How Smart Are V4 Cities? Evidence from the Multidimensional Analysis

Abstract

The article presents findings from the multidimensional comparative study focusing on the implementation of the Smart City concept in selected cities of the Visegrad Group (V4). V4 has been established by its member states (Poland, Slovakia, Czech Republic and Hungary) in a joint effort to break from the communist past and develop the common democratic values of the European Community. Aforementioned efforts involved meeting other important goals, such as socio-economic convergence processes including reaching better living conditions for V4 citizens. At present, strategies that aim to improve the wellbeing of citizens are manifested by Smart City concepts and are being implemented in V4 cities. The main research task in this article is to answer the question regarding regional (state) divergence in Smart City implementation in selected cities. The results of the study show that the best Smart City implementation scores are shared by Czech Republic towns. Polish cities (Kielce, Rzeszów, Lublin) located in the eastern regions of the country (which are also eastern border regions of the European Union) have the worst Smart City implementation scores, according to the value of the synthetic measure.

1. Introduction

Until the mid-2010s, Smart City was a little-known concept for the Visegrad Group countries (Poland, the Czech Republic, Slovakia and Hungary). However, starting from the mid-twenties, it has attracted much wider attention in the discourse on modern public space management and in scientific publications [1,2,3,4,5,6,7,8,9,10]. In addition to the concept of Smart City, this article covers the idea of a Sustainable City, which is characterized in this introduction.

The Visegrad Group (also known as the V4) brings together some of the former Soviet-dependent states. The leaders of Poland, Hungary and Czechoslovakia (later the Czech Republic and Slovakia) signed a joint declaration in 1991 which sought close cooperation in order to integrate with the European Community. The motivators behind this cooperation included: the desire to dismiss the communist legacy (a centrally controlled economy, outdated economic systems and a society deprived of political liberties and rights), serving to move beyond historical animosities, the pursuit of joint efforts to join the European Community by meeting the EU membership criteria for economically transitioning candidate countries (for the accession of the V4 countries to the community on 1 May 2004) and similar joint efforts towards NATO membership (with Poland, the Czech Republic and Hungary first to join on 12 March 1999, followed by Slovakia on 19 March 2004) [11] and the common set of values shared by leaders of such economically transitioning countries [12]. The Visegrad Group cities, which are the research subject of this article, are primarily oriented towards economic cooperation among the countries in this part of Central and Eastern Europe in order to bridge the economic and technological gap that exists between V4 countries and Western European countries. Priority areas of cooperation are the expansion of transport infrastructure and the strengthening of energy security in the region. There is also an opportunity for cooperation with third countries in the ‘V4+’ formula. The only fully institutionalized form of cooperation in the V4 is the Bratislava-based International Visegrad Fund (IVF). Before elucidating on how a city could combine the features of a Smart City and a Sustainable City, it is first necessary to define both of these concepts—and it is not an easy task, due to their complexity and ambiguity. Though the term Smart City is clearly defined, it is often used interchangeably with others, such as Clever City, Slow City or Digital City, all of which refer to different aspects of how an urban space functions [13,14,15,16]. The definition adopted as correct for the purpose of this study was the one presented by the authors of the Mapping Smart Cities in the EU report (pp. 21–23, [17]) representing one of the more extensive descriptions of the phenomenon. This definition states that a Smart City is “a city where public processes and activities are carried out thanks to the use of information and communication technologies. (…) a smart city is one that is characterized by a competitive economy, intelligent transport networks, sustainable energy use, high-quality social capital, high quality of life and intelligent public management.”

While each of these elements is an important building block, this paper mainly focuses on intelligent transport networks [18], sustainable energy use and intelligent public governance—as these activities largely depend on government supervision [19,20,21,22,23,24,25,26]. The study also takes into account indicators relating to the transparency of public institutions in metropolitan areas and explores the wide range of facilities provided to residents of these centers: parks, green areas, recreational spaces, health care facilities, public utility facilities, etc. Intelligent transport networks are transport and logistics systems integrated through ICT [27,28,29]. Sustainable resource use is based on the economic management of natural resources, by employing more renewable energy sources and implementing municipal network control systems to optimize costs and reduce their adverse environmental externalities. Finally, intelligent public management extends city management to include transparency in the operation of public authorities, the quality and availability of services and the dissemination of ICT technologies/infrastructure to enable real-time city management [30,31,32,33,34,35] and (pp. 42–43, [36]).

