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, including nine counties: Grodziski, Legionowski, Miński, Nowodworski, Otwocki, Piaseczyński, Pruszkowski, Warszawski Zachodni, Wołominski and the capital city of Warsaw, which has the status of a city with county rights (
Figure 1 and
Figure 2).
The Warsaw Metropolitan Area covers an area of 6,100 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.
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 municipalities types between 2003 and 2022 were 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 [
23]. Types C and D included almost 70 of all municipalities in the analyzed period.
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 neighbouring 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 favourable 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 favourable demographic situation was characterized mainly by rural municipalities well-connected to Warsaw such as Lesznowola, Jabłonna, Żabia Wola, Nieporęt, Wieliszew, 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 to 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, 22 municipalities were depopulating (31%), indicating that the situation was worse than in 2003. 20 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.
The choice of the Warsaw Metropolitan Area as the research area was caused by its unique functions, the nature of its internal and external links as well as the complex dynamics of urbanization processes. As the largest agglomeration 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 also performing important symbolic functions.
While Polish municipalities gained considerable autonomy and authority over the spatial planning, the resultant suburbanization, urban sprawl, and the formation of functional areas around cities have not, however, gained traction in debates over the city-regional planning. The issue of management of metropolitan areas development was resolved by a special act by the central government. On March 9, 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. Pursuant to the regulation of the Council of Ministers of June 26, 2017 on the establishment of a metropolitan association in the Silesian Voivodeship under the name "Upper Silesian-Zagłębiowska Metropolia", as of July 1, 2017, the first in Poland multi-purpose metropolitan union. The association 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. 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.
2.2. Research Design
The study used data and information from the Local Data Base (LDB), the RWDZ database - Register of Applications, Decisions and Notifications in Construction Matters and the Demography database. The data covered the years 2002-2021. 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 [
9]. These underlying dimensions are represented through the creation of new, composite variables known as factors [
7].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 [
3,
7].
Factor analysis is frequently employed by researchers from various scientific disciplines to analyze diversifications occurring in metropolitan areas [
1,
2,
16,
21]. This technique has also been used to identify transformations in the socio-spatial structure of Warsaw [
20]. However, there is a lack of studies demonstrating the direction of socio-economic and spatial changes occurring in the Warsaw Metropolitan Area, related to the suburbanization processes taking place within its region.
The analysis reduced the number of variables studied and enabled the identification of the most important areas of WMA’s diversification. The selection of characteristics for the analysis took into account a wide range of the area’s socio-economic variations associated with changes in:
population (change in natural birthrate; change in migration balance; change in old-age dependency ratios; change in feminization rate; change in population density; change in the number of marriages),
construction activity and housing stock (change in the number of building permits; change in the number of dwellings, including commissioned dwellings; change in the average floor area of dwellings; change in the number of persons per dwelling; change in the proportion using gas infrastructure, sewerage and water supply networks),
land use and spatial policy (change in the share of the area covered by the existing local spatial development plans; change in the share of the area of agricultural land subject to alteration in terms of designation for non-agricultural purposes in the plans; change in the share of the area of forest land subject to alteration in designation for non-forest purposes in the plans; change in the proportion of areas designated in the study requiring switching from agricultural land use to use for non-agricultural purposes; change in the proportion of areas designated in the study requiring moving from forest land use to use for non-forest purposes; change in the proportion of parks, green spaces and residential green areas; change in the share of green areas),
economic and investment attractiveness (change in the number of businesses - including micro-enterprises; change in the number of natural persons engaged in business activities; change in the share of PIT and CIT taxes in municipalities’ own revenues; change in the number of businesses in sections J-N, the creative sector, and the agri-food sector; change in the number of accommodation facilities and beds).
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 49 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 of the examined components. On this basis, four basic typological classes of municipalities were distinguished, and within them several subclasses.