categorical

Frequency of public transport use Almost never, rarely, a few times per month, a few times per week,

every day (Personal)

Categorical

Perception of residents’ variables

Sense of belonging to neighbourhood No, yes (Personal) Categorical

Attractiveness of shops No, yes (Personal) Categorical

Entertainment areas Far away, inside my neighbourhood Categorical

Quality of social/recreational facilities Not attractive, a little attractive, acceptably attractive, medium, very

attractive (Personal)

Categorical

Residential location choice The house was affordable to buy, the house was near my work, the

surrounding environment is attractive, the house will have a higher

price, the house is near my relatives, l have lived here since I was

born (Personal)

Categorical

Length of time residents have lived in

their current home

(Personal) Continuous

Land-use variables

Urban sprawl around home Measured by Shannon entropy/Krakow. City divided into 4,256 grids

in GIS followed by employing zonal extension and spatial analysis

tools. Then, home points joined to grids based on common spatial

location to get their amount of disaggregated Shannon entropy

Continuous

Commuting distance The distance from home to workplace for participants, according to

pinned home and workplace locations on the maps

Continuous

Intersection density The number of intersections in the catchment area divided by the

area of the catchment area (catchment area = a circle with 600

metres radius for participants who indicated the nearest

intersection to home)

Continuous

Link density The total lengths of all streets in the catchment area divided by the

area of the catchment area (catchment area = a circle with 600

metres radius for participants who indicated the nearest

intersection to home)

Continuous

Link-node ratio The number of intersections in the catchment area divided by the

total length of all streets in the catchment area (catchment area = a

circle with 600 metres radius for participants who indicated the

nearest intersection to home)

Continuous

The figures of monthly income for household were converted to euro from Iranian Rial according to the rate of these two cur-

rencies on December 1, 2019 during the data collection period.

10 M. MEHRIAR ET AL.

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Lambda, phi, Cramer’s V, and gamma coefficients are used as PRE to evaluate the degree of strength

between variables. In this paper, Cramer’s V and phi coefficients were used. Then, Cramer’s V and

phi were computed. A value of phi and Cramer’s V between 0.0 and 0.10 shows weak strength, while

the value between 0.10 and 0.30 indicates moderate strength. The values of phi and Cramer’s V

higher than 0.30 indicate a strong relationship between two categorical variables.

To address the third research question, three ordinary least squares (OLS) regression models were

developed for the full dataset, covering both commuting and non-commuting trips within and outside

the neighbourhood. The aim of these OLS models was to determine the correlations between street

network configuration and active mobility in Iranian cities. Intersection density was used as the depen-

dent variable to examine the relationship between urban form and street connectivity, while active

mobility in commuting and non-commuting trips served as the independent variables in the OLS

models. Monthly household income, age, gender, and car ownership were included as fixed variables

in the models. Based on the analyses by Hamidi and Ewing (2020) for the United States, street con-

nectivity is considered a key component for assessing compactness levels (Hamidi and Ewing 2020).

Figure 2 illustrates the map of the current research for answer three research questions.

4. Findings

The participants had a range age of 13 and 87 (M = 37) during the study. Among them, 51.5% were

men and 48.5% were women. The average monthly household income was 595 Euro in Hamedan

and 688 Euro in Nowshahr. Additionally, 60% of the participants had jobs or were studying at the

university in both cities. Personal car usage was the dominant mode of transportation in both cities,

accounting for 29.2% in Hamedan and 31.2% in Nowshahr. Cycling and walking constituted 6.8%

and 0.2% of transportation choices in Hamedan and Nowshahr, respectively.

4.1. Kruskal–Wallist and chi-squared tests

Kruskal–Wallis tests were conducted for the continuous variables to study the differences in socio-

economic and mobility variables between compact and sprawling neighbourhoods in Hamedan and

Figure 2. Conceptual framework of the paper.

INTERNATIONAL PLANNING STUDIES 11

Nowshahr. As shown in Table 3, all the continuous socioeconomic and mobility variables are highly

significant with the exception of age for residents in Hamedan. In other words, significant differ-

ences were observed between compact and sprawling neighbourhoods in Hamedan with respect

to income, car ownership, number of driver’s licences, number of commuting and non-commuting

trips, commuting distance, and length of time residents living in their current homes. However, age

was not significant. Thus, the pattern of distribution of different age groups is similar in compact

and sprawling neighbourhoods in Hamedan. However, age and other variables are all highly signifi-

cant in Nowshahr, which confirms the hypothesis.