A Sustainable City should first and foremost take into account the needs of future generations. Therefore, according to the report entitled “Our Common Future”, issued by the World Commission on Environment and Development, such a city should meet the needs of its current inhabitants to a moderate degree, but without sacrificing the quality of life in the following decades [37,38,39,40,41,42,43,44,45]. A Sustainable City should therefore make efforts to limit the negative externalities related to environmental pollution (defined as excessive emission of exhaust gases and the resulting excessive heat in the urban space) [46,47,48], as well as combating smog emissions—which cause severe health problems for residents [49]—ensuring that health-neutral noise standards are not exceeded [50,51] and preventing the appropriation of recreation space for infrastructure, urban buildings and commercial spaces [52,53,54].

The main advantage of implementing a Smart City strategy is that it can develop along the lines of maximum subsidiarity in administration [55]. The dissemination of smart solutions for citizens minimizes the necessary state supervision and the responsibility for the city is delegated to the inhabitants [29,56,57,58,59]. A Smart City is a city with readily available digital information, which ensures intellectual and social development of its inhabitants [59,60,61,62,63]. As an entity, it serves as a great innovation hub in itself [64], directly affecting the well-being of its inhabitants [65] and providing them with a modern, but people-friendly place to live [60,66,67,68,69,70]. Although the benefits of such activities are manifold, they mainly relate to the administrative sphere, e.g., building a positive image for the administration, creating preferential conditions for entrepreneurs, generating savings for the municipal economy, improving transport and logistics systems in the city and building a positive image of a place friendly to residents and ideas [1,71,72,73,74,75,76,77,78,79,80,81,82,83]. The idea of a Smart City is beneficial both for the city implementing smart solutions and for the neighboring towns [84]. In the long term, a Smart City may become a solution to the problem of urban (or national) competition. Cities in the United Arab Emirates are shifting away from the oil-based model—correctly recognizing the possibility of a drop in oil prices or, in the long run, depletion of reserves—and moving towards entrepreneurial-, tourist- and, above all, resident-friendly Smart Cities [85] and (pp. 59–82, [86]).

Taking into account the above considerations, the objective of the study was to analyze the spatial differentiation of the smart concept among V4 cities. The core of this text is a reflection on the scale and scope in which the V4 cities fulfil the role of smart cities in accordance with the Smart City concept. Then, the correctness of the assumption about the primacy of large metropolitan centers over the others was considered. Finally, the question of a possible national specificity within the V4 was the direct trigger for this study. In other words, on the basis of identical, objective measures, is it possible to identify the leaders of such a juxtaposition in supra-regional terms?

The article focuses on a comparative analysis of the following Group cities: Prague, Brno, Ostrava, Plzen (Czech Republic), Budapest, Gyor, Miskolc, Debrecen, Szekesfehervar (Hungary), Poznań, Warsaw, Toruń, Szczecin, Gdańsk, Bydgoszcz, Wrocław, Olsztyn, Kraków, Białystok, Katowice, Łódź, Lublin, Rzeszów, Kielce (Poland) and Bratislava and Košice (Slovakia). Urban centers from the Visegrad Group countries have been chosen to show the evolution of the Smart City and Sustainable City concept in the area of Central Europe that was affected by systemic and economic transformation, starting from the last decade of the 20th century [87,88,89,90,91,92,93,94,95,96]. The final list of cities selected for the analysis was heavily influenced by their presence in international statistical records (above all in Eurostat publications), which allowed the authors of this paper to access to comparable numerical data. The results, which show how quickly the Smart and Sustainable City concept was implemented, may serve as an interesting basis for a comparative analysis of similar processes in other parts of the world, including other countries of the European Community, as well as, e.g., BRIC group countries (Brazil, China, India and Russia), which have undergone similar transformation. The experiences of the BRIC countries on the development of the Smart City concept can be a very valuable point of reference for the inhabitants of European metropolises (especially those in Central and Eastern Europe) (pp. 347–354, [97], pp. 2–232, [98] and p. 4, [99]). The above-mentioned countries may, over the next decades, become leaders in economic transformation (including digital transformation)—the experience of some of the new EC Member States may provide valuable tips on how to simultaneously achieve two important goals: economic/technological transformation and environmentally friendly sustainable growth [100,101,102,103,104].

