Article citation information:

Mehriar, M., Masoumi, H., Nosal-Hoy, K. Correlations of urban sprawl with transport patterns and socioeconomics of university students in Cracow, Poland. Scientific Journal of Silesian University of Technology. Series Transport. 2020, 108, 159-181. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2020.108.14.

 

 

Melika MEHRIAR[1], Houshmand MASOUMI[2], Katarzyna NOSAL-HOY[3]

 

 

 

CORRELATIONS OF URBAN SPRAWL WITH TRANSPORT PATTERNS AND SOCIOECONOMICS OF UNIVERSITY STUDENTS IN CRACOW, POLAND

 

Summary. Urban sprawl is considered as a western urban development pattern, which is common in different cities around the world. Although, a large number of studies have focused on urban sprawl, modelling urban sprawl has been less emphasised, especially in various geographical contexts. This study aims to investigate urban sprawl and its determinants in a post-socialist country and model urban sprawl based on disaggregated data. In addition, the correlations of urban sprawl with travel patterns were examined, along with the socioeconomic characteristics of university students in Cracow, Poland by applying the Weighted Least Square (WLS) regression model. The WLS regression model was fitted based on the data from 1,288 online questionnaires targeting university students. Furthermore, urban sprawl around the home and the university for each student who indicated the nearest intersection to their home and university were separately estimated by employing the Shannon entropy. Based on the findings, urban sprawl around homes was correlated with 14 transport patterns and socioeconomic features such as gender, age, driving license, financial dependency status, gross monthly income, number of commute trips, mode of transportation for commuting, number of trips for shopping or entertainment, daily shopping area, mode choice for shopping and entertainment trips inside and outside the neighbourhoods, frequency of public transport use, the attractiveness of shops inside the neighbourhoods, and the length of time living in the current home. Additionally, urban sprawl around the university was significantly correlated with age, car ownership, number of commute trips, and a sense of belonging to neighbourhoods, entertainment place, and residential location choice. Finally, a positive correlation was reported between urban sprawl with higher income, elderly student, financial dependent students, and car dependency trips, while urban sprawl was negatively related to the use of public transit.

Keywords: urban sprawl, socioeconomics factors, travel pattern

 

 

1.    INTRODUCTION

 

Urban sprawl is considered as a specific urban form which is characterised as a low-density, single land use, car-dependent, discontinues or leapfrog new development, or uneven pattern of growth between urban population and urban areas [14, 18, 26, 45]. Although urban sprawl is well-known as an American urban form, it is regarded as a controversial topic among urban planners and decision-makers in different parts of the world, which has been discussed as a global problem. Urban sprawl is related to environmental, social, and economic issues. For instance, urban sprawl can influence the environment by increasing CO2 emission in more long-distance commuting trips or using natural resources as a new development area [16, 26, 49].

More distances are driven by car, less use of walking and public transport, long-distance between home and workplaces, lack of sense of place, low density and single land-use area are highly related to different social, economic, cultural, and geo-demographic circumstances, which should be considered in urban planning. Although, some studies focused on the relationship between urban form and travel behaviour [6, 16, 17, 19, 20, 26, 30, 38-40, 43, 49], more should be conducted to evaluate urban sprawl as an urban form which is correlated with various socio-economic indicators and travel patterns based on different geographical contexts. The correlation among urban sprawl, individual socio-economic variables, and travel behaviour clarify a better understanding of modelling urban sprawl and provide more practical policies for urban planners. Urban sprawl has been a matter of concern not only in developed countries but also in developing parts and emerging economies. Although, there is no agreement in definition and its quantification, the characteristics of urban sprawl were discussed by urban researchers [4, 10, 14]. However, less attention has been paid to the mechanisms, processes, patterns, and factors related to urban sprawl in different parts of the world. On the other hand, urban form plays an important role in travel literature [11, 16, 19, 47]. Hence, urban sprawl as a specific urban form associated with socioeconomic determinants and travel behaviours based on economic, governmental, and geographical context. Although, urban sprawl was studied in post-socialist cities [8, 21, 31, 35, 42], most paid more attention to the differences between socialist and post-socialist cities and impact of transformation on urbanisation in these cities. However, the socioeconomic determinants, travel patterns, and their association with urban sprawl were not considered in the literature.