Chi-squared tests are used to find the independence of two categorical variables. In this study,

compact or sprawling as the type of neighbourhood was the first categorical variable, and the

chi-squared test was used to measure its relationship with other categorical variables such as

socioeconomic factors, mobility, and residents’ perceptions. A p-value with less than 0.05 rejects

the null hypothesis, which means that the two variables are related. The results indicated that

the distribution of observations for variables is different depending on sprawling or compact

neighbourhoods. Table 4 shows the chi-square results for categorical variables in Hamedan,

main daily activity, driver’s licence, and the sense of belonging to a neighbourhood with the

p-values of 0.17, 0.09, and 0.10, respectively, which confirms the null hypothesis. In addition,

the sense of belonging to a neighbourhood has a marginal significance in the test. However, gen-

der, attractiveness of shops, preferred entertainment location, and frequency of public transport

use had moderate relationships, while transport mode regardless of the distance or reason (com-

muting or not, inside or outside the neighbourhood), quality of social and recreational facilities,

and residential location choice had a strong relationship with sprawling or compact neighbour-

hood in Hamedan.

Table 5 presents the results of chi-square for categorical variables in Nowshahr. Mode choice for

non-commuting within the neighbourhood, the quality of social and recreational facilities in

Table 3. Kruskal–Wallis test among continuous variables in Hamedan and Nowshahr.

Age

Number of

driving

licences in

household

Number of

cars in

houeshold

Montly income

for houeshold

(Euro)

Number of

commute

trips per week

Number of

non-commute

trips per week

Length of

time for living

in the current

home

Commuting

distance

Hamedan

Chi-square 0.817 25.513 51.169 120.245 66.598 7.191 24.885 75.299

df 1 1 1 1 1 1 1 1

P-value 0.366 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

Nowshahr

Chi-square 5.789 6.409 8.650 20.498 17.367 111.04 47.077 117.271

df 1 1 1 1 1 1 1 1

P-value .016 .011 0.003 <0.001 <0.001 <0.001 <0.001 <0.001

Table 4. Chi-square test for categorical variables in Hamedan.

Pearson chi-square Value df P-value Cramer’s V Phi

Gender 11.17 2 0.004 0.15

Main daily activity 3.482 2 0.175

Driver’s licence 4.819 2 0.09

Transport mode for commuting trip 62.729 9 <0.001 0.37

Daily shopping area 34.374 2 <0.001 0.27

Transport mode for shopping/entertainment inside the neighbourhood 84.413 8 <0.001 0.43

Transport mode for shopping/entertainment outside the neighbourhood 56.07 8 <0.001 0.35

Frequency of public transport use 32.025 5 <0.001 0.26

Sense of belonging to neighbourhood 4.527 2 0.104

Attractiveness of shops 10.934 2 0.004 0.15

Entertainment area 7.767 2 0.021 0.13

Quality of social/recreational facilities 128.441 5 <0.001 0.53

Residential location choice 96.394 6 <0.001 0.46

12 M. MEHRIAR ET AL.

neighbourhood, and gender with the p-value of 0.14, 0.17, and 0.08, respectively, cannot reject the

null hypothesis. Strong relationships were reported for main daily activity, possession of a driver’s

licence, mode choice for commuting and non-commuting trips outside the neighbourhood, fre-

quency of public transport use, entertainment area, residential location choice, and the sense of

belonging to the neighbourhood. Daily shopping areas and the attractiveness of shops had strong

explanatory strength in chi-squared tests in Nowshahr.

4.2. OLS models street connectivity in commuting and non-commuting trips in Iran

An OLS model for street connectivity is used for determining the relationship between street con-

nectivity as one feature of urban form which is different between sprawling and compact neigh-

bourhood and the use of walking and cycling as environmentally friendly mode choices in

Iranian cities. As shown in Table 6, a highly significant relationship is observed between intersec-

tion density and active mobility in commuting trips in Iran.

As shown in Table 6, a one-unit increase in intersection density is correlated to a 17% increase in the

use of active mobility in commuting trips in both Hamedan and Nowshahar. Thus, the residents living

in the areas with a high level of connectivity (in this case, compact areas with higher rates of intersection

density) prefer to use walking and cycling as commuting mode choices more than those living in sprawl-

ing areas. Table 7 indicates a positive and significant association between living in the areas with a high

level of street connectivity and active mobility in non-commuting travels inside the neighbourhoods for

both Hamedan and Nowshahr. Table 8 represents a 19% increase in active mobility related to a one-unit

increase in intersection density in non-commuting travels outside the neighbourhoods for these two

Table 5. Chi-square test for categorical variables in Nowshahr.

