Changes in the Characteristics of Suburbanization in the Warsaw Metropolitan Area in the First Decades of the 21st Century

Abstract

This paper presents the results of a study on the nature, pace, and directions of socio-economic and spatial changes in the Warsaw Metropolitan Area (WMA) related to the suburbanization processes taking place there. This paper’s contribution to research on suburbanization processes and the uniqueness of the research approach used are related to the dimensions of suburbanization analyzed and their integration while distinguishing types of municipalities, as well as an attempt to present the course and dynamics of changes in suburbanization in municipalities depending on their location, their development potential, and the time of appearance of suburbanization in their areas. In the course of our research, data on municipalities from the Local Data Bank were used, and that made it possible to trace the changes occurring in 2002–2021. The selection of analytical characteristics took into account a wide range of socio-economic diversities of the area related to changes in population, construction activity and housing stock, land use and spatial policy, and economic factors and those associated with investment attractiveness. The components of diversification were used to develop the typological classes of municipalities. The final result of this study is the identification of the specificity of suburbanization in the WMA which is reflected in the social, economic, and spatial aspects of the functioning of municipalities.

1. Introduction

Suburbanization is the process of urbanization of the areas outside a city, usually in municipalities that border the central city. Unlike urban sprawl, suburbanization as a phenomenon concentrates on areas that are functionally linked to the central city. While urbanization concentrates people and activities within cities, suburbanization disperses them outward. This process can have significant social, economic, and environmental implications. On one hand, it can relieve pressure on crowded urban areas, provide housing options for a growing population, and spur economic development. On the other hand, it can contribute to traffic congestion, social segregation, and environmental degradation.

Thus, suburbanization is closely related to urbanization, which is the overall growth and expansion of cities. As cities grow, they often become more densely populated, leading to higher land costs and increased congestion. Suburbanization occurs as a response to these pressures, with people and businesses seeking cheaper land, larger living spaces, and a quieter environment outside of the city center.

The forms and nature of suburbanization are determined by the course of urbanization processes. These, in turn, vary from country to country and are subject to change over time. What the suburbanization of different latitudes has in common is the complexity of its causes and the multidimensionality of its effects. Suburbanization is particularly evident in the functional areas of cities, including metropolitan areas as their particularly complex form. Suburbanization causes many problems associated with its negative impact on the natural environment, on the functioning of urbanized areas, and on the formation of socio-spatial structures [1,2].

The process of suburbanization is observed all around the globe and is being researched from various perspectives. In the US, the topic of suburbanization is often examined through the lens of racial and economic segregation [3,4,5].

Comparative research between US and other metropolitan areas can also be found, as the process has different shades in each part of the world. German city regions for in-stance are considered to be more centralized, with more stable employment suburbanization than those in the US [6]. The European context can also vary from country to country. Main differences are seen between western, democratic metropolitan areas and those post-Soviet from the east where the process is delayed [7]. The deconcentration of the settlement emerged together with the stagnation of the overall population and the suburbanization of the non-residential functions as the employment expanded in the suburban areas [8].The research focuses also on assessing negative outcomes of the urban sprawl [9].

The topic has been moreover widely discussed in the polish literature, as the process slightly differs from one metropolitan area to other. The railway development has been an important reason for the suburbanization expansion in the Gdańsk-Gdynia-Sopot Metropolitan Area [10,11] while in Kraków Metropolitan Area there is the seek for peaceful and quiet, more attractive place of living that encourages the urban sprawl besides the development of the transportation net [12]. In Poland researchers assume that spatial planning policy does provide sufficient tools to control the process and avoid it’s negative outcomes; however, they are not commonly used, which leads the polish metropolitan areas to a not-sustainable path of development [13,14].

In the countries of Central and Eastern Europe, the process of intensive suburbanization began in the 1990s. This was mainly related to systemic changes in political regimes and ways of functioning of the economy [15]. Decentralization and the emergence of local governments were associated with new mechanisms for programming, planning, and managing territorial development [16,17]. The transition from a centrally planned economy in the communist system to a market economy changed the perspective of perceiving and using space [11,18,19]. This was directly related to the ways of land-use planning in cities and the areas surrounding them.

Economic development in post-communist countries brought an increase in the wealth of society in general and, in particular, in the wealth of city residents. The opening of post-communist countries to the world brought about an increase in the aspirations of their inhabitants and growing needs regarding a place of residence. During the communist period, cities were dominated by large housing estates. After the political transformation, the demand for single-family houses, either terraced houses or semi-detached houses, increased [20,21]. This led to the creation of new estates of these houses on the outskirts of cities. The civilizational development of post-communist countries brought the development of transport infrastructure, which was also a factor stimulating the process of suburbanization. It should also be mentioned that, in many post-communist countries, the process of privatization and liberalization of the economy favored the development of the private sector in housing construction. Developers often invested in the construction of new housing estates on the cheaper and easily available land on the outskirts of cities, responding to the growing demand for housing.

One of the factors determining the entry of cities into the suburbanization phase is mobility. In the case of the population, mobility is motivated in different ways and requires the appropriate conditions. The affluence of the population, the emergence of the possibility to choose where to live, and the population’s needs and preferences in terms of living conditions and lifestyle lead to residential suburbanization. Deterioration of the conditions under which businesses operate in cities or new requirements regarding the location of commercial activities are the reasons for their relocation to the urban edges. Spatial forms of the city and urban life are emerging in the areas surrounding the urban core. The number, variety, scale, and strength of the links between the suburban zone and the urban core are increasing. A process of their functional integration is taking place [22].