Inhabitants living in sprawled neighbourhoods suffer more from traffic congestion, air pollution, long-distance commuting, lack of efficient public transport, lack of social interactions than those who live in compact urban forms. Based on sustainable development, studying the correlations between urban sprawl and its different factors including individual, household and, socio-economic characteristics, self-selection of the home place, mobility, urban form and perceptions in the Polish city can provide a better understanding of urban sprawl and its determinants in the post-socialist city. Urban sprawl is observed across the continent, although, European urban areas are more compact than Western cities in the United States. Recently, Urban sprawl has been developed in the post-socialist context. However, no study has been conducted based on reliable and disaggregated data.

Thus, this study aims to evaluate the interrelations between urban sprawl, mobility choices, socioeconomics, and perceptions of university students in Cracow, which represents Polish’s larger city, as well as considering context-specificity of these factors by conducting a descriptive comparison between the results of this paper and determinants of urban sprawl in a different context. In addition, there is a shortcoming in modelling effective factors on urban sprawl based on disaggregated data in Eastern-European countries compared to the western countries. Therefore, urban sprawl and urban form features were measured for each participant individually in this study, and the features of urban sprawl were simulated.

The remainder of this paper is as follows; Section 2 reviews the determinants of urban sprawl in the sprawl literature, as well as in the context of emerging economies and post-socialist cities, and an overview of urban sprawl in Polish cities. Section 3 describes the brief explanation of the methodology. Section 4 presents the main findings of the results. While, section 5 demonstrates urban sprawl and its determinants contextually and discusses the findings in Poland and international literature, as well as the implications of urban sprawl in Poland. Finally, the conclusion is presented in Section 6.

 

 

2. THE RELATION BETWEEN URBAN SPRAWL AND SOCIETAL PHENOMENA

 