Pearson chi-square Value df P-value Cramer’s V Phi

Gender 2.898 1 0.089

Main daily activity 9.633 1 0.002 0.139

Driver’s licence 7.237 1 0.007 0.12

Transport mode for commuting trip 26.262 8 0.001 0.229

Daily shopping area 110.804 1 <0.001 0.471

Transport mode for shopping/entertainment inside the neighbourhood 12.048 8 0.148

Transport mode for shopping/entertainment outside the neighbourhood 25.344 7 0.001 0.225

Frequency of public transport use 19.996 4 0.001 0.2

Sense of belonging to neighbourhood 31.387 1 <0.001 0.251

Attractiveness of shops 158.84 1 <0.001 0.564

Entertainment area 12.362 2 0.002 0.157

Quality of social/recreational facilities 4.989 3 0.173

Residential location choice 20.207 5 0.001 0.201

Table 6. OLS model for street connectivity and active mobility in commuting travels in Iranian cities.

BStd. error Beta t P

Constant 14.06 2.33 6.01 <0.001

Active mode choice 7.36 1.60 0.17 4.59 <0.001

Age −0.10 0.02 −0.13 −3.64 <0.001

Gender 1.93 0.71 0.10 2.72 0.007

Number of cars in household 0.88 0.50 0.07 1.76 0.07

Income (euro) −.003 .001 −.11 −2.70 0.007

Model validation

Model Sum of squares df Mean square F P

Regression 3663.28 5 732.65 10.12 <0.001

Residual 46595.64 644 72.35

Total 50258.93 649

Modal summary

Model R R square Adjusted R square Std. error of the estimate

1 0.27 .070 .060 8.50000

INTERNATIONAL PLANNING STUDIES 13

Iranian cities. The small value of R squares in the three models for our model has just one variable, and

we study the correlation of only one variable with street connectivity, so we can ignore them.

5. Discussion

Cities in the developed and emerging countries experience different patterns of development. These

patterns have some dissimilarities even among the developed countries. Urban sprawl has taken

various forms in American cities: edge cities, planned communities, or individual houses among

rural landscapes. However, American and Western European cities have experienced strong income

growth. New urban forms are derived from the growth of car ownership in American cities, while

large investment in public transportation has been a key factor in shaping new development among

European cities (Nechyba and Walsh 2004).

Different theories have discussed the structures of urban forms. In a monocentric city model

(Alonso 1964), spatial urban structures come from trade-offs between commuting costs and land

prices in peripheral areas of cities (Nechyba and Walsh 2004). For example, corresponding auto-

mobile ownership and lower transportation costs became one of the main drivers of sprawling cities

in the United States during the twentieth century (Glaeser and Kahn 2004). Further, the mono-

centric city model explains that raising incomes causes a decrease in city densities and an increase

in demand for housing (Nechyba and Walsh 2004). Nechyba and Walsh (2004) emphasized that the

monocentric city model suggests the primary historical causes of urban sprawl, where residential

Table 8. OLS model for street connectivity and active mobility in non-commuting travels outside the neighbourhoods in Iranian

cities.

BStd. error Beta t P

Constant 14.06 2.15 6.52 < 0.001

Active mode choice 9.66 1.59 0.19 6.04 < 0.001

Age −0.10 0.01 −0.19 −6.15 < 0.001

Gender −0.22 0.53 −0.01 −0.41 0.68

Number of cars in household 0.77 0.39 0.06 1.95 0.51

Income (euro) −.003 .001 −.09 −2.760 0.006

Model validation

Model Sum of squares df Mean square F P

Regression 5454.36 5 1090.87 16.72 < 0.001

Residual 60190.12 923 65.21

Total 65644.48 928

Modal summary

Model R R square Adjusted R square Std. error of the estimate

1 0.28 .08 .08 8.07

Table 7. OLS model for street connectivity and active mobility in non-commuting travels inside the neighbourhoods in Iranian

cities.