The choice of the Warsaw Metropolitan Area as our research area was caused by its unique functions, the nature of its internal and external links, and the complex dynamics of urbanization processes. As the largest agglomeration in the settlement system and an area with a degree of urbanization ranked among the highest in Poland, the Warsaw Metropolitan Area is characterized by intensive links between Warsaw—its core—and the smaller towns surrounding it, especially those in its immediate vicinity. Compared to other Polish metropolises, it stands out in terms of the level of economic development, the presence of leading enterprises, the availability of advanced services, and the quality of life.

While Polish municipalities gained considerable autonomy and authority over their spatial planning, the resultant suburbanization and urban sprawl, and the formation of functional areas around cities, have not, however, gained traction in debates over the spatial planning on a supra-local or metropolitan scale. The issue of the management of metropolitan areas’ development was resolved by a special act by the central government. On 9 March 2017, the Act on the Metropolitan Union in the Silesian Voivodeship entered into force, limiting the application of metropolitan structures to the region of cities of the Upper Silesian conurbation [23]. This metropolitan union, under the name “Upper Silesian-Zagłębiowska Metropolia”, is the first and the only one legally recognized in the Poland Multi-Purpose Metropolitan Union. The union began its operations in 2018. The Warsaw Metropolitan Area is not formally recognized and there is no administrative, institutional, or organizational structure responsible for managing the area’s planning and development. However, municipalities from the WMA cooperate with each other. This cooperation is largely bound to public transport, municipal services (waste management and education), environmental protection, and Integrated Territorial Investments (ITI)—a mechanism enabling municipalities to prepare development strategies at a supra-local level [24].

The course of suburbanization processes in the WMA is important from a cognitive and application point of view. This is because the metropolitan area is differentiated in terms of settlement and investment attractiveness i.e., costs, access to services, the quality of the natural environment, and transport accessibility. From the point of view of the sustainable development of the area, the absence of a mechanism designed for programming and managing its development is crucial. The effects of suburbanization are dealt with individually by the municipalities located in the area. Although they are affected by urbanization pressure to varying degrees, many are in danger of losing their values in economic (decline in investment attractiveness, lack of investment areas, outflow of workers), social (breakdown of well-established, traditional social structures, emergence of social conflicts), and spatial (loss of naturally attractive areas, deterioration of the quality of the natural environment) contexts.

Suburbanization in the Warsaw Metropolitan Area did not appear in all municipalities at the same time. The effects of suburbanization, as well as the prospects for its development, are diverse. The main goal of this study, the results of which are included in this article, was to identify the dimensions of socio-economic and spatial differentiation of municipalities in the WMA in the context of suburbanization. To achieve this goal, we needed to find answers to the following questions: what spheres of the functioning of municipalities were influenced by suburbanization over the last 20 years, what changes occurred in the suburbanization process during that time, and what are the potential directions of its expansion?

2. Materials and Methods

2.1. Study Area

The Warsaw Metropolitan Area (WMA), which is also known as the Capital Region of Warsaw (CRW), is located in the central part of the Mazowieckie Voivodeship. It consists of 70 municipalities and nine counties. The capital city of Warsaw has the status of a city with county rights (Figure 1 and Figure 2).

The Warsaw Metropolitan Area (WMA) is a special territorial case in Poland. This area has formed around Warsaw, a city with exceptionally strong development potential. As the country’s capital, Warsaw is the central political and administrative hub. Its modern economic development base, which is dominated by specialized services and its cultural and scientific potential, gives the city a special role and importance in the context of the development of the metropolitan area.

Warsaw attracts foreign investment, which fosters further development of the service, financial, and high-tech sectors. As a business center with headquarters of corporations, banks, and financial institutions as well as representative offices of international companies, Warsaw draws professionals and employees from various industries, strengthening its position as well as that of the entire metropolitan area. Warsaw is also a magnet as a hub of university education and concentrates institutions from the research sector all in one place. This makes Warsaw and its metropolitan area a destination for domestic and foreign migration. A so-called “metropolitan class” already emerged in the city several years ago, and the expectations, aspirations, opportunities, and preferences of those working in Poland’s capital city are reflected in their search for suitable places to live, including in the suburban zone.

The Warsaw Metropolitan Area covers an area of 6100 km², which accounts for 17.2% of the area of the Mazowieckie Voivodeship and is treated as a coherent system in which dynamic social and economic changes are taking place, resulting from functional links between its component units. In 2022, the area covered by the present study was inhabited by more than 3.3 million people, which is a significant increase from 2.9 million over the past ten years. During the same period, Poland’s population decreased from 38.5 million to 37.8 million, making the metropolitan area’s share of the country’s total population increase from nearly 7.6% to nearly 8.6%. Currently, the inhabitants of the WMA comprise 59.1% of the population of the Mazowieckie Voivodeship. Despite a nationwide decrease in population between 2003 and 2022, the Warsaw Metropolitan Area has recorded the largest population growth [25].

Between 2003 and 2020, the Warsaw Metropolitan Area experienced significant demographic changes, characterized by varying patterns of population growth and decline across its municipalities (Figure 3). In accordance with the typology by Webb (1963), the most common municipality types between 2003 and 2020 were demographically active and represented type C, in which positive natural increase is lower than positive net migration, and type D, in which positive net migration with surplus compensates negative natural increase [26]. Types C and D included almost 70% of all municipalities in the analyzed period (

Table 1. Changes in the number of municipalities representing Webb’s demographic types between 2003 and 2020.