To the best of authors’ knowledge, no compilation of the implementation of the smart concept in cities in the V4 group of countries on the basis of a single synthetic measure has been published so far. Individual cities have been ranked by other researchers (Cities in motion, smart cities, ranking of European medium-sized cities). The aim was to look for similarities and differences at the level of a group of countries with a similar level of past development and heritage.

The article consists of the following sections: introduction, materials and methods—a section presenting the synthetic smartness index and the statistical methods used in the research—research results, discussion and conclusion.

2. Materials and Methods

A “synthetic indicator of smartness” was used to indicate the spatial dispersion of this issue (and its many socio-economic subsets) and compare it across regions in an accessible way (i.e., using a single numerical value). This is carried out by translating a multivariate set of variables into one numerical value, usually from a specific range, thus clearly expressing the extent of the considered phenomenon. Then, from the arrangement of the obtained numerical values, the situation of individual areas can be compared against each other. The procedure itself consists of several steps that are detailed below.

The selection of diagnostic variables (partial indicators) is crucial for the reliability of the obtained results. They must meet formal and statistical criteria, but above all they must be substantive. The variables used for the final analysis (pp. 36–37, [105]) were selected for: widespread recognition, measurability, the availability of numerical data, relatively high quality and high substantive value. One of the attributes of the variables put forward for the study is their authenticity, which was ensured in part by weighing them against the population number and taking into account its proportion in the percentage composition of the phenomenon. Thus, the final values of the diagnostic variables (and thus the results of subsequent classification) are unaffected by the city size (measured by the number of inhabitants or by absolute numbers). Therefore, the empirical analysis only included those variables that met the requirements set by the formal and statistical criteria, namely those characterized by (p. 95, [106]):

(a)

Completeness of data across the entire analyzed time series;

(b)

Sufficient spatial variability, measured by the coefficient of variation (v_j > 10%);

(c)

No excessive correlation between variables (Pearson correlation coefficient < 0.85).

The presence of highly correlated variables in the set of diagnostic variables means that these features give more weight to data that are duplicated in the analysis (similar data are brought to the analysis through correlated variables). This may lead to a situation where the taxonomic analysis does not reliably represent the examined reality by placing excessive weight on over-correlated variables. The selection of diagnostic features was followed by the next stage of the study, namely unitarization. Unitarization is one of the normalization formulas (the others being standardization and normalization) which reduce the variables to a specific interval (comparability), while making them unitless. This is implemented to avoid situations where variables with large absolute values (an order of magnitude higher than other variables) would have a decisive share in the construction of the standard-of-living synthetic index. This would mean that the results of the classification could be distorted by these variables, over-representing their influence on the other variables. The unitarization procedure offers an advantage over standardization by preventing a situation where the final result of the synthetic index would be strongly influenced by the extreme values of some variables. Thus, unlike standardization, unitarization eliminates such scenarios, reducing the data to the range from 0 to 1 with a closed interval (p. 18, [107]). This reduced variables (often described by different units) for comparability (in this case—to values between 0 and 1), using the following formula:

$$z_{ij}=\frac{x_{ij}-\underset{i}{ \min }\left\{x_{ij}\right\}}{\underset{i}{ \max }\left\{x_{ij}\right\}-\underset{i}{ \min }\left\{x_{ij}\right\}}$$

(1)

where:

• $z_{ij}$—the unitized value of the j-th variable for the i-th object;
• $x_{ij}$—the value of the j-th variable for the i-th object.

Each of the variables included in the study was evaluated by identifying the stimulants and the destimulants. The latter had to be followed up with a stimulation process, i.e., transforming a destimulant into a stimulant to ensure uniform direction of influence across all variables, so the higher values of the final synthetic index indicated a better standard of living. The following stimulation formula was used to this end (p. 18, [108]):

$$x_{ij}=a-b{x}_{ij}^D$$

(2)

where:

• j—variable;
• i—research object (city);
• a, b—arbitrary constants: b = 1, a =$\underset{i}{ \max }\left\{x_{ij}^D\right\}$;
• $x_{ij}^D$—the value of j-th destimulant in i-object.

