Impact of Polycentric Urban Systems on Intra-regional Disparities: A Micro-regional Approach

Transcription

1 Impact of Polycentric Urban Systems on Intra-regional Disparities: A Micro-regional Approach JIŘÍ MALÝ a a Department of Environmental Geography, Institute of Geonics AS CR, Brno, Czech Republic The Version of Record of this manuscript has been published and is available in European Planning Studies, (2016), 24(1): , ABSTRACT From the normative perspective of EU regional policy, polycentric spatial development has been seen as an effective tool to overcome regional disparities since the adoption of the European Spatial Development Perspective. However, the impact of a polycentric spatial structure on equal spatial development is ambiguous and still waits for its clarification. This paper examines the relationship by focusing on the local scale, where medium-sized towns play a crucial role as local centres in everyday human lives. Using functional regions of the Czech Republic, this paper defines polycentric urban systems at the micro-regional scale. Subsequently, the level of mono/polycentricity is compared with intraregional disparities. Based on the results of the analysis, it can be argued that the connection of polycentricity with balanced spatial development is strongly determined by selected indicators. However, the findings do not provide evidence about the strictly positive impact of polycentricity on intra-regional disparities. In a light of these results, the conclusion is rather sceptical about the unique role of polycentricity in spatial development and due to the lack of similar studies, further research is recommended. Keywords: spatial development; polycentricity; intra-regional disparities; Czech Republic 1 Introduction After the adoption of the European Spatial Development Perspective by EU member states at the end of the past millennium, polycentric development has begun to be seen as the core concept of EU regional policy (CEC, 1999). Reaching polycentricity has been even more emphasized by territorial cohesion as a goal of EU Cohesion Policy (Faludi, 2010). Polycentric development is now viewed as a process that contributes to lowering regional disparities and to the overall economic growth of the European territory (CEC, 1999; EU Ministers responsible 1

2 for Spatial Development, 2011). According to the Territorial Agenda of the EU 2020 (EU Ministers responsible for Spatial Development, 2011), European regions are facing several challenges which should be transformed into potentials for sustainable and harmonious territorial development. The first territorial priority in this matter is to promote polycentric and balanced territorial development. It states that where the most developed cities and regions within Europe cooperate as parts of a polycentric pattern they add value and act as centres contributing to the development of their wider regions (EU Ministers responsible for Spatial Development, 2011, p. 7). Similarly in ESPON programme studies, polycentricity across cities and regions is seen as a necessary condition for a better distributed economic and social growth (ESPON, 2014). Polycentric development is a vision not only of European territorial development, but also of many European countries (Meijers et al., 2005). European documents relating to spatial planning mostly address polycentricity in the context of large territorial units (macro-regions and cross-border regions), where the emphasis is put on the territorial competitiveness of the EU territory, improving the performance of cities in European and global competition, and promoting economic prosperity of second-tier metropolitan areas and cities that lag behind the largest metropolises - typically a situation in new EU member states where less-developed and large cities lie in the shadow of supported capitals (CEC, 1999; EU Ministers responsible for Spatial Development, 2011; ESPON, 2014). When the national scale and smaller regions are considered, the EU documents stress the importance of partnership between local centres and rural areas, and mutual complementarity of functions used on a daily basis which led to effective cooperation (ESPON, 2005; EU Ministers responsible for Spatial Development, 2011). Thus, the principles of polycentric system functioning differ depending on a geographical scale. In the case of micro-regions, the absence of a stronger centre may result not only in a more polycentric pattern but also in particular dependence on an external centre. The popularity of polycentricity has grown in spite of (or perhaps because of? 1 ) the unclear meaning of the concept (Molle, 2007). Polycentricity commonly refers to even distribution of human activities across space and the existence of multiple centres in one area (Kloosterman and Musterd, 2001). The core elements of polycentricity are urban systems since the polycentric structure of human activities may be best recognized through the spatial organization and functional interconnectedness of urban centres (Kloosterman and Musterd, 2001). It is a state of the urban system in which several nodes are interdependent and interact with one another via various types of flows (Parr, 2004; Limtanakool et al., 2009). In other words, the urban system lacks one dominant node and forms a network of cities. Polycentric urban systems may benefit from positive externalities associated with large agglomerations and simultaneously avoid negative aspects of urban sprawl (Parr, 2004). Thus, development of urban networks is the assumption for stimulating regional economic growth (CEC, 1999; ESPON, 2014). 2