BStd. error Beta T P

Constant 19.37 1.65 11.71 <0.001

Active mode choice 4.29 0.87 0.16 4.95 <0.001

Age −0.10 0.01 −0.19 −6.14 <0.001

Gender −0.31 0.53 −0.01 −0.57 0.56

Number of cars in household 0.81 0.39 0.07 2.04 0.04

Income (euro) −.003 −.001 −.10 −2.810 0.005

Model validation

Model Sum of squares df Mean square F P

Regression 4763.92 5 952.78 14.44 <0.001

Residual 60958.58 924 65.97

Total 657222.51 929

Modal summary

Model R R square Adjusted R square Std. error of the estimate

1 0.26 0.07 .06 8.12

14 M. MEHRIAR ET AL.

location choice is influenced by socioeconomic aspects such as crime rate, racial structures, and

local facilities, in addition to income level, transport costs, car ownership, and land prices (Nechyba

and Walsh 2004).

Although the reasons for urban sprawl are not the focus of this paper, the findings indicated

some differences between compact and sprawling neighbourhoods in Iranian cities. The differences

were found in income, gender, car ownership, and job status within the socioeconomic structure.

Furthermore, they confirmed the results of the previous studies on different income levels in dis-

tinct urban forms (Mendonça et al. 2020; Nechyba and Walsh 2004; Persson, Eriksson, and Lõhmus

2018) in both high-income countries and emerging economies (Acheampong 2020; de Espindola,

da Costa Carneiro, & Façanha, 2017; Li & Li, 2019; Zhang, Miao, Zhang, & Chen, 2018). Based on

the results, the structures of age groups do not follow different patterns in compact and sprawling

neighbourhoods in Hamedan, which is inconsistent with the findings of the EEA report on Euro-

pean countries in 2016 (EEA 2016). However, the findings on age in Nowshahr are consistent with

the EEA report.

The different patterns of urban mobility through different neighbourhoods have attracted a lot of

attention in both developed and developing countries. Based on the findings, the frequency of

commuting and non-commuting trips, distance of the commute, mode choice in commuting

and non-commuting trips, and frequency of using public transport demonstrate different beha-

viours in compact and sprawling neighbourhoods in Hamedan and Nowshahr, irrespective

of their size. The findings are in line with those of some other studies (Camagni, Gibelli, and

Rigamonti 2002; Figueroa, Nielsen, and Siren 2014; Kaza 2020; Lee 2020; Pouyanne 2010), which

compare an urban travel behaviour between compact and sprawling neighbourhoods. Camagni,

Gibelli, and Rigamonti (2002) showed that travel time for private vehicles has a weak correlation

with urban density in Milan (Camagni, Gibelli, and Rigamonti 2002). A study on Danish cities

reported substantial differences between travel patterns and urban forms among various age groups

(Figueroa, Nielsen, and Siren 2014).

Regarding the findings in Hamedan, the number of men in the sprawling neighbourhood is

more than the women living in the sprawling area, while there is a considerable difference between

the number of women and men in the compact neighbourhood in Nowshahr. Further, the number

of people with driving licences is more than those without one in both compact and sprawling

neighbourhoods of Hamedan and Nowshahr. However, the ratio and pattern of this factor vary

among neighbourhoods. Regarding Hamedan, no meaningful difference was observed in job status

based on whether living in the compact or sprawling neighbourhood, while a considerable differ-

ence was reported between employed and unemployed people in the sprawling neighbourhood in

Nowshahr. The number of employed people is higher in this district. Therefore, job locations have a

more balanced distribution in both sprawling and compact districts of Nowshahr than in Hamedan,

and people living in sprawling and peripheral districts of Nowshahr select these areas for better

access to workplaces.

Hamedan has a monocentric pattern where a considerable number of jobs are located in the

centre or internal ring. In its compact district, participants use more bus/minibus/van, service/shut-

tle, and taxi for commuting than those living in the sprawling neighbourhood. Meanwhile, personal

car is a dominant mode choice for non-commuting trips in the sprawling district. Regarding the

commuting trips in Nowshahr, sprawling district residents use car and motorbike more than

other transportation modes compared with compact area residents. Regarding non-commuting

trips, the participants in both types of neighbourhoods use car more than other mode choices.

Further, the residents of the compact neighbourhood use mobility and public transit more actively

than those of the sprawling area. The findings confirmed that the people in sprawling areas have

more car dependency than those in compact districts. Residents of sprawling areas in both Iranian

cities use personal cars more than any other mode choice due to low level of accessibility, less con-

nected street network, single land-use structure, inefficient public transit system, and lack of social

and recreational facilities.