Webb Type	Population Change	Webb Type-Specific Demographic Profile	Number of Municipalities [%]
			2003	2005	2010	2016	2020
A	Demographically active units (actual population growth)	positive natural increase surpasses negative net migration	1 (1.41)	2 (2.82)	7 (9.86)	4 (5.63)	2 (2.82)
B		positive natural increase surpasses positive net migration	3 (4.23)	3 (4.23)	3 (4.23)	5 (7.04)	0 (0.00)
C		positive natural increase is lower than positive net migration	13 (18.31)	23 (32.39)	47 (66.20)	36 (50.70)	20 (28.17)
D		positive net migration with surplus compensates negative natural increase	37 (52.11)	28 (39.44)	4 (5.63)	14 (19.72)	27 (38.03)
E	Demographically non-active units (actual population loss)	negative natural increase is not compensated by positive net migration	5 (7.04)	5 (7.04)	2 (2.82)	0 (0.00)	10 (14.08)
F		negative natural increase with negative, but not lesser (in absolute value) net migration	4 (5.63)	4 (5.63)	0 (0.00)	5 (7.04)	9 (12.68)
G		negative natural increase with negative, but not bigger (in absolute value) net migration	4 (5.63)	5 (7.04)	4 (5.63)	5 (7.04)	2 (2.82)
H		negative net migration is not compensated by positive natural increase	2 (2.82)	1 (1.41)	3 (4.23)	2 (2.82)	1 (1.41)

).

In 2003, within the Warsaw Metropolitan Area, 54 municipalities (76%) experienced population growth, while 15 municipalities (21%) faced depopulation. In 16 municipalities, both natural increase and positive migration balance were observed (types B and C). These were primarily municipalities neighboring Warsaw or well-connected to the capital (e.g., Jabłonna, Lesznowola, Piaseczno, Marki). There were as many as 8 municipalities where depopulation occurred due to both natural decrease and negative migration balance, mainly rural municipalities located in the peripheral parts of the area, mostly in the eastern part.

In 2005, there were 56 populated municipalities (79%), with a clear increase in the share of municipalities in the most favorable demographic situation, classified as type C, and located along the communication routes leading from Warsaw. Notable were the southern strip (Raszyn, Nadarzyn, Piaseczno, Tarczyn, Prażmów), the northern strip (Łomianki, Jabłonna, Legionowo, Nowy Dwór Mazowiecki, Wieliszew, Serock), and several municipalities to the east of the capital belonging to the Wołomin county (Ząbki, Marki, Wołomin, Kobyłka, Zielonka) and Otwock county (Karczew, Józefów, Wiązowna, Celestynów). That year, 15 municipalities experienced depopulation, mainly rural municipalities on the outskirts of the metropolitan area (e.g., Jadów, Stachówka, Kołbiel).

The most favourable demographic situation occurred in the WMA in 2010—as many as 61 municipalities saw population growth, of which 50 could be classified as types B and C. The most favorable demographic situation was characterized mainly by rural municipalities well-connected to Warsaw such as Lesznowola, Jabłonna, Żabia Wola, Nieporęt, Wieliszew, and Wiązowna. Depopulation occurred in 9 municipalities, with the worst demographic situation during this period occurring in 4 municipalities located in the eastern part of the region (Dobre, Kałuszyn, Latowicz, and Strachówka).

In 2016, the demographic situation in the WMA slightly deteriorated—59 municipalities experienced population growth, and 12 municipalities faced depopulation. Municipalities to the west of Warsaw began ageing, with an increasing share of units belonging to type D. The highest migration balance continued to be recorded in municipalities located near Warsaw (Wieliszew, Lesznowola, Ząbki, Kobyłka, and Marki). Among the depopulating municipalities, apart from the peripheral municipalities (Jadów, Stachówka), there were also cities located closer to the capital (Otwock, Piastów).

In 2020, within the Warsaw Metropolitan Area, 49 municipalities (69% of the municipalities studied) experienced population growth and 22 municipalities were depopulating (31%), indicating that the situation was worse than in 2003. Twenty municipalities represented type C. It can be observed that, this time, the municipalities are also located in the southern strip (Piaseczno, Lesznowola), northern strip (Jabłonna, Wieliszew), northeastern strip (Ząbki, Marki, Kobyłka), and eastern strip (Dębe Wielkie, Mińsk Mazowiecki); however, the share of rural municipalities and those located further from Warsaw than in 2003 has increased. In 2020, there was also a marked increase in the number of municipalities classified as types F and G in close proximity to the capital (Otwock, Legionowo, Piastów).

Demographic projections for the Mazovian Voivodeship for 2022–2040 indicate a further increase in population in the municipalities of the Warsaw Metropolitan Area, which is in contrast to the expected population decline in most municipalities of the rest of the voivodeship [24].

2.2. Research Design

This study used data and information from the Local Data Bank (LDB), the RWDZ database—Register of Applications, Decisions and Notifications in Construction Matters, and the Demography database. The data covered the years 2002–2021. Urbanization and suburbanization are ongoing processes shaping the modern world, diverse in nature and varying across time and space. Researching these phenomena presents challenges in selecting appropriate measures [27,28,29]. Podawca et al. emphasize that the relevant literature extensively describes the diversity of urbanization in suburban areas, considering demographic, economic, socio-cultural, natural landscape, and spatial factors [30]. Studies employ a comprehensive set of variables to assess urbanization levels and dynamics, including demographic indicators such as natural increase and migration attractiveness, economic parameters like GDP per capita and non-agricultural employment, social metrics involving cultural and leisure facility use, infrastructural factors like land use changes, and sociological aspects reflecting lifestyle shifts [31,32,33,34]. The selection of characteristics for this analysis was based on a literature review and took into account a wide range of the area’s socio-economic variations associated with changes in population, construction activity and housing stock, land use and spatial policy, and economic and investment attractiveness (

Table 2. Variables describing the urbanization process selected for principal component analysis with data sources.