3 A unique position of polycentric development within European spatial development is demonstrated by a plethora of studies and papers focused on the concept that can be divided into two types. The first are theoretical papers discussing the sense and importance of polycentric development (e.g. Kloosterman and Musterd, 2001; Davoudi, 2003; Parr, 2004; Faludi, 2005) and the second are papers stressing the measurement and assessment of polycentricity mostly using specific case studies (e.g. Green, 2007; Meijers, 2007; Limtanakool et al., 2009; Goei et al., 2010; Burger and Meijers, 2012). Much less attention is paid to the actual connection of polycentric urban systems to regional disparities (see Meijers and Sandberg, 2006; Veneri and Burgalassi, 2012). However, the merit of such studies is high since the feature of polycentricity to overcome regional disparities and contribute to a balanced spatial development is crucial. The present study is focused on functionally self-contained regions, that is, the microregional level, where intensive inter-urban interactions and everyday human activities occur. Regional and local centres are accelerators of development at this spatial scale (EU Ministers responsible for Spatial Development, 2011). The main aim of this paper is to assess the relation between urban system structure (the level of polycentricity) and the level of intra-regional disparities. Can we confirm the assumption that the higher the level of polycentricity is, the lower the internal disparities are? Is it possible to observe some changes depending on the population size of the region? Firstly, the paper focuses on urban systems theories which are strongly relevant in the introduction of the polycentricity concept. After a brief description of polycentricity, the crucial aspects of the relationship between polycentricity and regional disparities are subjected to a closer inspection based on the case study of Czech micro-regions. 2 Theoretical Background 2.1 From Central Place Model to Polycentric Urban Systems Cities have been seen as engines of economic growth since the first theories of organization of urban systems were formed (Christaller, 1933; Lösch, 1944). Cities have not been perceived as isolated units, but as centres providing specific services to surrounding localities. In theories of central place systems, the interactions between centres and rural areas were limited to trade and consumer behaviour (Berry and Pred, 1965). The principal centre s market area contained surrounding municipalities which tended to be places of labour supply depending on the centre in terms of provision of goods and services. Moreover, attention was mostly paid to the size and distribution of cities. The central place model pointed to the hierarchical urban order with a clearly dominant centre and other lower ranked municipalities (e.g. Christaller s Central place theory). At the intra-urban level, the monocentric city was represented by the well-known Burgess s Concentric zone model (Burgess, 1925). While the urban core was the loci of economic activities, residential function was fulfilled by outer zones. Hierarchical organization 3

4 of the city expressed by commuting flows from suburbs to the city centre resulted in nodal character of the model. At both geographical levels, the monocentric system is therefore characterized by core/periphery duality. Traditional central place models assumed that all urban systems have a rather monocentric character and the emphasis was put mainly on size-ranking and spatial distribution of cities. According to Parr (1987), the aspect of commuting is absent in the standard model of the central place system but it can be viewed as a result of an inequality in the demand and supply for labour at each hierarchical level. While the centre is characterized by an excess labour demand, the small places are characterized by an excess labour supply (Burger and Meijers, 2012). Based on the general urban systems theory, claiming that any urban system is formed by interdependent cities and functional interactions between them (Berry, 1964; Pred, 1977), central place models have been extended to general hierarchical urban models, which in some cases provide less strict definition of relations between the centre and hinterland (Berry and Parr, 1988; Coffey et al., 1998 provide an overview). In other words, modified hierarchical models aim at a more realistic representation of spatial organization of urban systems. But the interdependence of cities does not have to represent only the centralized urban system. The interactions can be more balanced with less apparent hierarchy. Thus, the central place models are becoming unable to describe the changing urban systems at all spatial scales (Davoudi, 2003). New spatial and functional organization of urban systems becomes less hierarchically structured. Deconcentration of housing and economic functions within individual cities has substantially modified the traditional monocentric city structure. At the intra-urban scale, suburbs emerged into local centres which are now parts of the metropolitan area and fulfil not only the resident but also the economic function. Multidirectional commuting and the existence of multiple centres within the city gave rise to polycentric organization of the inner urban structure. However, the emergence of such networks is not limited to the intra-urban scale, but it also concerns inter-urban linkages (Meijers, 2007). With increasing geographical scope of social and economic activities (e.g. commuting and business relations), the functional linkages between original and new urban centres generate a more complex pattern of spatial organization of society (van der Laan, 1998; Kloosterman and Musterd, 2001). A variety of factors lies behind such evolution of urban systems (see e.g. Brotchie et al., 1995; van der Laan, 1998; Hall and Pain, 2006). The spatial organization of society has been transformed in particular by the structural process of globalization, which involves the creation of complex world-wide webs in which economic actors such as producers, consumers, workers and investors are linked together in both physical and virtual ways (Kloosterman and Lambregts, 2001, p. 717). In the context of the post-industrial economy and globalization processes, the role of metropolitan areas and major agglomerations as economic, social and 4