The next stage of the analysis was to select the coordinates of the model object, consisting of the most favorable values of the variables, as observed in communes

$$z_{0j}=\{\begin{array}{c}\underset{}{\underset{i}{\max }\left\{z_{ij}\right\}dla z_j^S}\\ \underset{i}{\min }\left\{zij\right\} dla z_j^D\end{array}$$

(3)

The distances of the individual cities from the standard were then calculated using the following Euclidean metrics (p. 69, [109]):

$$d_{i0}=\sqrt{{\displaystyle \sum _{j=1}^m{(z_{ij}-z_{0j})}^2}}$$

(4)

where:

• d_i0—the distance of the object from the pattern;
• z_ij—the value of normalized variable j for the i-th object;
• z_0j—the coordinates of the reference object for the j-th variable.

The penultimate stage of the study was to determine the value of the synthetic index, which was used to rank cities according to the standard of living of their inhabitants. The following formulas were used in the calculations (p. 69, [109]):

$$s_i=1-\frac{d_{i0}}{d_0}, d_0={\overline{d}}_0+2S(d_0), {\overline{d}}_0=\frac{1}{n}{\displaystyle \sum _{i=1}^n{d}_{i0}}, S(d_0)=\sqrt{\frac{1}{n}{\displaystyle \sum _{i=1}^n{(d_{i0}-{\overline{d}}_0)}^2}}$$

(5)

where:

• S_i—the measure of synthetic development;
• d_i0—the distance of the object from the model;
• $\overline{ d}$—arithmetic mean d₀;
• S(d₀)—standard deviation d_0.

The last stage of the analysis, after ordering the cities according to the standard of living values, was to group individual units into four clusters based on the synthetic index obtained. The classification was established according to the following ranges:

$$\begin{gathered} \mathrm{Tier} 1: w_i\in \left[\overline{w}+s_w,1\right], \\ \mathrm{Tier} 2: w_i\in \left[\overline{w},\overline{w}+s_w\right), \\ \mathrm{Tier} 3: w_i\in \left[\overline{w}-s_w,\overline{w}\right), \\ \mathrm{Tier} 4: w_i\in \left[0,\overline{w}-s_w\right). \end{gathered}$$

where:

• w_i—synthetic indicator;
• $\overline{w}$—the mean value of the synthetic indicator;
• $s_w$—the standard deviation of the synthetic indicator.

On the basis of selected variables, an analysis of the spatial differentiation of the implementation of the smart idea was carried out using the Hellwig’s development pattern method (pp. 304–320, [110]) and (pp. 153–158, [111]). The obtained synthetic values of the development index were used to arrange cities linearly according to the strength of the subject phenomenon.

This analysis was supplemented by a determination of similarities, in this regard between the cities. Grouping was carried out using classification methods in order to distinguish the most homogeneous clusters of objects in terms of similarity in the structure of observed values—in this case, the synthetic measures of “smartness”. The groups of objects show strong differentiation across the groups and maximum homogenization within the groups (p. 66, [112]).

Finally, the Ward’s method was used for agglomerate hierarchical clustering, which uses the number of clusters equal to the number of test objects as the starting point. The criterion for grouping units into consecutive clusters (groups) is the minimal variance of the values for the features (p. 122, [113]), which are used as segmentation criteria relative to the value of the clusters created in the successive steps. As a result, the objects included in a single group are as similar as possible in terms of the analyzed features. On the other hand, subsequent iterations are determined by the distance (d_ip) between the newly created cluster and the remaining clusters, calculated from the formula below (p. 278, [114]):

$$d_{ip}=\frac{n_i+n_k}{n_i+n_j+n_k}{d}_{ik}+\frac{n_j+n_k}{n_i+n_j+n_k}{d}_{jk}-\frac{n_k}{n_i+n_j+n_k}{d}_{ij}$$

(6)

where:

• n_i—size of the cluster i;
• n_j—size of the cluster j;
• n_k—size of the cluster k;
• d_ik—distance between primary cluster i and cluster k;
• d_jk—distance between primary cluster j and cluster k;
• d_ij—distance between primary cluster i and cluster j.