INTERNATIONAL PLANNING STUDIES 15

In addition to travel habits and socioeconomic features, the perception of people seems to be

different among compact and sprawling neighbourhoods. No significant difference was observed

between the participants from both areas in Hamedan regarding the sense of belonging to the

neighbourhood, while a considerable proportion of participants who claim a stronger sense of

belonging to the neighbourhood live in the sprawling district in Nowshahr.

Nowshahr is located in the north of Iran and has spectacular green landscapes. Thus, puerperal

and sprawling areas have more accessibility to green spaces. On the other hand, the residents of this

district are not satisfied with the attractiveness of shops in their neighbourhood, while the majority

of the residents in the sprawling neighbourhood of Hamedan seem to be happy about the quality of

their shops and commercial centres. It is worth noting that the most significant commercial centres

have been recently developed in the second and third rings of the city, and the commercial centres

in the first ring have transformed from retail shops to wholesale centres although Hamedan has a

monocentric pattern. Nowshahr, on the other hand, has a linear pattern and commercial centres

were built in important points of the city.

In addition, a positive correlation was reported between active mobility and intersection density

as a measure of the compactness of the built environment. There is an argument about the relation-

ship between the built environment and travel behaviours such as active mobility and car use in

travel behaviour theories, particularly, self-selection theory (Chowdhury and Scott 2020; Hamidi

and Ewing 2020). Although this study did not discuss how residence choices could influence travel

behaviours, it demonstrated a strong relationship between the built environment and active mobi-

lity data from Iran.

The findings of this study are in line with that of Xu (2019), which focused on the relationship

between active mobility and different urban forms in Toronto, and the results of Koohsari et al.

(2017) showing a positive relationship between walking as a mode choice in commuting trips

and intersection density in urban areas of Japan. Concerning the positive relationship between

active mobility and street connectivity, the results are consistent with some studies in Western

countries (Badland, Schofield, and Garrett 2008; Marshall and Garrick 2010). The consistent results

of this study with urban mobility literature in the developed counties on connectivity in both devel-

oped and emerging countries, as well as the pattern of the street network is a key factor in the mode

choice behaviour of residents. However, the strength of relationship between connectivity with

active mobility may be different in different contexts, which is dependent on various socioeconomic

or perceptional features. The socioeconomic features including age, gender, car ownership, and

income were controlled in this paper. However, gender inequalities or cultural restrictions in devel-

oping countries can have different impacts on urban mobility compared with the developed

countries.

In addition to using intersection density as a metric for evaluating street connectivity, a large

number of studies have employed street connectivity as a component for calculating the level of

compactness and sprawl in urban areas along with its relationship with travel behaviour. In this

regard, this study confirmed the results of Lee (2020) on urban areas of the United States, which

found a relationship between the areas of less sprawl and more walking commuters. However,

the results are inconsistent with the study of Chen et al. (2013) regarding the relationship between

the features of the built environment and walking time in Japan. Thus, the findings of this study

could confirm the statistical correlation between living in urban sprawl and using non-motorized

mode choices like in Western countries, which are in line with the results of other studies conducted

in the developing countries. For instance, Kakar and Prasad (2020) reported the relationship

between residing in sprawling areas of Kabul and lower percentages of people interested in using

walking as a mode choice in both commuting and non-commuting trips. Additionally, the results

are in line with the study conducted in the Middle East and North Africa (MENA) region which

showed a negative relationship between increasing street connectivity and using vehicles instead

of walking (Mostofi, Masoumi, and Dienel 2020). Furthermore, the results of this paper regarding

positive correlations between street connectivity and active mobility confirm the findings of a study

16 M. MEHRIAR ET AL.

conducted in large Pakistani cities (Mehriar et al. 2021), although street-length density was used to

assess street connectivity in Pakistani cities.

This study revealed some variations in socioeconomic, travel patterns, and corresponding per-

ceptions about the residence in different types of Iranian neighbourhoods. Therefore, urban plan-

ners and decision-makers should consider the personal/household structures in different types of

neighbourhoods.

Additionally, the residents of compact neighbourhoods have different socioeconomic structures

and follow dissimilar urban mobility trends compared with the residents of sprawling districts.