5 administrative centres within urban networks is growing. The significance of the urban centre is not expressed only by its size (e.g. economic, social and cultural), but primarily by its position within urban networks. The space of places still exists (people live, work or shop in specific places), but it is included in the broader and more dominant system of space of flows (Castells, 1996). These are the flows of capital, information or technology, or the interaction of different firms and organizations. From the perspective of relational space, the actors (people, cities or firms) do not exist per se, but their existence and functioning are conditioned and formed by interaction with other actors. 2.2 Conceptualizing Polycentricity Until recently, a clearly defined hypothesis explaining the functioning of urban systems (and replacing central place theory) had not been accepted (Camagni, 1993). Nevertheless, references to non-monocentric forms of urban systems can be traced back to the first half of the twentieth century. In the context of city-region, Geddes (1915) used the word conurbation for spreading of cities into the countryside. The concept of dispersed-city was discussed few years later (Burton, 1963), and then the network model was invented as a new model of spatial organization (Camagni, 1993; Capello, 2000). Although these concepts are based on different theoretical backgrounds, they have one thing in common. All of them were closely connected to the issue of what now is called polycentricity, the situation in the urban system characterized by several similar-sized centres (or sub-centres, depending on the spatial scale) in close proximity that are connected by balanced and multi-directed flows of information, goods and people. Although there have been several attempts to clarify the concept of polycentricity (e.g. Hague and Kirk, 2003; Turok and Bailey, 2004; Lambregts, 2009; Sýkora et al., 2009; Groth et al., 2011), a widely acceptable definition is still missing. An important feature of polycentricity, which contributes to confusion regarding the concept understanding, is its dependence on the scale and different spatial arenas (see e.g. Hall, 2002; Taylor et al., 2008; Vasanen, 2013). The internal structure of cities is examined at the intra-urban scale. Attention is here paid to the processes of population and economic decentralization that have resulted in the creation of residential and commercial suburbs and new forms of spatial organization of society within the administrative city borders (Anas et al., 1998). At the inter-urban scale, the focus is on grouping of distinct cities without a clear dominant centre and with mutual interactions. The polycentric urban system at the inter-urban (regional) scale is defined by the concept of polycentric urban region (Davoudi, 2003; Parr, 2004; Green, 2007). Polycentricity at the national and European levels is proclaimed by policy-makers as a path to combat regional disparities and to achieve more balanced development (CEC, 1999; EU ministers responsible for Spatial Development, 2011). At a national level, importance is attached to the development of medium-sized cities, which should contribute to reducing polarization between major cities, metropolitan areas and 5

6 regional centres. Supporting polycentricity at the European level is mainly focused on metropolitan areas situated outside the current economic core 2 that can restart their economic growth by forming networks and linkages with other cities (EU Ministers Responsible for Spatial Development, 2011). The aim is to create more globally competitive and economically strong regions, which would ensure the future of the European economy (Davoudi, 2003). It is evident that the issue of scale significantly affects the meaning and conceptualization of polycentricity. One of the possible results of such ambiguity is that a polycentric system at one scale may be considered as monocentric at another scale (Hall and Pain, 2006). The presented study addresses the micro-regional approach (reasons are discussed in the following section) and hence polycentricity will henceforth be conceptualized at the inter-urban scale. A polycentric urban system can be characterized by a greater number of centres located in the same area, none of which is significantly dominant. A polycentric urban system is distinguished by the distribution of economic and economically relevant functions over the spatial system in a balanced way, resulting in a less urban hierarchy (Meijers et al., 2005). However, the existence of multiple centres situated in close proximity does not mean that these centres are strongly functionally linked to each other (Albrechts, 2001). Thus, the polycentric urban system is expressed not only by a morphologically balanced structure, but also by infrastructural and functional complementarity of centres. The relational dimension of polycentricity is then characterized by balanced functional interaction between urban centres. Balanced relations could be simply imagined as functional linkages that are not directed at one centre, but that rather create mutual (reciprocal) and criss-cross interactions (Goei et al., 2010). Green (2007) presents a noteworthy study of functional polycentricity using an approach based on a formal network analysis technique. Networks comprise actors (e.g. cities, people, businesses and telephones) and relations between them (e.g. commuting roads and telephone lines), while these linkages are functional in nature. Green (2007, p. 2101) describes functional polycentricity by the statement that a collection of nodes, be they cities, small businesses or people, must be functionally connected and balanced if they are to be considered a system. Besides the balance of functional relations, Green (2007) adds the dimension of network density 3 which reflects the level of connection between urban centres (or functional interdependency of urban centres). The level of urban system polycentricity can be the result of the morphological structure, functional linkages and network density. As Burger and Meijers (2012) have shown, although the level of morphological and functional polycentricity is interdependent to a certain extent, the level of network density is associated with different aspects of urban systems. When evaluating polycentricity, it is essential to distinguish different aspects of spatial organization of urban systems and consider them separately. As mentioned above, the highly integrated network of separate cities can be considered as a polycentric (or polynuclear) urban region (PUR), a concept which has become widely 6