The Ward’s method is widely accepted due to its theoretical properties and favorable results of simulation studies, producing very good grouping results with highly homogeneous clusters. As a result of the simulations, T. Grabiński and A. Sokołowski proved that the effectiveness of detecting the real data structure in this method is about 40% higher than the farthest neighbor method, which rates second in effectiveness [115,116]. Its strengths also toile in the transparency of the presented content, which is presented in the dendrogram form.

In an attempt to build a synthetic measure of city smartness, which would describe the spatial variance in the implementation of the Smart City concept, a taxonomic analysis was initiated, with the first step being diagnostic feature selection. It should be emphasized that this is the most subjective stage of the analysis, because it requires the researcher to select the features that will best characterize the analyzed phenomenon. Therefore, the selection of diagnostic variables for the calculation of the synthetic index was based on both substantive and formal/statistical criteria. The variables initially qualified for the study were selected based on the review of literature and were characterized by (pp. 37–38, [105]): widespread recognition, high substantive value, measurability, the availability of numerical data and relatively high quality. The variables were related to the number of inhabitants in order to reduce the influence of city size on the final values.

The research sample encompassed statistical data on the standard of living from 26 leading urban centers of the Visegrad Group (V4) countries. A comparative analysis was carried out for the year 2018. European Commission publications on European cities, including 26 V4 cities, were used as a reference point. Most of them were Polish cities (15) due to their demographic potential. The catalog of cities was rounded off with five Hungarian, four Czech and two Slovak cities. The indicators adopted for the analysis are measurable and reliable, as the data were taken from official Eurostat publications, as well as reports, publications and information portals aggregating data at various administrative levels.

The variables used for the analysis encompass many areas of life, including economic and demographic indicators, socio-cultural infrastructure and environmental protection. Their systemic selection was dictated by the analysis of (p. 10, [117]). The authors, after analyzing numerous publications, determined that the most important activities for improving urban living conditions in accordance with the Smart City concept were those related to urban planning, city infrastructure, mobility, public safety, health, sustainability and public policies. For this reason, an attempt was made to include variables describing the standard of living in the city in economic terms, but social, environmental and civic aspects were also taken into account. Some of the potential variables were eliminated at the preliminary selection due to incomplete data and, in some cases, difficulties in aggregating data at the given administrative level due to organizational and formal considerations. Other researchers exploring this subject faced similar problems [10], (pp. 6–7, [118]), (pp. 4–5, [119]), (pp. 121–125, [120]) and (pp. 45–47, [121]). The set of variables was based on several databases which were also used in other studies [122]. After formal and statistical verification (see Appendix A), the final set of fifteen variables (describing the spatial variation in the smartness across the leading cities of the V4 group) was completed (

Table 1. Diagnostic variables included in the synthetic ‘smart’ indicator for V4 cities in 2018.

Variable	Type of Variable	Source
GDP per capita	Stimulant	Eurostat
Age dependency ratio	Destimulant	Eurostat
Unemployment rate (%)	Destimulant	Eurostat
Single-person households (%)	Stimulant	Eurostat
Foreigners in the total population	Stimulant	Eurostat
Digital Governance Maturity (DGM)	Stimulant	World Bank
e-Payment Service	Stimulant	World Bank
Corruption Perception Index (CPI)	Stimulant	World Bank
Green areas as a share of total city area (%)	Stimulant	Eurostat
Share of city inhabitants exposed to PM 2.5 pollution	Destimulant	Eurostat
Healthcare Index *	Stimulant	Numbeo
Motor vehicle fatalities	Destimulant	Eurostat
Cars per thousand population	Stimulant	Eurostat
City bikes per thousand population	Stimulant	Eurostat
Number of wi-fi hotspots in the city	Stimulant	Wifimap.io

Source: Own study; * means that it was estimated of the overall quality of the healthcare system, health professionals, equipment, personnel, doctors, costs, etc.

).

3. Results

The research results showed that Czech cities definitely had the highest synthetic measure scores among the V4 cities implementing Smart City strategies. The values for Prague (0.5355) and other Czech cities were the only ones that exceeded 0.3 (

Table 2. Ranking of the cities based on the synthetic measure of smartness in 2018.