In both Iranian cities, urban form characteristics and street connectivity were correlated with active

mobility in commuting and non-commuting trips. Thus, increasing street connectivity and acces-

sibility can help achieve sustainable development goals. While designing new development areas,

considering shorter streets and more intersections, as well as avoiding massive residential blocks

in primary urban development plans can be considered as helpful policies to achieve sustainable

urban mobility. Further, it is an effective strategy for revitalizing and regenerating Iranian cities.

Hence, urban planners and sustainable strategists should focus on improving street connectivity

to enhance active and environmentally friendly mode choices.

The results of this study indicated a positive correlation between more compact neighbourhoods

and greater active mobility in non-commuting trips within neighbourhoods. Rezoning for mixed

land use and increasing the number of walking paths can result in reducing the number of motor-

ized mode choices for commercial or recreational trips within neighbourhoods. However, this tra-

vel pattern was observed in non-commuting travels outside the neighbourhoods. Therefore,

adopting a compact development approach towards Iranian cities and increasing the level of street

connectivity can encourage urban planners with solutions to reduce traffic congestion, which, in

turn, can reduce CO

emissions and increase physical activity and well-being.

These findings offer valuable insights for urban planners and decision-makers in understanding

the relationship between urban forms and travel behaviour in a less-explored context. Urban planners

and policy-makers in the cities of emerging economies can provide policies and strategies for urban

planning and transportation systems based on knowledge from the developed countries. Hence,

transportation and land use policies in the developing countries are suffering from shortcomings

regarding the knowledge gap. According to urban transportation literature, socioeconomic, psycho-

logical, and cultural features along with urban built environment characteristics can play a significant

role in the pattern of urban mobility. Thus, ignoring specific and different structures of socioeco-

nomic and perceived behaviour in different contexts makes inefficient policies for tackling problems

in emerging economies. This paper discussed similarities and differences in socioeconomic and travel

habits in different urban forms and models street connectivity regarding active mobility in a less-

studied context to shed light on urban transportation planning for Middle Eastern cities.

6. Conclusion

The results of this study demonstrated that Iranians residing in different types of built environ-

ments (compact and sprawling neighbourhoods) have different socioeconomic and mobility pat-

terns. The residents of sprawling areas have more different income levels, more cars, and are

more likely to have a driver’s licence in the household in comparison with those living in compact

areas. Further, significant differences were observed in commuting distance, the number of trips

people take (regardless of destination or purpose), transportation mode choice for these trips,

and the frequency they use public transport. Other factors like daily shopping areas, places of enter-

tainment, attractiveness of shops, length of time they have lived in their current home, sense of

belonging in the neighbourhood, and residential location choices are different in compact and

sprawling neighbourhoods in both Hamedan and Nowshahr.

Additionally, there is a positive correlation between street connectivity as an indicator for the

compactness of the built environment and active mobility when socioeconomic variables including

INTERNATIONAL PLANNING STUDIES 17

age, gender, the number of cars in the household, and the monthly household income are controlled

in the models. It can be observed in all kinds of commuting and non-commuting trips inside and

outside the neighbourhoods in both cities.

Comparing explanatory variables related to different types of urban forms in emerging countries

is difficult due to the lack of modelling urban forms with mobility behaviour in this context. In par-

ticular, Middle Eastern cities are confronting rapid urbanization and modernization, as well as

recent discontinuous development areas. Considering socioeconomic contexts, different percep-

tions and attitudes on the part of residents, along with different patterns of mobility behaviours

in Middle Eastern cities, can make it difficult to implement general policies and strategies originated

from Western countries.

Further studies can focus on implementing more statistical models for analysing the relationship

between various characteristics of urban sprawl in the emerging countries. Examples of these vari-

ables are the structure of land use, distance to urban cores, and access to public facilities. Further,

they can incorporate the variables related to street networks such as accessibility to walking paths,

link-node ratio, and length density, as well as how they are related to urban mobility patterns.

Urban sprawl has been emerged as a pattern of new development in developing countries just as

it appeared in the developed ones. However, our knowledge on the developing countries is limited,

which has made it difficult to assess the relationship of urban forms with urban travel behaviour and

socioeconomic features.

Disclosure statement

No potential conflict of interest was reported by the author(s).

ORCID

Melika Mehriar http://orcid.org/0000-0001-7303-1316

Houshmand Masoumi http://orcid.org/0000-0003-2843-4890

Inmaculada Mohino http://orcid.org/0000-0001-5751-2507

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