7 discussed in the academic and policy debates (Kloosterman and Lambregts, 2001; Davoudi, 2003; Parr, 2004; Turok and Bailey, 2004; Green, 2007). Parr (2004) distinguishes a PUR from the other regions mostly by the small distance between cities, by no domination of any city size and by the intensity of the linkages and specialization of cities that is higher than that in other regions (e.g. Randstad Holland is considered to be the archetypal example of a PUR - see Lambregts, 2009). Defining small distance and intensity of linkages depends on the specific geographical context and population density. Also, the minimum size of a PUR in terms of market size and necessary human capital is questioned (Capello, 2000). Leaving aside methodological issues, the economic significance of PURs is seen in their specific spatial and functional structure. Meijers (2008) argues that PURs are able to develop new competitive advantages due to enjoying economies of scale, scope and complexity and avoiding agglomeration diseconomies, high land prices, congestions and environmental pollution. From a more social perspective, the specialization of centres should contribute to plurality of choices primarily in terms of job opportunities. Such assumptions about polycentricity advantages go hand in hand with a rather unjustified conviction of the European and some national policymakers about polycentricity and its positive impact on regional disparities Polycentricity as a Path to Overcome (Intra-)Regional Disparities? Up till now, research on polycentricity has been focused on conceptual and analytical issues. It can be argued that in the context of theory and methods, the knowledge about polycentricity has reached a certain level of saturation. This idea is also supported by Burger and Meijers (2012), who hope that polycentricity research is no longer dominated by conceptual issues, but that focuses on whether the alleged benefits of polycentricity and polycentric development hold true or not (Burger and Meijers, 2012, p. 1145). Taking into account that polycentric development is a key part of EU cohesion policy (EU Ministers responsible for Spatial Development, 2011), assessment of its real contribution to reducing regional disparities seems to be the most important part of the polycentricity research agenda. Paradoxically, the assumed vital aspect of polycentricity to overcome regional disparities is evaluated lastly. Although regional and spatial policies see the polycentric structure of urban systems as a situation contributing to lowering regional disparities, they do that without sufficient empirical basis. Meijers and Sandberg (2006) offer an empirical analysis of the relation between polycentricity and regional disparities in European countries. Their study shows that between most measures of polycentricity and regional disparities, there is no significant relationship. However, significant correlations in the opposite direction can be observed between some measures. In such cases, the monocentric urban system is characterized by less regional disparities. Their results are therefore in contrast to what have been expected. Not surprisingly the authors conclude that their results call for a critical reflection on the assumed link between polycentric development and its contribution to diminishing regional disparities (Meijers and 7

8 Sandberg, 2006, p. 18). Häzners and Jirgena (2013) used a similar approach to test the hypothesis about the positive impact of polycentricity on regional disparities. Their results show that higher polycentricity cannot be associated with less regional disparities. Another attempt to tackle this issue is the one made by Veneri and Burgalassi (2012). They investigate the relationship between the level of regional polycentricity and distribution of the key economic variables of performance (competitiveness, social cohesion and environmental sustainability) across Italian NUTS 2 regions. Especially social cohesion decreases with increasing level of polycentricity. In measuring income distribution, the authors find out that correlation exists between polycentricity and a more unequal income distribution (i.e. the opposite of expectations). Even the levels of productivity and environmental sustainability are not significantly correlated with polycentricity. In the end, the authors argue that a polycentric spatial structure can be hardly considered as an effective tool to ensure an equal spatial development concept, at least in the context of Italian NUTS 2 regions (Veneri and Burgalassi, 2012). The existing studies are rather sceptical about the crucial role of polycentricity in reducing territorial disparities, at least if a national and regional approach is considered. Certain limitations of the above-mentioned studies can be the comparison of simply administrative territorial units. NUTS 2 regions are divisions of countries used mainly for statistical purposes and their delimitation very often ignores functional aspects which form the spatial organization of socio-economic activities. This is obvious especially when the principles of functional polycentricity are taken into account. Thus, possibly strong cross-border relations may rapidly change the structure of NUTS 2 urban systems and consequently the level of polycentricity. In this respect, inappropriate territorial units disrupt also the morphological picture of an urban system structure. Even if the morphological approach is used, it is necessary to assess the level of polycentricity in areas that represent a functionally integrated system of urban centres and other municipalities. In the case of NUTS 2 regions, one or more centres belonging to one region can be in fact functional parts of a neighbouring region which can again change polycentricity measures. Notwithstanding the issue of delimitation of territorial units, these studies are important steps in building an empirical evidence base which is needed for polycentricity evaluation. Unfortunately, additional research on linkages between polycentric development and territorial disparities is lacking. Especially the impact of polycentricity on intra-regional disparities has not yet been subjected to closer inspection. Despite the desired polycentric development at the macro-regional and national levels, where it is important to avoid polarization between capitals, metropolitan regions and other regional centres, the European perspective also put a strong emphasis on the regional dimension and the role of small and medium-sized towns in local development (EU Ministers responsible for Spatial Development, 8