No.	City	Value of the Indicator in 2018	Class	Country
1	Prague (CZ)	0.5355	1	Czech Republic
2	Brno (CZ)	0.4086	1	Czech Republic
3	Ostrava (CZ)	0.3067	2	Czech Republic
4	Plzen (CZ)	0.3047	2	Czech Republic
5	Poznan (PL)	0.2792	2	Poland
6	Warsaw (PL)	0.2778	2	Poland
7	Bratislava (SK)	0.2649	2	Slovakia
8	Budapest (HU)	0.2307	2	Hungary
9	Torun (PL)	0.2293	2	Poland
10	Szczecin (PL)	0.2277	2	Poland
11	Gdansk (PL)	0.2161	2	Poland
12	Gyor (HU)	0.2148	2	Hungary
13	Bydgoszcz (PL)	0.1994	3	Poland
14	Wrocław (PL)	0.1906	3	Poland
15	Olsztyn (PL)	0.1727	3	Poland
16	Kosice (SK)	0.1530	3	Slovakia
17	Cracow (PL)	0.1409	3	Poland
18	Miskolc (HU)	0.1388	3	Hungary
19	Debrecen (HU)	0.1353	3	Hungary
20	Białystok (PL)	0.1294	3	Poland
21	Katowice (PL)	0.1165	3	Poland
22	Łódź (PL)	0.1149	3	Poland
23	Lublin (PL)	0.1051	3	Poland
24	Rzeszow (PL)	0.1001	4	Poland
25	Szekesfehervar (HU)	0.0948	4	Hungary
26	Kielce (PL)	0.0745	4	Poland

Source: Own study.

) and constituted a point of reference for other cities. These cities were followed by two Polish cities, Poznań and Warsaw. The next highest values of the synthetic measure were achieved by the capitals of the remaining V4 countries, i.e., Bratislava and Budapest. The high scores of capitals in the ranking are not surprising, and stem from the endogenous capacity of these metropolises, which play an important role as metropolitan growth centers. The capitals of the regions of eastern Poland had the lowest scores. These areas face many social and economic problems, the most important of which are undoubtedly depopulation (especially in rural areas) and population aging, relatively high unemployment and low wages and infrastructure deficiencies (which mainly include problems related to the standard of living, municipal infrastructure and transport infrastructure). In the lists of EU regions with the lowest GDP per capita, Polish regions with the capitals of Kielce, Rzeszów and Lublin, as well as the poorly developed Bulgarian and Romanian regions, rank at the top. Out of all Hungarian cities included in the analysis, the lowest value of the synthetic measure was recorded for Szekesfehervar.

As mentioned earlier, for the purposes of the analysis, four classes were distinguished according to the value of the synthetic measure: above average, average, below average and low, respectively. After taking into account the diagnostic variables selected for the study, out of all the cities included in the study, only two were included in the first class—the two Czech cities of Prague and Brno, whose value of the synthetic measure clearly exceeded the value for other cities in this comparison (

Table 3. Classification of the V4 cities into four types by the synthetic value.

	2018
Class 1	Prague (CZ), Brno (CZ)
Class 2	Ostrava (CZ), Plzen (CZ), Poznań (PL), Warsaw (PL), Bratislava (SK), Budapest (HU), Toruń (PL), Szczecin (PL), Gdańsk (PL), Gyor (HU)
Class 3	Bydgoszcz (PL), Wrocław (PL), Olsztyn (PL), Kosice (SK), Cracow (PL), Miskolc (HU), Debrecen (HU), Białystok (PL), Katowice (PL), Łódź (PL), Lublin (PL)
Class 4	Rzeszów (PL), Szekesfehervar (HU), Kielce (PL)

Source: Own study.

). The two subsequent classes were actually equal in number, with ten cities assigned to the second class and eleven included in the third class (with below-average values), according to the value of the synthetic indicator. The last class, defined by the low values of the measure, was made up of three cities: the aforementioned Hungarian Szekesfehervar and two cities of Easter Poland, Kielce and Rzeszów. The size of each class is normal, indicating that the values were mostly close to the population mean, while extremes, both positive and negative, accounted for a small fraction of all values.