There is no unique definition of urban sprawl. The term "urban sprawl" describes low-density, single land use, inefficient, suburban development around the edge of cities, leapfrog and discontinues urban forms distinct from those identified in the United States. Urban sprawl is defined as low-density, leapfrog development in a relatively pristine setting [4]. Galster et al. (2001) argued that urban sprawl is a pattern in an urban area which has a relatively low level of eight dimensions including density, continuity, concentration, clustering, centrality, mixed uses, and proximity [14]. Urban sprawl as a type of urban form, which is related to socioeconomic and travel pattern, has attracted a lot of attention during recent decades. In addition, metropolitan expansion, low density, diversity of land use as three dimensions of urban sprawl were evaluated along with their relation to flow network, mode-choice, and commuting time in the metropolitan area of Madrid [15]. Further, the correlation between socio-demographic features of households, the individual, urban structure at both home and workplace with the travel pattern in Beijing, China was considered. The results indicated a significant correlation between tour-travel decision and socioeconomic attributes [24]. However, Zhao (2010) considered the sprawl pattern of expansion and the increase in distance trip and car-dependent travel in Beijing as a megacity [49]. Additionally, the differences between socioeconomic causes of urban sprawl in China and western urban sprawl were investigated to determine the drivers of urban sprawl in China. Thus, urban population density, gross domestic product per capita, and industrial structures in China were considered as main determinants [26]. Hamidi et al. (2016) applied principal component analysis and cross-sectional data to operationalise sprawl/compactness in four contributors of urban sprawl including development density, land use mix, centralisation of activities, and street accessibility [19]. Ewing et al. (2016) examined the relationship between urban sprawl and upward mobility for Americans. The result indicated that upward mobility is considerably higher in compact areas than sprawling areas due to more accessibility to better job [12]. European Environment Agency (2016) measured the urban sprawl between European countries, along with urban sprawl determinants and its impact on natural resources [9]. Guerra et al. (2018) applied a multinomial model to examine the preference of commuters by different transport modes based on the role of age, income, education, and information about urban area [16]. Travisi et al. (2010) analysed the causal relationship between spatial developments, the sprawling pattern of the urban area and travel movement in seven major Italian cities using the mobility impact index, the cross-section data, and the Casual Path Analysis (CPA). The empirical results indicated the high impacts on travel behaviour of less compact and mix-used cities [44]. Shorter distance to the city centre and high-density neighbourhoods are associated with lower trip duration to work or education. Thus, the residents in compacted neighbourhoods are more willing to use active mode transport (walking and biking) than those living in sprawled neighbourhoods [32]. The socio-economic contributors of urban sprawl were modelled by employing regression model of urban sprawl metric in Switzerland. Furthermore, the increase in population and income, and the changes in social patterns such as single household and age resulted in creating sprawl [32]. In another study in Switzerland, the components of the urban sprawl metric developed by [22, 23], were applied in the cross-sectional regression model to study urban sprawl, along with its socio-demographic factors such as population, income, commuting pattern, price of agricultural land, homeowner rate, age, and single household [47]. Urbanisation in the 20th century was associated with modernisation and industrialisation as two primary socio-economic trends [42]. Thus, post-socialist cities were influenced by these two crucial trends like other parts of the world but at different speeds and time. Evaluating the cities in socialist countries indicated that the political economy is considered as a significant factor in shaping urban areas. Cities have some similarities and differences between capitalism and socialism in some aspects. In socialist cities, political and economic forces drove the built environment, land use, and patterns of allocation. However, there are some similarities in the spatial patterns of both capitalism and socialism since industrialisation and modernism. Socialist cities captured the features of capitalism context with the delay of a few years. To understand the post-socialist city better, it is necessary to study socialist cities' former Iron Curtain in East-Central Europe. Compared with the capitalist cities, especially American types, cities were denser in the socialist period. However, the industrialisation of the socialist economy led to boom industrial centres, which attracted rural migrants to the cities. Hence, socialist cities faced a fast growth in the urbanisation [21].

Socialist cities did not experience urban sprawl like the United States. Inhabitants in socialist cities were accommodated in mass-housing complexes between 1960 and 1980 in urban edges [5]. Socialist cities were governed through the top-down centralised control of regional development. Spatial public space and urban development were concentrated in target areas by land development policies of a hierarchically organised system. In addition, the construction of massive housing estates at the urban edge, a dense network of public transportation, and allocation of services in neighbourhoods, districts, and urban centres according to a hierarchically organised system characterised socialist cities [3, 42]. The urban development of larger cities in East-Central Europe has been transformed by collapsing socialism in the 1990s. The interactions between the socialist urban structure and new ideologies, the market economy, and the democratic governments have shaped post-socialist cities. Transformation in spatial patterns of the urban area is considered as one of the dimensions of transformation, along with the changes in social, political, cultural, and economic aspects of East-Central Europe. Suburbanisation has become the most familiar process of changing the landscapes of post-socialist cites. Rapidly developing suburban sprawl and residential segregation are regarded as a consequence of uneven development and following globalisation in post-socialist cities [35, 42]. The clear edge of Eastern/Central European city was vanished by the end of socialism. Land-cover changes studies in Europe indicated a high rate of sprawling in post-socialist cities than Western Europe [9]. Urban sprawl has been enabled by transformation policies such as urban land, housing, and the properties have been revalued as economic goods due to the cancelling restrictions on private ownerships. In addition, car ownership increased in most parts of Eastern/Central Europe, which affected public transport, urban structure, especially in the urban form [21, 31]. After falling socialism in 1989, some changes occurred in special and social aspects of urban structures. Post-socialist suburbanisation and decentralisation of population and employment in Polish cities were considered as an urban form. Hence, Polish urban sprawl is in the early stages. Polish suburbanisation is identified by the large distances from the city centres, the extension of single-family housing, and the desire to live in the suburban zone compared to the core cities [41].