Aspects	Name of Variables	Data Sources
The demographic and social aspect	Change in the birth rate	Local Data Bank, Statistics Poland
	Change in old-age dependency ratio	Local Data Bank, Statistics Poland
	Change in the post-working age population per 100 persons of pre-working age	Local Data Bank, Statistics Poland
	Change in migration balance	Local Data Bank, Statistics Poland
	Change in population density	Local Data Bank, Statistics Poland
	Change in the number of marriages per 1000 population	Local Data Bank, Statistics Poland
	Change in the percentage of residents moving out from one municipality to another within the Warsaw Metropolitan Area	Demography Database, Statistics Poland
	Change in the percentage of residents moving into a municipality from other municipalities within the Warsaw Metropolitan Area	Demography Database, Statistics Poland
	Change in the number of primary school students per 1000 population	Local Data Bank, Statistics Poland
	Change in the number of middle school students per 1000 population	Local Data Bank, Statistics Poland
	Change in the number of preschool children per 1000 population	Local Data Bank, Statistics Poland
The construction activity and housing stock	Change in the average number of persons per dwelling	Local Data Bank, Statistics Poland
	Change in share of new dwellings in total number of dwellings	Local Data Bank, Statistics Poland
	Change in the number of building permits per 1000 population	Local Data Bank, Statistics Poland
	Change in average floor area of commissioned dwellings per 1000 population	Local Data Bank, Statistics Poland
	Change in share of new dwellings in total number of dwellings	Local Data Bank, Statistics Poland
	Change in the average number of dwellings per residential building	Local Data Bank, Statistics Poland
	Change in the proportion using the water supply system	Local Data Bank, Statistics Poland
	Change in share of sewerage users	Local Data Bank, Statistics Poland
	Change in the proportion using gas infrastructure	Local Data Bank, Statistics Poland
The land use and spatial policy aspect	Change in the number of building permits issued for residential construction	General Office of Construction Supervision
	Change in the proportion of agricultural land for which the plans foresee change to non-agricultural use	Local Data Bank, Statistics Poland
	Change in the proportion of forest land for which the plans foresee change to non-forest use	Local Data Bank, Statistics Poland
	Change in the proportion of total area covered by local spatial management plans in preparation	Local Data Bank, Statistics Poland
	Change in the total area of agricultural land designated for non-agricultural purposes in the plans	Local Data Bank, Statistics Poland
	“Change in the total area of forest land designated for non-forest purposes in the plans	Local Data Bank, Statistics Poland
	Change in the area required for re-designation of agricultural land to non-agricultural purposes	Local Data Bank, Statistics Poland
	Change in the area required for re-designation of forest land to non-forest purposes	Local Data Bank, Statistics Poland
	Change in the proportion of parks, green spaces and residential green spaces in the area of the municipality	Local Data Bank, Statistics Poland
The economic aspect	Change in the number of businesses per 1000 working-age population	Local Data Bank, Statistics Poland
	Change in the number of entities employing up to 9 persons per 10,000 inhabitants of working age	Local Data Bank, Statistics Poland
	Change in the number of sole traders per 100,000 people of working age	Local Data Bank, Statistics Poland
	Change in the share of PIT in the municipality’s own income	Local Data Bank, Statistics Poland
	Change in the share of CIT in the municipality’s own income	Local Data Bank, Statistics Poland
	Change in the number of businesses in sections J–N per 1000 population	Local Data Bank, Statistics Poland
	Change in the share of newly registered creative sector entities in the total number of newly registered entities	Local Data Bank, Statistics Poland
	Change in the share of newly registered entities of the agri-food processing sector in the total number of newly registered entities	Local Data Bank, Statistics Poland
	Change in the number of tourism entities per 1000 population	Local Data Bank, Statistics Poland
	Change in the number of tourist accommodation establishments per 1000 population	Local Data Bank, Statistics Poland

).

A principal component analysis was carried out to determine the nature, pace, and direction of socio-economic change in the WMA. Factor analysis, a multivariate technique, uncovers clusters of variables that exhibit comparable patterns of fluctuation. This approach is designed to simplify the intricacy inherent in any given data set. It operates under the premise that a limited number of core dimensions are responsible for the variability observed in individual variables [35]. These underlying dimensions are represented through the creation of new, composite variables known as factors [36]. The main goal of factor analysis is to simplify and interpret data by reducing a large number of variables into a smaller set of factors.

The process begins with the examination of correlations among the observed variables. High correlations suggest that variables share a common underlying factor. Factor analysis then quantifies how much of the variance in the observed variables can be attributed to these underlying factors, which are not directly observable but are inferred from the data.

The factors are constructed as linear combinations of the original variables, and each factor represents a specific dimension of the data. These factors are designed to be independent of one another, capturing different aspects of the data. The analysis provides factor loadings, which indicate the strength and direction of the relationship between the variables and the factors, helping to interpret the factors. Factor scores represent the extracted dimension in the spatial units that the analysis is based on. The higher (positive) or lower (negative) the factor scores calculated for a single unit, the more the unit is characterized by the respective dimension [36,37].

Factor analysis is frequently employed by researchers from various scientific disciplines to analyze diversifications occurring in metropolitan areas [38,39,40,41,42,43,44].

It has been used to define suburbanization trends in certain metropolitan areas, considering various dimensions [42], and to relate the suburbanization process with the economic situation of a region [41]. It was also implemented to quantify urban–rural gradients [45] as well as predict the spread of the urbanization process [46]. In the context of social changes, factor analysis is used in terms of the quality of living [47]. When it comes to economic aspects, this technique may be useful to identify the spatial configuration of a region’s employment [38], to esteem motivations, success factors, and problems of developing entrepreneurship [48], and to define the capabilities of the government to boost innovation [49]; as well, it can be used for creating rankings [50]. Factor analysis is also helpful in estimating demographic trends [43].

This technique has also been used to identify transformations in the socio-spatial structure of Warsaw [29]. However, there is a lack of studies demonstrating the direction of socio-economic and spatial changes occurring in the Warsaw Metropolitan Area that are related to the suburbanization processes taking place within its region.

This analysis reduced the number of variables studied and enabled the identification of the most important areas of the WMA’s diversification. The reduction of variables was carried out using the correlation-factor method—unimportant variables with a low coefficient of variation and high correlation were eliminated. As a result, out of a set of 39 preselected variables, 27 were ultimately used for analysis.

The results of the principal component analysis were used to classify the municipalities, which was carried out using hierarchical cluster analysis. A classification tree was obtained, which presents several clusters of elements with a similar structure to the examined components. On this basis, four basic typological classes of municipalities were distinguished, and within them several subclasses.

3. Results

3.1. Dimensions of Socio-Spatial Diversity of the WMA and Spheres Affected by Suburbanization

Principal component analysis made it possible to distinguish five latent dimensions in which the analyzed variables are grouped (

Table 3. Main components of the transformation of socio-spatial diversity in the Warsaw Metropolitan Area *.

	Components
	1	2	3	4	5
Change in the number of businesses in sections J–N per 1000 inhabitants	0.905	0.156	−0.075	0.055	−0.061
Change in the number of entities employing up to 9 persons per 10,000 inhabitants of working age	0.866	0.121	0.185	0.08	0.042
Change in the number of businesses per 1000 working-age population	0.857	0.109	0.192	0.068	0.036
Change in the share of PIT in the municipality’s own income	0.796	−0.204	0.102	−0.189	−0.047
Change in the number of sole traders per 100,000 people of working age	0.781	0.064	0.416	0.027	0.070
Change in the proportion of parks, green spaces and residential green spaces in the area of the municipality	0.465	0.179	−0.246	−0.344	−0.290
Change in the share of CIT in the municipality’s own income	0.416	−0.129	−0.19	0.131	−0.089
Change in old-age dependency ratio	0.094	−0.759	0.044	0.088	0.030
Change in the birth rate	−0.058	0.721	−0.104	0.087	0.051
Change in population density	0.095	0.635	−0.546	−0.081	0.126
Change in the proportion using gas infrastructure	0.013	0.389	0.192	0.175	0.334
Change in average floor area of commissioned dwellings per 1000 population	0.177	0.374	0.34	0.304	0.257
Change in average floor area of 1 dwelling	0.456	0.063	0.663	−0.118	0.135
Change in the number of building permits per 1000 population	0.131	0.391	0.524	0.08	−0.232
Change in the average number of persons per dwelling	0.072	0.151	0.383	−0.682	−0.118
Change in the number of tourism entities per 1000 population	0.15	0.024	0.295	0.669	−0.104
Change in the number of tourist accommodation establishments per 1000 population	0.202	0.043	0.341	0.663	−0.097
Change in the number of dwellings per 1000 population	0.269	0.058	−0.53	0.572	−0.085
Change in the share of newly registered creative sector entities in the total number of newly registered entities	0.203	−0.050	0.060	−0.427	0.070
Change in migration balance	−0.336	−0.097	−0.126	0.413	0.258
Change in the share of newly registered entities of the agri-food processing sector in the total number of newly registered entities	0.057	0.107	−0.095	0.281	−0.082
Change in the number of marriages per 1000 population	0.177	0.109	−0.071	−0.251	0.007
Change in share of sewerage users	0.155	−0.063	−0.108	−0.180	0.723
Change in the proportion using the water supply system	−0.095	0.090	−0.046	−0.160	0.673
Change in share of new dwellings in total number of dwellings	−0.310	0.314	0.242	0.104	0.599
Change in the proportion of agricultural land for which the plans foresee change to non-agricultural use	0.000	0.027	0.229	−0.07	−0.404
Change in the proportion of forest land for which the plans foresee change to non-forest use	0.114	0.102	0.156	−0.051	0.397

* The principal component method with varimax rotation was used.

and Figure 4). The resulting components, together, explained 50.8% of the variation in the baseline variables, and their significance varied quite considerably.

The first component was defined as economic urbanization. The following components correlated most strongly with it: the change in the number of business entities in sections J–N per 1000 inhabitants, the change in the number of entities employing up to nine persons per 10,000 inhabitants of working age, the change in the number of business entities per 1000 inhabitants of working age, the change in the share of PIT in the municipality’s own income, and the change in the number of individuals running a business per 100,000 inhabitants of working age.

This component differentiated the municipalities most strongly in terms of changes in the number of businesses operating there, especially micro-enterprises engaged in professional activities. This allowed it to identify the municipalities that have become attractive places for business in the last two decades—this group included Warsaw, Izabelin, Lesznowola, Konstancin-Jeziorna, Stare Babice, Michałowice, and Nadarzyn, as well as municipalities where the dynamics of economic development were the lowest—rural municipalities prevailed among them, i.e., Mrozy, Latowicz, Jakubów, Strachówka, Siennica, Stanisławów, and Poświętne. The spatial distribution of the value of the first principal component clearly divided the Warsaw Metropolitan Area into east and west. Municipalities located to the west of Warsaw, especially those in close proximity, stood out in terms of high values of this component, while most of the municipalities with the lowest values were located in the eastern, peripheral part of the WMA.