The next step aimed to identify which cities in the group of V4 countries are most similar to each other in terms of Smart City implementation. Using agglomeration methods, the 26 cities included in the study were initially grouped into five clusters (Figure 1). Two of them, however, were single-element clusters and were created from the cities with the highest values of the synthetic measure (Figure 1). The next grouping consisted of five data points and included the remaining Czech cities, two leading Polish cities (Poznań and Warsaw) and the capital of Slovakia. They were followed most closely by seven cities, including five Polish and two Hungarian, including the final capital on this list, Budapest. Only in the further stages of the analysis did all these units form one cluster. The last (base) cluster, i.e., the most isolated one, had as many as 12 cities. The cut-off point, in this case, was Olsztyn (fifteenth in the ranking of cities based on the value of the synthetic measure), a regional capital in the northeastern part of Poland (Figure 1). The cities included in this cluster constituted the most homogeneous group of cities, but the most distinct in relation to the remaining ones. They clearly differed not only from the leaders of this list, but also from the cities of the fourth cluster (demarcated by Wrocław, which placed 14th in the synthetic measure ranking).

4. Discussion

Based on the results presented above, it can be concluded that the Czech cities (Prague, Brno, Ostrava and Plzen) scored the highest out of all the analyzed cities. Therefore, the second research question—pointing to the impact of national specific policies—can be answered positively. In studies on the implementation of sustainable development (p. 65, [123]) and Smart City solutions [124], the Czech Republic is the leader among the V4 group (p. 31, [125]). Its success could be explained by the efficient deployment of e-governance [126,127,128,129,130], socio-economic potential [131], the condition of the labor market [132] and its openness to economic immigrants [133,134]. The quality of health care was also important (p. 142, [135]) and [136]. Therefore, the high position of Czech cities should come as no surprise. This is also reflected in the opinions of the residents of these cities [70] and (p. 164, [137]). This finding is also corroborated by studies on large and medium-sized cities in Central and Eastern Europe (p. 1485, [95]). It is important to indicate that all of the Czech cities mentioned above have strived to implement elements of the Smart City strategy. For instance, Prague is implementing the Smart Prague 2030 strategy [138]. Brno is implementing a long-term development strategy to 2050, called vision # brno2050 [139]. The Smart City development strategy has also been pursued by Ostrava [140] and Plzen [141]. Clearly outlined and consistently implemented Smart City development strategies contribute to the high scores of the Czech cities. However, it should be noted that similar strategies are being deployed across all V4 countries [142,143]. Examples of interesting implementations improving smart cities in V4 include the Vooom application for safe planning of public transport, shared vehicles and taxis in the era of the COVID-19 pandemic—the project is being implemented in Gdansk. Another example is the Mozaweb toolkit for a complex educational program used in remote education in Hungarian cities. In the Czech Republic, an application called Zachranka was developed to support emergency calls. Golemio Prague, on the other hand, is a collaborative digital data administration of various city departments. The Monse project in Slovakia is a remote health monitoring system for senior citizens living alone at home in Hungarian cities [144]. Nevertheless, even in the case of Czech cities, the implementation of the Smart City strategy has not been without major problems. This issue was described by Martina Jaňurová and Markéta Chaloupková [8]. When faced with issues of competitiveness, human capital, civil participation of residents, transport, information and communication technologies (ICT), natural resources and quality of life, even the Czech metropolises which top the ranking have to deal with numerous problems with the deployment of Smart City strategies. Where large groups of people are concerned, one of the potential problems relates to the interpenetration of competences and the conflicting interests of individual parties [145]. Metropolises may struggle with insufficiently strong delineation of competences and tasks across central, regional and local management. Excessive centralization may lead to the delayed implementation of various components of the Smart City strategy in cities subordinate to the decisions of the state governments, which are usually headquartered in national capitals [146]. In addition, V4 cities may face the problems of poor communication between urban centers and higher-level institutions, poor-quality infrastructure cutting off cities from important communication routes and the lack of professionally educated local decision-makers to implement local strategies and allocate funds from European Community operational programs and other sources (p. 95, [8]). The authors point to the numerous problems faced by Czech cities—the leaders of the present V4 group ranking—which include: the lack of appropriate legislation that accounts for long-term development strategies for individual cities and regions, limited financial resources for such long-term strategies, insufficient decision-making power of cities due to excessive centralization, the reluctance of decision-makers to participate in planning and dependence on the decisions of the political central leadership, which may be pressured to act by political representatives of a given region of the country (pp. 97–101, [8]) and [147].