New Polish urban structures in the form of urban sprawl were driven by liberalising spatial planning, uneven growth, and socio-economic transformation [25, 27, 28]. The government policies during socialism emphasised relocation of workplaces outside the boundaries of Polish cities. Thus, the low quality of coordinated pattern of land development, the transformation of agricultural land, and degradation of natural resources are considered as the consequences of these policies. Economy transition, especially in land market, led to the escalating price of land in large Polish city centres. Hence, more agricultural lands were sold in the periphery of the cities [27]. Moreso, Polish cities faced massive growth in car ownership since 1990, which is related to the territorial development of urban areas and congestion on the roads [2]. Furthermore, privatisation and residential suburbanisation were promoted by national policies to support investment in housing during the 1990s. The lack of public facilities and infrastructure in new development areas is considered as one of the most critical issues accompanying urban sprawl in Poland because Polish local governments are obligated to pay high compensation to landowners for allocation of public facilities [29]. Mantey and Sudra (2019) studied 16 types of post-socialist suburbs in Warsaw, Poland, among which four planned the pattern of housing and high-density populated based on street grid neighbourhood. Contrarily, the other 12 categories followed leap-frogging new neighbourhood based on cul-de-sac street network, which was considered as sprawl pattern in the literature [29].

 

 

3. METHODOLOGY

 

3.1. Research objectives and hypotheses

 

This study aims to identify the determinants of urban sprawl in Cracow as an example of a large Polish city and determine whether factors such as transportation, socioeconomics, human perceptions, residential location choice, and urban form features of Cracow university students are correlated with urban sprawl or not. In addition, it seeks to determine the differences between these correlations in the case study compared with those of high-income countries. Following, these objectives illustrate how urban sprawl and its determinants, especially the relationship between urban sprawl and travel behaviour, may be different in various social, historical, cultural, and economic contexts.

The main hypothesis is that a wide range of factors including commute and non-commute travel patterns, socioeconomics, human perceptions, residential self-selections, and urban form features are significantly correlated with urban sprawl in Cracow, Poland. Furthermore, the correlation between urban sprawl and travel behaviour and socioeconomic factors in Cracow as a post-socialist city is different from those in high-income countries like North American cities, Western Europe, and Australia. Hence, urban sprawl follows different patterns and determinants in the different parts of the world based on various complex conditions between developed and developing countries and emerging economies.

 

3.2. Data and variables

 

To test the hypotheses, 1,288 online Polish-language questionnaires were collected and validated during the winter of 2019. The questionnaires were randomly distributed among university students of Cracow, which indicated home and university places in different districts of Cracow. It included 23 questions based on individual and household socioeconomic factors, commute and non-commute travel patterns, perceptions of the urban environment, and mode choice of transportation. In addition, urban form and configuration of street network variables were extracted by using Geographical Information System (GIS) version 10.3 for both home places and university, respectively. Therefore, 24 variables were developed including 20 socioeconomic factors, travel behaviour and perception variables such as gender, age, main daily activity, driving license, car ownership, financial dependency status, gross income per month, number of commute trips per week, mode of transportation of commute trips per week, number of non-commute trips including those for shopping or recreation per week both inside and outside of the neighbourhood, daily shopping area, mode choice for shopping and recreational trips in both inside and outside of the neighbourhood, the frequency of public transport use, sense of belonging to the neighbourhood, entertainment place, the perception of the quality of social-recreational facilities of the neighbourhood, residential location choice, length of time living in the current home, and two urban form variables for each home and university according to the home and university place specified by each student. The respondents indicated the nearest intersection to their home and university, and then their address was pinned in two layers in the Google Maps as separate layers for home and university. The layers were exported in KML format to get the GIS-ready layers. Hence, each point in the layer of home and university represents each university student's home and university, respectively. Afterwards, the 600-metre catchment area based on the street network for each point was measured to compute other variables in the catchment area. The inverse of Building Coverage Ratio (BCR) was calculated around home and university. Furthermore, the free shapefiles of Cracow were downloaded from Open Street Map (OSM) in the computing Shannon entropy and inverse of building coverage ratio, and network dataset were built in ArcMap version 10.3. All variables are presented in Tab. 1.