The second component was referred to as demographic urbanization because three components correlated most strongly with it, i.e., the change in the old-age dependency ratio (negative correlation), the change in the birth rate, and the change in population density.

This variable clearly differentiated between municipalities located at two poles: demographically young and attractive from the point of view of settlement, characterized by a high natural increase exceeding the negative migration balance, e.g., Ząbki, Marki, Jabłonna, and Lesznowola, and clearly ageing municipalities with a high old-age dependency ratio, e.g., Podkowa Leśna, Izabelin, Pomiechówek, Karczew, and Poświętne. The spatial distribution of the values of the second principal component was rather mosaic in character—in the vicinity of Warsaw you can find both demographically young and clearly ageing municipalities, similarly in the outlying areas of the WMA, although here municipalities with low values of this component prevail.

Another component was defined as functional–spatial urbanization and correlated most strongly with two variables, i.e., the change in the average floor area per dwelling and the change in the number of building permits per 1000 inhabitants.

This component attained high values in municipalities characterized by a large and increasing number of building permits issued in recent years and a fairly substantial usable area of dwellings per capita, which indicates a growing share of single-family houses in the development structure. This component strongly polarizes the area of Warsaw’s suburban municipalities, as it is considerably high in rural or urban-rural municipalities, i.e., Serock, Wiązowna, Nieporęt, Michałowice, Raszyn, and Stare Babice, and low in towns, i.e., Ząbki, Pruszków, Marki, Błonie, Nowy Dwór Mazowiecki, Legionowo, Piaseczno, and Sulejówek, where the number of newly constructed multi-family buildings is increasing.

The fourth component, identified as tourist urbanization, correlated most strongly with four variables, i.e., the change in the average number of inhabitants per dwelling (negatively), change in the number of tourist entities per 1000 inhabitants, change in the number of beds in tourist facilities per 1000 inhabitants, and change in the number of dwellings per 1000 inhabitants.

This component makes it possible to identify municipalities where the tourist function is developing and where second homes are being built. It reaches high values in municipalities such as Serock, Wieliszew, Nowy Dwór Mazowiecki, Nieporęt, and Pomiechówek, i.e., in areas of high natural value which also offer tourist or historical attractions.

At the opposite end of the spectrum are municipalities whose tourism potential has been clearly declining in recent years—Podkowa Leśna, Celestynów, Kampinos, Jabłonna, Brwinów, Stanisławów, Halinów, and Cegłów, which may be linked to a reduction in the tourism function of these municipalities or to the state of sufficient saturation in tourist facilities.

The last component was defined as infrastructural urbanization. Three variables demonstrated the strongest correlation with it, i.e., the change in the share of sewerage users, change in the share of water supply users, and change in the share of new dwellings in the total number of dwellings.

This component differentiates municipalities in terms of the provision of mains installations and the increase in new dwellings. It identified municipalities where there has been a dynamic increase in the number of new dwellings and dwellings connected to the water supply and sewerage network, e.g., Pruszków, Izabelin, Ożarów Mazowiecki, Nowy Dwór Mazowiecki, and Kampinos, and municipalities where these changes are slow or do not keep up with the development of residential development, e.g., Żyrardów, Warsaw, Jabłonna, and Klembów.

3.2. Typology of WMA Municipalities According to the Course of Suburbanisation

A classification of WMA municipalities was developed using the hierarchical cluster analysis, drawing on Ward’s method to optimize the results [51]. This approach produced a classification diagram that identified multiple clusters of elements with a similar structure in terms of the measures (components) analyzed. In this instance, the identified classes were not formalized, and examining the dendrite structure in relation to the spatial distribution of the recognized types was pivotal in their identification and naming. Ultimately, four basic typological classes of municipalities were distinguished, and within them several subclasses (

Table 4. Mean values for components’ scores in the identified types/sub-types of WMA municipalities.

Typological Class	Components
	1—Economic Urbanization	2—Demographic Urbanization	3—Functional-Spatial Urbanization	4—Tourist Urbanization	5—Infrastructural Urbanization
1a	−0.723	−0.418	−0.236	0.187	−0.031
1b	0.447	−0.769	−0.423	−0.243	1.274
2a	0.709	−0.203	0.615	−0.393	−0.310
2b	−1.695	0.816	2.271	−0.648	0.745
3a	1.110	1.905	0.532	1.034	1.144
3b	−0.096	0.897	0.626	−0.204	−0.130
4a	1.593	−1.115	−2.090	0.560	−3.176
4b	0.137	2.501	−1.399	−0.666	−0.576
4c	0.284	0.496	2.312	5.206	−0.836

and Figure 5).

The first type distinguished includes municipalities in the slowdown phase of the suburbanization process (type 1). Within them, two sub-types can be distinguished: the first is dominated by municipalities where there has been a marked economic slowdown, while the second is dominated by a slowdown in demographic processes.

The first sub-type (type 1a) is represented by municipalities such as Cegłów, Góra Kalwarii, Legionowo, Otwock, and Sulejówek. The economic attractiveness of these municipalities declined in the period under study, as demonstrated by economic indicators which were significantly lower than the average for the WMA as a whole. They are also municipalities with low settlement attractiveness, as evidenced by very meagre real growth and a small increase in new housing.

Subtype two (type 1b) is distinguished by a slightly higher increase in population as a result of a positive migration balance. However, natural attrition in these is higher than in the first sub-type. Municipalities in this group are mainly distinguished by a progressive ageing of the population with some migration stagnation. Examples of this are Błonie, Kampinos, Podkowa Leśna, Pruszków, and Tarczyn.

The second type of municipalities was characterized by an improvement in economic indicators and an increase in construction activity in their area (type 2). Municipalities belonging to this category are in the continuation phase of moderate suburbanization processes.

Within it, two sub-types can be identified. The first sub-type (type 2a) is characterized by strong economic development: these are municipalities in which the largest number of businesses have been established, including micro-enterprises and professional businesses, and in which revenues to local budgets from PIT and CIT taxes have increased the most. It is noteworthy that an outflow of residents can also be seen in these municipalities. This group includes Celestynów, Józefów, Konstancin—Jeziorna, Raszyn, Wiązowna, Zielonka, Stare Babice, and Michałowice.

The second sub-type (type 2b) is mainly distinguished by the dynamic development of housing construction. In municipalities belonging to this category, the number of new dwellings is increasing, the average floor area per dwelling is rising and the percentage of those using the water supply system is also increasing. At the same time, these are also municipalities with a small natural attrition rate and a still small but negative migration balance. Examples include Łomianki, Mrozy, Radzymin, and Baranów.

The third sub-type is formed by municipalities in the continuation phase of intensive suburbanization (type 3). These are units where there has been a marked improvement in the demographic and economic situation and housing development. Two sub-types can also be distinguished for this type. The first one (type 3a) is represented by the most attractive municipalities in terms of settlement, where there has been a strong, real increase in the population as a result of the natural birth rate increase and positive migration balance as well as a clear development of housing. These are also the units in which there has been a significant increase in the share of PIT in the municipality’s own income, which testifies to the increasing affluence of its residents that is higher than elsewhere. It has also been noted that, with the influx of new residents, businesses, especially sole proprietorships, are also relocating to these municipalities. Only two municipalities belong to this type, i.e., Wieliszew and Lesznowola.

Many more municipalities are represented by the second sub-type (type 3b). It is also distinguished by an increase in population, but in this case, there is a negative migration balance. These municipalities are demographically quite young. They show lower construction activity but stand out for having the strongest growth in the number of building permits issued, which may precisely indicate a stronger development of housing in the coming years. This sub-type is represented mainly by rural and urban-rural municipalities, i.e., Brwinów, Dębe Wielkie, Grodzisk Mazowiecki, Kobyłka, Nadarzyn, Prażmów, Żabia Wola, and Halinów.

The last type is represented by municipalities in the evolutionary phase of conditionality, where the most internally diverse suburbanization processes are taking place (type 4). Within this type, three sub-types can be distinguished. The first sub-type (type 4a) is represented by two cities: Warsaw and Żyrardów. It is characterised by an ageing population, a decline in construction activity in the period under study, and stagnation in investment in technical infrastructure, but at the same time clear economic development. These are the municipalities where the strongest growth in revenues to local budgets from PIT and CIT taxes is observed as well as an increase in the number of entities engaged in professional activities.

The second sub-type (type 4b), in turn, is characterized by a decline in construction activity, and a significant outflow of population with a fairly high birth rate. This group includes the municipalities of Jabłonna, Marki, Piaseczno, and Ząbki. These are municipalities which, a number of years ago, showed considerable settlement attractiveness and recorded a positive migration balance. However, in recent years, the suburbanization processes in them have weakened, but the effect of this “migration boom” and the influx of young families is replaced by a high birth rate.

The last sub-type (type 4c) is represented by one municipality—Serock. It is distinguished by a marked development of the tourist function and an increase in construction activity that has not been matched by the development of technical infrastructure. It is a municipality with a record increase in the number of tourist facilities and accommodation places as well as a clear increase in the number of new dwellings and their floor space. It is also the only municipality to record a decrease in CIT revenues received by the local budget.

As the results of these analyses show, the main spheres affected by/associated with suburbanization are the rate of economic development, the number, changes, and demographic characteristics of the population of the municipalities, construction activity, and investment in technical infrastructure.

4. Discussion

The results of this study show that suburbanization in the WMA is diverse in terms of directions and trends. It can be said that the municipalities went through (or are still going through) the first “suburbanization cycle”, experiencing an influx of migrants, which has changed their socio-demographic structure, and influenced the economy and spatial development. The rate of suburbanization varied, and was connected with, among other things, land rent and transport accessibility—these factors had the greatest impact on the popularity of individual municipalities.

The contemporary characteristics of the municipalities in the WMA (following 20 years of suburbanization) are the result of the historically found development conditions, the time when the “suburbanization of the municipality” started, and the course of this process. It is mainly about the degree of planning or its spontaneity. Municipalities which entered a phase of slower development (including a weakening of the suburbanization process) have usually exhausted their resources or lost their attractiveness for other reasons. In the long term, this may translate into their further development. It can also be assumed that a change in external conditions such as the development of transport infrastructure will modify the suburbanization paths in the WMA. It should also be remembered that, for several years now, there has been a return trend of “returners”, i.e., those coming back to cities, and they are not uncommon in the WMA. It is also noteworthy that, next to municipalities that have fundamentally changed their image in the last 20 years, there are municipalities that we called “transparent” in our analyses—despite the changes in the neighborhood profile of these municipalities, we do not find any signs of transformation of socio-demographic structures or functions or forms of development. The “transparent” municipalities are mainly found in the eastern part of the WMA. How long will they remain “transparent”? Data on property prices suggest that suburbanization will also make an appearance in the eastern part of the WMA in the near future.

The results of this study show that the WMA is an example of a complex functional and spatial structure. Urbanization in metropolitan areas leads to the development of urban-rural regions, which have the character of polycentric systems. This is the case of the WMA. In urban-rural regions with polycentric arrangements of settlement network elements, we are not only dealing with the issue of the relationship between the central city and its surroundings, but also with the complex relationships between the spatial components of the region [1,52]. The different characteristics of suburbanization in different parts of the WMA influence the specificity of these relationships.

The picture of the WMA municipalities in the context of their suburbanization does not give grounds to conclude that we are dealing with the emergence of an entirely new quality of suburbanization and thus with new relations in the urban-rural region [30]. The problems experienced convince us that the path of suburbanization that is being pursued is the path that could have been found in recent years in many countries with a high level of urbanization. In other words, in the case of the WMA, the mistakes in managing development at the regional, metropolitan, and local levels known from these countries are being repeated [53,54]. For this reason, it is difficult to judge if and when we can expect to witness a functional and spatial evolution of the WMA that enables us to speak of decentralized, sustainable development. Sustainable development in the context of metropolises and metropolitan areas sounds somewhat provocative, of course. However, new suburbs defined by terms such as edge cities, metroburbia, boomburgia, ex-burbia, technoburbia, or expolis make us take a critical look at the traditional model of suburbanization and revise previously held beliefs. New suburbs are located not in suburban zones but rather away from the central city. They are not dominated by the residential function. Jobs are located in them, and development trends indicate that new suburbs can become attractive labor markets. The larger-scale emergence of residential functions and accompanying services in new suburbs may change their face and influence the shape and functioning of regional settlement systems. Suburbs will become new, autonomous, multifunctional components of that system.

When discussing suburbanization in the WMA, it is necessary to refer to the general trends in the traditional model of this urban process. This model assumes that one of the main reasons why people migrate to suburban areas is the search for better living conditions. Moreover, suburbanization is stereotypically associated with the migration of families seeking convenient conditions for raising children. Typically, it is also the middle class who migrate to the suburbs because of the opportunity to purchase larger houses or flats at a lower price compared to the real estate market in the central city. In addition to the middle class, corporate employees are also migrants—many companies choose to locate their headquarters outside the city center, leading those employees who are looking for housing to choose nearby suburban areas. Suburbanization affects most areas with a well-developed transport infrastructure. It is also possible that an ageing population and changes in the structure of families and households are leading to the migration of older people looking for a quiet place to live.

The results of this study show that all—except one—of the components constituting the traditional suburbanization model are present in the case of the processes taking place in the WMA. The missing component is the migration of corporate employees to the suburban zone due to the location of the workplace. Corporate headquarters are still located in Warsaw.

All other components of the traditional suburbanization model are present in the WMA, but—as the typological classification shows—to varying degrees depending on the municipality.

The suburbanization process in the WMA was seen as a threat to smaller towns located in the metropolitan area both in the context of their identity and economic situation [55]. The municipalities usually do not cooperate in order to solve common problems, making it impossible for the whole WMA to develop in a sustainable way [22]. Comparing this research to the research conducted before on suburbanization in the WMA, this particular period of time (2002–2020) was not evaluated as well, as such a diverse variable selection has not been used yet. It seems that the differences in the suburbanization of municipalities in the WMA are particularly important from the point of view of development programming in accordance with the principle of sustainable development. A regional urban policy component and a multi-level management approach should be introduced into the discussion about the future of the WMA [56,57,58,59].

5. Conclusions

Suburbanization does not happen by chance. Its course, forms, and multidimensional effects are the effects of policies and the ways in which they are implemented. In functional terms and in terms of how politics are understood, suburbanization is a political issue: an appropriately organized socio-political system (the state, its institutions, regulations, and actions taken) should ensure socio-economic development by, among other things, addressing problems and conflicts arising from the failure of public authorities to respond adequately and in a timely manner. The current system of planning and management of urban functional areas in Poland does not counteract the emergence of problems and does not mitigate their consequences. For example, the uncritical adaptation several years ago of the competitiveness paradigm was reflected in the space by allocating large areas for housing development in many municipalities without rational justification.

In general, suburbanization had the most dynamic character and visible effects in municipalities with transport accessibility. The historically developed sectoral pattern and the infilling of areas between transport lines with development is evident. Economic development took place in suburbs with mixed functions. However, transport accessibility was also important here.

Most of the WMA municipalities are in the slowing down or moderate continuation phase of development and suburbanization processes. Type 3 municipalities are much less numerous. This type comprises municipalities in the continuation phase of intensive suburbanization. Generalizing, we can say that these are units where there has been a clear improvement in the demographic and economic situation and housing development. In part, these are demographically young municipalities. They are attractive in terms of settlements, show a strong real growth as a result of their high birth rate and positive migration balance, and have seen a clear increase in housing development. In some of them, the number of building permits has increased significantly, which promises stronger housing development in the coming years. In these municipalities, there has also been a clear increase in the share of PIT in the municipality’s own revenues. With the influx of new residents, companies, especially sole proprietorships, are also relocating to these municipalities.

As is clear from the previous discussion on the types and sub-types of municipalities, the main spheres affected by suburbanization are the rate of economic development, the number, changes, and demographic characteristics of the populations of the municipalities, construction activity, and investments in technical infrastructure.