The aforementioned shortcomings related to excessive centralization may be of significant importance when analyzing the placements of the different V4 capitals: Prague, Budapest, Warsaw and Bratislava. Prague’s high ranking compared to other capitals is corroborated by reports [122,148,149] and studies (p. 95, [96]) and [150]. The capitals of the V4 Group countries stand out from other cities on the list (p. 12, [125]), which is characterized by high divergence rates, especially evident in the period of 2006–2012 for Slovakia and the Czech Republic (pp. 59–60, [151]). The analyses confirmed the specificity of these cities, which was one of the research questions presented in this study. Excessive disproportion between the capitals of states and other cities in the V4 countries may be constrained by the funds flowing in over the last dozen or so years, under the EU cohesion policy, intended to narrow the gap between average-developed and the least-developed regions of the European Community [87,152,153,154,155,156,157,158,159,160]. In this case, regional capitals must find their own resources or funds from other operational programs, allocated specifically for the implementation of Smart and Sustainable City strategies [161].

Regardless, the regions of the Polish East, i.e., Lublin, Rzeszów, Białystok and Kielce, have the worst scores in the ranking. The problem of these cities may lie in their gradual depopulation—the outflow of young people studying and seeking work in other cities, and the resulting aging of the eastern urban communities (p. 16, [45]) and [162,163,164]. It is also worth noting the positive examples from the list, including Poznań (which stands out in comparison to other cities, such as Kraków) [165] which is one of the fastest-developing smart urban centers on the list [166].

It should be emphasized that the concept of Smart City is not clear-cut. Therefore, the choice of diagnostic variables remains ambiguous as well, and their selection may affect the final ranking of the level of ‘smartness’ of the given cities. This is an important limitation that the authors are aware of. The choice of method has certain consequences. However, the overarching aim, which was to identify the spatial differentiation of Smart City implementation in V4 cities, seemed appropriate for the use of this form of data presentation and analysis. The analysis made in the article should be noteworthy as a reference point for all professionals interested in the latest trends regarding the functioning of the modern urban sphere. Limiting the choice of cities to the Visegrad Group may be important due to the major significance and special role of countries that have undergone systemic and economic transformations for over thirty years and are currently undergoing digital transformation as well. The V4 group countries are also new Member States of the European Community. In their case, the efficient implementation of the Smart City and Sustainable City concepts should help neutralize the historical divisions related to the membership of Poland, the Czech Republic, Hungary and Slovakia in the Council for Mutual Economic Assistance under the leadership of the Soviet Union, and their subsidiarity to and dependence on political and economic decisions being taken in Moscow. For over three decades, these countries have been bridging the economic gap between them and the European Community Member States, with increasing success—with the implementation of the Smart City and Sustainable City strategies being one of the examples of positive trends.

5. Conclusions

In the article, it discusses the international universalism of the implementation of the studied concept (within the context of the selected examples). In this respect, the Czech cities stand out from the rest. Prague and Brno were homogeneous within their cluster and clearly distanced the others, which were grouped in the second cluster with the V4 capitals. Polish and Hungarian cities performed the poorest, with the weakest implementations of the Smart City concept compared to the other cities. The socio-economic condition of these areas, especially the regions of eastern Poland, shows that they have a relatively greater gap to close. The results of the analyses also confirmed the pattern for the state capitals, where the well-being of citizens, especially related to the performance of metropolitan functions, is linked with their positions in the ranking. Their specific ranks (compared to other capitals and leading European cities) were in line with the works of other authors.

The experiences of the Visegrad Group countries should also be important for other regions of the globe, including countries undergoing transformation processes, e.g., BRIC group countries (Brazil, China, India and Russia). Identifying the patterns of best practice, as well as signaling potential problems related to the implementation of the Smart City and Sustainable City strategies, should be a valuable reference for professionals responsible for the implementation of similar concepts in other parts of the world, supporting efficient implementation changes related to the modernization of urban centers, while preserving the natural environment.

The article suggests recommendations for public authorities and city administrations, referring to the cited studies. The recommendations indicated can be statistically verified (through qualitative research) in subsequent studies as a continuation of this paper.