Tab. 1

Variables

 

Coding

Source / Quantifications

Variables

Men=1, female=2

Extracted by questionnaire

Gender

-

Extracted by questionnaire

Age

Only student=1

Work and study=2

Extracted by questionnaire

Main daily activity

No=0     Yes=1

Extracted by questionnaire

Driving license

Without car=0

1 car &2 cars and more=1

Extracted by questionnaire

Car ownership

No=0, yes=1

Extracted by questionnaire

Financial dependency status

Below 1000 PLN=0

From 1001 to 2000 PLN=1

Extracted by questionnaire

Gross monthly income

-

Extracted by questionnaire/each respondent indicated the number of trips for last week

Number of commute trips

By car=1, other mode choice=0

Extracted by questionnaire

Mode of transportation for commuting trips

-

Extracted by questionnaire/each respondent indicated the number of trips for last week

Number of trips for shopping or entertainment

Outside=1, inside=2

Extracted by questionnaire

Daily shopping area

By car=1, other mode choice=0

Extracted by questionnaire

Mode choice for shopping/entertainment inside the neighbourhood

By car=1, other mode choice=0

Extracted by questionnaire

Mode choice for shopping/entertainment outside the neighbourhood

Almost never=0, rarely=0, a few times per month=0, a few times per week=1, every day=1

Extracted by questionnaire

Frequency of public transport use

No=0, yes=1

Extracted by questionnaire

Sense of belonging to the neighbourhood

No=0, yes=1

Extracted by questionnaire

Attractiveness of shops

Far away=1, inside my neighbourhood=2

Extracted by questionnaire

Entertainment place

Not attractive=0, little attractive=0, acceptably attractive=1, medium=0, very attractive=1

Extracted by questionnaire

Quality of social/recreational facilities

The house was affordable to buy=1, the house was near my work=0, the surrounding environment is attractive=1, the house will have higher price=1, to be near my relatives=1, l live here since was born=1

Extracted by questionnaire

Residential location choice

-

Extracted by questionnaire

Length of time living in the current home

-

Measured by Shannon entropy/Cracow divided to 4256 grids in GIS,  then computed by employing zonal extension and spatial analysis tools, home points joined to grids based on common spatial location to get their amount of disaggregated Shannon entropy

Urban sprawl around home

-

For each respondent who indicated home place, the 600-metre catchment area calculated according to the street network, thereafter the area of buildings divided by the area of the catchment area and then one divided by the amount of BCR in each catchment area.

Inversed Building Coverage Ratio (BCR) around the home (%)

-

Measured by Shannon entropy/Cracow divided to 4256 grids in GIS,  then computed by employing zonal extension and spatial analysis tools, university points joined to grids based on common spatial location to get their amount of disaggregated Shannon entropy

Urban sprawl around university

-

For each respondent who indicated university place, the 600-metre catchment area calculated according to the street network, thereafter the area of buildings divided by the area of the catchment area and then one divided by the amount of BCR in each catchment area

Inversed Building Coverage Ratio (BCR) around the university (%)

 

 

3.3. Case study

 