9 2011). Local towns are considered to be crucial parts of less urbanized and rural areas because of their irreplaceable role as service and labour centres which ensure social and economic cohesion at the lowest geographical level. Moreover, local urban systems and networks of regional centres also have the potential to counterbalance the dominance of major cities and to contribute in consequence to a more balanced socio-economic development (RePUS, 2007). With regard to different geographical scales, the dynamics and form of interconnectedness of centres vary widely. In the context of polycentricity and disparity measures, the issue of scale must be taken into account first because it significantly affects the research design and interpretation of results (see e.g. Taylor et al., 2008; Vasanen, 2013). In the case of PUR, we can expect different forms of everyday interactions among centres than in the case of polycentric system at the national or international level. With increasing scale, the functional relations are transformed and based on different principles, for example, the daily movement of population in the case of a metropolitan area will not play such a significant role in transnational networking of cities, which is rather based on the exchange of information and institutional cooperation. The interactions between cities can take many different forms (e.g. commuting and inter-firm trades - to learn more about multiplexity of urban networks see Burger et al., 2014) whose intensity is strongly dependent on the geographical scale (van der Knaap, 2007). Apart from the European normative standpoint (see CEC, 1999; EU Ministers responsible for Spatial Development, 2011), the importance of studying polycentricity at a micro-regional scale is closely connected to processes that shape the form and structure of local urban systems. Daily-based human activities mostly take place at the micro-regional level, where spatial and functional patterns of society take the form of everyday routine (Limtanakool et al., 2009). Although even today the framework of everyday life may be confined to one city (especially for those who live and work in it), the city itself is increasingly integrated into the urban networks. Strong ties among municipalities across space are thus the characteristic feature of urban systems. Interactions can be, for example, in the form of work commuting, shopping trips or trade (e.g. retailing, intermediate goods and business services). At this scale of human activities, we can expect the emergence of PURs, which differ from other regions by their internal organization of socio-economic activities (Parr, 2004). 3 Methods and Data A methodical approach consists of four basic stages presented in a consequential order. The first step is the definition of territorial units used for an empirical analysis, the second is the evaluation of polycentricity of individual regions, the third involves assessing the level of intraregional socio-economic disparities and the final step is to measure the significance of the relationship between polycentricity and intra-regional disparities. 9

10 3.1 Delimitation of Territorial Units When assessing the level of polycentricity and intra-regional disparities, it is important to determine the geographical scale and form of regions first. The selection of the micro-regional scale is based on the three main reasons mentioned above: importance of small and mediumsized towns in local development, potential of micro-regions to generate PURs and the absence of a study (with respect to the present topic) aimed at this geographical scale (although a highly pragmatic reason, in this context it is the most relevant). Since the administrative boundaries do not often correspond to the actual spatial functioning of the territory (at least as regards the Czech space - see Halás et al., 2010; Klapka et al., 2014), the form of functional regions is used in this study. According to the method of Coombes (2002), defining Travel-to-work Areas and using a special algorithm (Mulíček and Kozel, 2012) allowing regionalization based on any input relational data (data quantifying the strength or intensity of the relationship between two spatial units), 134 Czech functional regions based on work commuting have been delimitated (Fig. 1). The inductive character of regionalization does not require a predefined fixed set of centres 4 that would determine the final form of regions to some extent. On the contrary, the principle is the functional self-containment of a region, whose internal structure allows the formation of a polycentric character. The interaction is represented by work commuting flows in 2011 as travelling to work represents most of all daily trips at the micro-regional scale (Clark and Kuijpers-Linde, 1994; van der Laan, 1998) and best available data on commuting. 3.2 Assessing Morphological and Functional Polycentricity With regard to the aim of the present study, it is essential to evaluate the structure of the urban system in particular regions. The basic territorial unit for assessing polycentricity is therefore the municipality, which may or may not play a role of a centre. Assessment of polycentricity is twofold, reflecting two basic approaches, morphological and functional. Morphological dimension of polycentricity refers to a more balanced size and territorial distribution of the urban centres across the territory (Kloosterman and Lambregts, 2001; Parr, 2004). The functional (relational) dimension of polycentricity concerns the functional connections between municipalities, whereas a balanced and multidirectional set of relations is considered to be more polycentric (Burger and Meijers, 2012). As demonstrated by Burger and Meijers (2012), seemingly different approaches may be linked together even if they show different results (a morphologically polycentric region is not necessarily functionally polycentric and vice versa). Nevertheless, the methods used in this paper do not allow simple explanation of differences between both approaches since they are not part of the same formula and rather point to a more precise identification of morphological/functional polycentricity. 10

11 Fig. 1. The Czech functional regions based on work commuting flows in Source: data collected from Czech Statistical Office (2014a), author s processing. Morphological polycentricity is evaluated using the absolute size of the centre expressed in the number of jobs, 5 specifically by the rank-size distribution of the largest centres and the deviation of the largest city from the regression line of rank-size distribution (primacy). Although morphological polycentricity is measured in terms of employment (as in the case of e.g. CRR MU, 2009; Burger and Meijers, 2012), it is not the only way to assess it. Alternative measures include, for example, rank-size distribution by GDP (for larger territorial units, see ESPON 2005) and population size (Meijers, 2008), or different methods reflecting other features than distribution of cities, for example, clustering of cities, characteristics of separation and spacing of cities, or connectivity (Parr, 2004; ESPON, 2005). However, since the functioning of defined regions is based on work commuting, evaluating the importance of centres according to their job function seems to be relevant. Furthermore, focus on the municipality level automatically excludes the use of generalized data (for example GDP) that are not available at this spatial scale. Rank-size distribution provides information on urban hierarchy which is an essential indicator of mono/polycentricity. 6 Rank-size order of the largest centres may indicate not only an urban system with one dominant centre, a situation rather monocentric, but also an urban system that lacks strong hierarchy and is characterized by similar-sized cities, a situation rather 11

12 polycentric. In fact, identification of strictly mono/polycentric systems is almost impossible, and more accurate results are provided by scores on a scale ranging from rather monocentric to polycentric systems. While a region can be considered as more polycentric when the slope of the regression line is flatter, with increasing steepness of the regression line, the urban system tends to be more monocentric. Considering the number of centres to be measured by rank-size distribution a fixed number of cities have been preferred for the reasons summarized by Meijers (2008). Finally, the sample size of 5 largest cities has been chosen (there has been a strong correlation between measures using 5 and 10 cities). Scores of the slope of the regression line and primacy measures have been standardized by the Z-score method. According to Meijers and Sandberg (2006), the resulting values have been transformed into the overall indicator of morphological polycentricity. The Z-score of 0 has been given the value of 100 and the standard deviation of 1 equals the value of 20 (e.g. 1 = 120, 0.5 = 90). The average of the values of ranksize distribution and primacy is the overall indicator of morphological polycentricity. Higher values of the indicator indicate more polycentric urban systems (on the contrary, lower values indicate more monocentric urban systems). Polycentric development should lead to a balanced spatial organization of human activities, which is within the polycentric urban systems expressed by intense mutual relations between centres (or between centres and their hinterlands). Whereas a functionally monocentric urban system is characterized by hierarchical interactions, a functionally polycentric urban system can be recognized by multidirectional flows and a higher level of reciprocal (mutual) interactions between municipalities. Functional polycentricity is thus measured by the level of reciprocal flows using data on work commuting and methods from the POLYREG project 7 (CRR MU, 2009). The reciprocal component of interaction between two municipalities is the sum of a number of mutual commuters and its share in the total number of commuters: for example, from municipality A 80 commuters heading to municipality B and from municipality B 120 commuters heading to municipality A. The total number of commuters in this case is 200, the reciprocal component includes 80 commuters from A to B and 80 commuters in the opposite direction and the hierarchical component comprises 40 commuters. The sum of reciprocal commuters is then 160 and the share in the total number of commuters is 80%. This approach is applied to all the interactions between municipalities within each functional microregion. The total number of internal commuters and the sum of reciprocal interactions are calculated for each micro-region. The share of total reciprocal component in the total number of commuters compared with the other regions indicates the level of functional polycentricity. As Burger and Meijers (2012) point out, a functional polycentric system represents centres that are relatively equal in terms of their connectivity to other centres and it is not a networked system characterized by functionally interdependent centres (higher level of network density). The level of spatial and functional interdependence is the extent to which the centres are functionally related. In this context, measuring the share of reciprocal flows illustrates the 12

13 balance of functional connections between centres and does not reflect the strength of functional interdependence (network density). As in the case of morphological polycentricity, the values are transformed into the overall indicator of functional polycentricity. 3.3 Measuring Intra-regional Disparities Intra-regional disparities indicate the difference between the levels of socio-economic development of municipalities. Selection of indicators has been limited due to the availability of data at the local (municipal) level. Despite this limitation, five indicators have been selected in order to characterize the economic, social and demographic levels of each municipality. The unemployment rate and the number of active firms per inhabitant indicate the economic level, the share of university-educated people in the total population over 15 years points to social aspects, the demographic situation of municipalities is represented by age index and the crude net migration rate denotes the attractiveness of places for living. Listed categorization of economic, social and demographic indicators does not have to be so strict and should serve rather as a formal framing of selected indicators (e.g. the unemployment rate certainly indicates not only the economic performance, but also the social aspects of development). Indicators are calculated for the year 2011; only the crude net migration rate represents the overall value of the time period from 2001 to A considerable number of methods for measuring regional disparities exist (see e.g. Shankar and Shah, 2003; Webber and White, 2003; Portnov and Felsenstein, 2005). In the present study, intra-regional disparities of each region have been measured by the most common and traditional statistical measures of variability, the standard deviation and the Gini coefficient. Standard deviation is a measure of dispersion around the average and is influenced by extreme and mean values. The higher the standard deviation, the greater the intra-regional disparities. Coefficients that are independent of a mean provide a more precise measure and easier interpretation of results. Thus, the Gini coefficient is used for measuring the relative extent of regional disparities. Probably the most widely used regional inequality measure in social sciences is calculated as the arithmetic average of the absolute value of differences between all municipalities, divided by the average value of an indicator 8 (Shankar and Shah, 2003). The Gini coefficient (G) is calculated as follows: G = ( 1 2y ) 1 n(n 1) y i y j The arithmetic mean of the specific indicator is y, yi and yj are the values of the specific indicator for municipalities i and j, respectively and n is the number of municipalities in a particular region. If the value of the indicator is the same in all municipalities, then G = 0. For perfect inequality G = 1. With increasing value of the Gini coefficient, the disparities are greater. 13 n i n j

14 3.4 Correlation of Variables Resulting values of variability measures of each region are finally compared to the level of mono/polycentricity of particular urban systems. The relationship between intra-regional disparities and the level of mono/polycentricity is evaluated using the Pearson correlation coefficient. 4 Results of the Empirical Study: Polycentricity and Disparity Levels in the Czech Regions 4.1 Levels of Polycentricity From 134 functional regions, three regions with less than five municipalities have been excluded (in order to measure the levels of polycentricity by rank-size distribution of five largest centres). Yet, the size of the regions is highly differentiated. While more than 2 million inhabitants live in the functional region of Prague (the largest Czech region), the smallest regions have only slightly more than 6000 inhabitants (see Fig. 1). Except for methodological parameters, the significant differences in the size of the regions are due to urban system hierarchy and the ability of municipalities to play the role of the centre. The results are therefore divided into categories based on the size of the regions. Tab. 1 shows the population distribution and number of municipalities in each size category. With increasing population size of the regions, the number of regions is smaller (with one exception in the smallest category). The uneven distribution of economic activities is documented by the three largest regions (Prague, Brno and Ostrava as the biggest centres), where almost a third of the total Czech population is concentrated. Tab. 1. Size of the Czech functional regions and Pearson correlation coefficient between population size of the regions and levels of morphological (MP) and functional (FP) polycentricity. Size category Population (2011) Number of regions Arithmetic mean Correlation of population size of the regions with levels of polycentricity 14 MP FP MP FP 20,000> 426, ,001-50,000 1,659, , ,000 1,931, ,001< 6,397, * All regions 10,415, ** * significant correlation at the 0.05 level, ** significant correlation at the 0.01 level Source: data collected from Czech Statistical Office (2014a), author s calculation When comparing the level of polycentricity and the size of the region, both approaches to polycentricity (morphological and functional) should be assessed separately. Although a certain level of interdependence between the two polycentricity approaches is evident (see Fig.

15 2), in fact, a morphologically polycentric region does not have to be a functionally polycentric region (and vice versa). A significant negative relationship is present between morphological polycentricity and size of the regions (see Tab. 1). Within the particular size categories only the morphological polycentricity of the largest regions correlates significantly and again negatively with the size of the regions. Thus, it can be suggested that more morphologically polycentric regions emerge predominantly in smaller regions in terms of population size. The level of functional polycentricity is not closely related to the population size of the regions. Fig. 2. The levels of morphological and functional polycentricity in the Czech functional regions. Source: data collected from Czech Statistical Office (2014a), author s processing. The levels of both morphological and functional polycentricity in the Czech functional regions are illustrated in Fig. 2. Regions with the highest scores of morphological and functional polycentricity are situated mostly close to the national borders with a rather random order. A noticeable concentration of these regions can be found in the north-east of the Czech Republic. One exception is the region with the largest centre, Tábor, which extends to the central part of the country and can be also distinguished from other polycentric regions by its population size and area. The most morphological and functional monocentric urban systems can be observed in regions of the largest cities (Prague, Brno, Plzeň, České Budějovice and Pardubice) and in 15

16 medium-sized regions (from 20,000 to 100,000 inhabitants) localized in the eastern part of the country. However, the structure of urban systems of the rest of the Czech regions cannot be clearly defined since the morphological and functional levels of polycentricity differ to a greater extent. For example, functionally polycentric regions are evidently concentrated in the northwestern part of the country, where also morphologically monocentric regions are situated. 4.2 Relationship between Polycentricity and Disparities The urban system structure of each region, in terms of polycentricity level, has been finally correlated with the level of intra-regional disparities (measured by standard deviation and the Gini coefficient). Positive correlation means that with increasing level of polycentricity the intra-regional disparities grow. Negative correlation means the opposite, with increasing level of polycentricity the intra-regional disparities are lower (see Fig. 3). Being aware of the differences between morphological and functional polycentricity, the correlation has been measured twice (for both approaches to polycentricity). In addition, the measurement has been also carried out for each size category of regions. Considering these aspects and five indicators of development levels, the resulting number of correlation values is 100. When interpreting the relationship between polycentricity levels and intra-regional disparities, it is necessary to take into account the type of polycentricity (morphological/functional), the size of the region, the chosen method for measuring disparities and the specific indicator of intra-regional disparities. The results show (see Tab. 2) that the connection of polycentricity to the level of intraregional disparities is not unambiguous. A significant relationship is mostly formed when evaluating all regions. This is partly a consequence of inner differentiation in the population size of regions. Firstly, a significant relationship for all regions is achieved despite the absence of a significant relationship within particular size categories (e.g. morphological polycentricity with age index and university-educated measured by the Gini coefficient). Secondly, a significant relationship for all regions exists because of the correlation value in specific size category, where the significant relationship substantially exceeds the values measured in other size categories (e.g. morphological polycentricity with university-educated and crude net migration rate measured by standard deviation, and functional polycentricity with universityeducated measured by the Gini coefficient). In this case, the significant relationship for all regions is the result of only one (or two) significant correlation(s) which belongs to one size category. Besides the impact of population size categories of regions on results, different levels of morphological and functional polycentricity are also reflected in various correlation values. One possible consequence is that in case of a relationship between morphological polycentricity and a selected indicator of intra-regional disparities, the correlation is significant, but in the case of functional polycentricity there is no significant relationship (and vice versa). However, 16

17 the situation of opposite significant relationships (positive and negative) related to different levels of morphological and functional polycentricity does not occur. Fig. 3. Examples of positive and negative correlations between levels of morphological polycentricity and the specific indicator of intra-regional disparities measured by the Gini coefficient (G) and standard deviation (SD). Source: data collected from Czech Statistical Office (2014a, 2014b), author s processing. Finally, correlation values depend on the chosen method for evaluating intra-regional disparities. The difference between standard deviation and the Gini coefficient is primarily visible when focusing on measures for all regions and the correlation of morphological polycentricity with the crude net migration rate. While a significant positive correlation results from the measure by the Gini coefficient, a significant negative correlation occurs when standard deviation is used. Differences of these two measures are caused not only by their methods of calculation but also by specific distribution of the crude net migration values. Crude net migration rate is characterized by greater disparities and extreme values than other indicators. Thus, standard deviation is more influenced by such distribution and differs significantly from Gini coefficient values. Nevertheless, the above-mentioned difference deserves a deeper explanation. Negative correlation (measured by standard deviation) reaches the highest value in the category with more than 100,000 inhabitants. Since the number of municipalities is mostly larger in more populated regions (at least in Czech regions), the probability of more extreme values is higher in these regions and the standard deviation values indicate greater intra-regional disparities. Reflecting the most populated Czech functional regions with the largest number of municipalities, the majority of them are rather monocentric with one dominant centre and are typical areas with the most intensive process of suburbanization in the observed period (e.g. 17

18 regions of Prague, Brno and Plzeň). Suburbanization significantly contributes to an increase in the crude net migration rate and may widen the distribution of values. As a result, the intraregional disparities in the crude net migration rate are greater in these monocentric regions. On the contrary, polycentric regions with more than 100,000 inhabitants are characterized by smaller number of municipalities and lower level of suburbanization (e.g. regions of Nový Jičín, Uherské Hradiště, Teplice and Frýdek-Místek). Thus, intra-regional disparities are lower in these regions. Since the crude net migration rate gives us a picture about the attractiveness of a particular place it can be suggested that the most populated and monocentric regions tend to be fragmented in local attractiveness for living. The significant positive correlation of morphological polycentricity and intra-regional disparities in the crude net migration rate measured by the Gini coefficient for all regions originates in the smallest population size category (less than 20,000 inhabitants). The intra-regional disparities in the crude net migration rate increase with higher levels of morphological polycentricity. Since the Gini coefficient measures inner structure of distribution of values (differences between all pairs of values) it provides a more precise view of the relative extent of intra-regional disparities. Concerning the correlation of polycentricity with socio-demographic indicators, age index and university-educated people in the total population over 15 years, both of them show similar results. With the increasing level of polycentricity, the intra-regional disparities in age index and university-educated people in the total population over 15 years decrease. The concentration of university-educated population in the largest centres with sufficient number of jobs is a common trend that holds true also in the Czech Republic (based on own calculation of data from Czech Statistical Office, 2014a). Accordingly, polycentric regions provide more places that are preferred by people with higher education. A more even distribution of university-educated population can be observed predominantly in polycentric regions with more than 100,000 inhabitants (e.g. regions of Teplice and Uherské Hradiště). On the other hand, excessive concentration of people with university education can be found in strongly monocentric regions (e.g. region of Prague). The largest centres of each region attract university-educated people as well as other young people who are looking for employment opportunities. Young immigrants very often start families in these centres and the share of the youngest (or pre-productive) population is rising. However, the processes of suburbanization and urban shrinkage (connected to post-industrial transformation of former industrial centres) could modify this trend. In the case of Czech regions, age index is more evenly spread in polycentric urban systems and more monocentric regions are characterized by a rather unequal demographic structure in terms of distribution of population by age. Intra-regional disparities in economic performance are represented by unemployment rate and active firms per inhabitant. The relationship between economic disparities and polycentricity has not been proved. However, two exceptions exist. A significant negative 18