Cracow is located in the south of Poland on the Vistula River, the largest river in Poland. The city has 372 km² areas with its suburban areas and is populated by nearly one million inhabitants by considering suburban population. Cracow is the second-largest city and one of the oldest cities in Poland. In 2016, Cracow was populated by 765,300 inhabitants (Female= 53%, Male=47%) [38]. It was considered as an urban centre from the 9th century. Furthermore, Cracow is one of the biggest academic centres in Poland with almost 160,000 students. There are 21 higher education institutions (Public=10, Non-public= 11) in the city, among whom the students of public universities constitute the vast majority (86%) [48]. Among the public universities, the majority of students study at Jagiellonian University, while Andrzej Frycz Modrzewski Cracow University has the biggest share among non-public universities. The highest number of variety was related to the students of technical universities, specifically about 28%. The universities have been of significant impact on the potential of Cracow as a scientific, cultural, economic, and political centre since 1985 [34]. After 1989, Cracow faced a series of transformations. The modern economic and political system increased the processes of urbanisation in the city, especially when Poland joined the European Union in 2004. Extension of the urban area in Cracow happened in the 20th century when Cracow was extended five times [38]. The rapid development in Cracow is a result of government policies on the construction of housing estates. The first spatial development took place in 1945 and the second important extension happened in 1973 in all directions [50]. Political transformation from socialism to post-socialism could affect Cracow in four aspects such as centralisation to decentralisation, industrialisation to deindustrialisation, underrated to acclaimed city, and the inclusion of large outside areas to city versus suburbanisation. The development of public transportation network, improvement of urban facilities, as well as lower price of land in the outskirts, resulted in extending peripheral areas in Cracow [38]. Ziobro (2019) referred to urban sprawl in Cracow, especially towards the north. In the northern part of the city, spatial structure transformed from villages into a pre-urban structure with scattered pattern and residential land driven by shortcoming urban planning system and uneven development [50].

 

Fig 1

 

Fig. 1. The distribution of homes in Cracow

 

Fig2

 

Fig. 2. The distribution of university facilities in Cracow

 


3.3. Case study

 

Cracow is located in the south of Poland on the Vistula River, the largest river in Poland. The city has 372 km² areas with its suburban areas and is populated by nearly one million inhabitants by considering suburban population. Cracow is the second-largest city and one of the oldest cities in Poland. In 2016, Cracow was populated by 765,300 inhabitants (Female= 53%, Male=47%) [38]. It was considered as an urban centre from the 9th century. Furthermore, Cracow is one of the biggest academic centres in Poland with almost 160,000 students. There are 21 higher education institutions (Public=10, Non-public= 11) in the city, among whom the students of public universities constitute the vast majority (86%) [48]. Among the public universities, the majority of students study at Jagiellonian University, while Andrzej Frycz Modrzewski Cracow University has the biggest share among non-public universities. The highest number of variety was related to the students of technical universities, specifically about 28%. The universities have been of significant impact on the potential of Cracow as a scientific, cultural, economic, and political centre since 1985 [34]. After 1989, Cracow faced a series of transformations. The modern economic and political system increased the processes of urbanisation in the city, especially when Poland joined the European Union in 2004. Extension of the urban area in Cracow happened in the 20th century when Cracow was extended five times [38]. The rapid development in Cracow is a result of government policies on the construction of housing estates. The first spatial development took place in 1945 and the second important extension happened in 1973 in all directions [50]. Political transformation from socialism to post-socialism could affect Cracow in four aspects such as centralisation to decentralisation, industrialisation to deindustrialisation, underrated to acclaimed city, and the inclusion of large outside areas to city versus suburbanisation. The development of public transportation network, improvement of urban facilities, as well as lower price of land in the outskirts, resulted in extending peripheral areas in Cracow [38]. Ziobro (2019) referred to urban sprawl in Cracow, especially towards the north. In the northern part of the city, spatial structure transformed from villages into a pre-urban structure with scattered pattern and residential land driven by shortcoming urban planning system and uneven development [50].

 

3.4. Analysis method

 

The correlation of urban sprawl and 20 socioeconomics, travel patterns, and urban form variables were modelled by Weighted Least Squares (WLS) regression modelling method in IBM SPSS version 22. Urban sprawl was measured using Shannon entropy. By considering the definition of urban sprawl, there are a lot of challenges regarding the methods for measuring urban sprawl in the literature [1, 13, 22, 23, 33]. Bhatta et al. (2010) argued different methods for measuring urban sprawl and indicated that the Shannon entropy is the most reliable metric for measuring urban sprawl [1].

Yeh and Li (2001) discussed that Shannon entropy can measure the density of land development in a set of buffer zones that were drawn around the city centre. The value of entropy is always between 0 and and calculated by the following equation: