Research of the disparities in the process of revitalization of brownfields in small towns and cities

sustainability Article Research of the Disparities in the Process of Revitalization of Brownfields in Small Towns and Cities Natalie Szeligova 1, Marek Teichmann 2,* and Frantisek Kuda 2   Citation: Szeligova, N.; Teichmann, M.; Kuda, F. Research of the Disparities in the Process of Revitalization of Brownfields in Small Towns and Cities. Sustainability 2021, 13, 1232. https://doi.org/10.3390/ su13031232 Academic Editors: Enzo Martinelli and Radim Cajka Received: 27 November 2020 Accepted: 21 January 2021 Published: 25 January 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Department of Spatial Planning and the Environment, Karvina City Authority, Frystatska 72/1, 733 24 Karvina, Czech Republic; [email protected] 2Department of Urban Engineering, Faculty of Civil Engineering, VSB—Technical University of Ostrava, Ludvika Podeste 1875/17, 708 00 Ostrava-Poruba, Czech Republic; [email protected] *Correspondence: mar[email protected]; Tel.: +420-597-321-963 Abstract: The subject of the work is the research on relevant factors influencing participation in the success of brownfield revitalization, especially in the territory of small municipalities. Research has so far dealt with the issue of determining disparities in the municipalities of the Czech Republic, not excluding small municipalities, but their subsequent application has usually been presented in larger cities. The focus on smaller municipalities or cities was usually addressed only in general. The introduction provides an overview of theoretical knowledge in the field of brownfield revitalization. Defining the level of knowledge of the monitored issues is an essential step for the purposes of more effective determination of disparities. Disparities will be determined on the basis of information on localities that have been successfully revitalized. The identified disparities are then monitored in the territory of small municipalities. For the purposes of processing, it was determined that a small municipality or city is an area with a maximum of 5000 inhabitants. Using appropriately selected statistical methods, an overview of disparities and their weights is determined, which significantly affect the success of revitalization. In small municipalities, the issue of brownfields is not emphasized but, in terms of maintaining community strength and reducing population turnover, the reuse of brownfields is a crucial theme. Keywords: brownfield; disparities; indicators; regeneration; land use 1. Introduction The importance of brownfield regeneration is closely connected with the protection of the agricultural land fund and the open landscape, which is one of the exhaustible and usually nonrenewable resources. One of the possibilities of preserving greenfield sites is the reuse of so-called brownfield sites (i.e., areas, buildings, and land) that are no longer used today, abandoned, usually burdened by a certain degree of contamination and affected by the original purpose of use [ 1 , 2 ]. Most of them are located on very lucrative lands in the built-up area of towns and villages. They thus become one of the elements limiting the development of the territory, and their existence usually contributes to creating a negative view of the city as a whole, mainly due to their negative characteristics, but also in terms of various accompanying aspects related to them, such as socio-pathological phenomena, crime, rising unemployment and other social, economic and environmental phenomena [ 3 ]. However, the use of these brownfields can lead to a reduction in the amount of unproductive dilapidated real estate and at the same time to an influx of new investors, but above all to the preservation of greenfield sites [4]. The term brownfield is not enacted in the Czech legislation, as is the case in many other countries of the European Union. In the Czech Republic, a definition from the National Brownfield Regeneration Strategy can be given: “A brownfield is a property (land, building, area) that is underused, neglected and can even be contaminated. It arises as a remnant of Sustainability 2021,13, 1232. https://doi.org/10.3390/su13031232 https://www.mdpi.com/journal/sustainability Sustainability 2021,13, 1232 2 of 18 industrial, agricultural, residential, military, or other activities. Brownfield cannot be used properly and efficiently without a process of regeneration.” [5]. For comparison, the Czech definition is supplemented by the American definition according to the Environmental Protection Agency (EPA) organization: “brownfields are abandoned, empty, unused industrial or commercial areas, where their original use has caused some contamination, where there is potential for regeneration. They typically include old industrial bodies of water, abandoned mines, former railway stations, abandoned gas stations and former treatment plants” [6]. In the territory of small municipalities, the process is complicated by the more limited possibility of obtaining subsidies. Currently, this trend is beginning to reverse and the mayors of municipalities make extensive use of these financial resources. The fact that the revitalization of small municipalities in the eyes of the public is often described as a success for a larger territorial unit (for example, successful revitalization in a small town is described as a success for a regional city or a territorially close large city) was found to be a major shortcoming. The main goal of the CircUse project, for example, was to support the sustainable development of the area and at the same time protect the environment by supporting the reuse of areas that are no longer in use today, brownfields, in favor of protecting the land fund. It proposes the recycling of areas as an option for the protection of the agricultural fund [7–10]. Much revitalization has been carried out in the territory of small towns and these are often successful projects, usually of a small scale, but they are essential for the development of the municipality. The issue of brownfield regeneration is becoming of interest in smaller municipalities, and so, for example, agricultural brownfields have already become part of the open landscape in many places and residents have become accustomed to their existence close to home [11–13]. In the existing literature, indicators are used for the evaluation of municipalities, which usually verify their potential development and relationship to a healthy environment. For the evaluation of brownfield areas, the evaluation of the suitability of the locality for subsequent revitalization is often missing. Many authors consider that the most effective way to revitalize a brownfield area in the territory of small municipalities for community purposes is by multifunctional buildings, which are used throughout the year for leisure, social and cultural activities, sports activities, rest and the education of children for all residents, without distinction [14–23] . The implementation of community centers in abandoned buildings, especially in small villages and towns, is popular in the USA and Canada. In the last ten to fifteen years, with a change in the lifestyle of the population and a change in the approach to nature protection, a large number of associations and organizations are being established which are actively involved in the process of revitalizing abandoned buildings. These do not have to be private individuals, but can also be organizations that are backed by large municipalities. Reduced space rental costs create a large number of small businesses that are set up only for a limited time until the founder secures sufficient resources for further development. This is one of the new approaches to the temporary use of resources, because it is always more appropriate to use the resource at least partially than to leave it abandoned. 2. Materials and Methods From available sources, it was found that the relationship between disparities and the process of brownfield revitalization has not been comprehensively addressed. Many authors [ 24 , 25 ] use the term disparity as a synonym for the term indicators. Indicators are commonly discussed in strategic documents, territorial analysis documents and other documents related to territorial or regional development. It follows from the definition of disparities that it is not a synonym for the term indicator, but often the two terms are confused. In general, disparities are evaluated negatively, but their identification can also Sustainability 2021,13, 1232 3 of 18 lead to positive results, which can describe the significance and potential of, for example, the project in relation to others [24]. Most authors deal with the issue of disparities within the following definition of the word—in general, disparities mean inequalities or differences. Some authors address regional disparities as a multidisciplinary issue that can become a major obstacle to achieving sustainable development goals [ 24 – 26 ]. The methodology and scope of disparity detection depends on the purposes for which the research results will be subsequently used. First of all, it is necessary to perform an analysis and then the creation of a list of the most important disparities that can subsequently motivate specific authorized entities to propose possible variants of the approach to the monitored issues. Disparity is “any difference or inequality, the identification and comparison of which makes some sense (social, economic, political, etc.)” [ 24 ]. Disparities accompany every development project, in every territory and are variable over time. Disparities can be influenced or unaffected. Identified disparities usually have the following characteristics: •cognitive: represents an overview of information on monitored attributes; • motivational: based on the findings, can lead to the motivation of the competent authorities to correct, to take action; • operational: based on the information obtained, it is easier to respond to the everchanging situation; •decision-making: it is easier to make a decision based on the information found [24]. The countries of the European Union also face regional disparities, which are often influenced by the approach to solving them in individual member states. In many countries, regional policy is perceived as a national problem, not a single region problem. The approach to the solution and evaluation of regional disparities on the scale of the countries of the European Union is not focused only on individual regions and their specific disparities, but is taken into account from a broader national and international perspective. This makes the region competitive and supports economic growth in all countries of the European Union [24]. 2.1. Research Target The goals of the research were determined on the basis of the shortcomings identified during the process of searching for current knowledge of the issue, the experience resulting from previously implemented projects, and the needs of municipalities. The main goal of the research is to determine the disparities affecting the success of brownfield revitalization, especially in small municipalities and cities, and then assess the data using appropriate statistical methods. The subobjectives of the research include, in particular: •evaluation of the current state of the monitored issues; •overview of disparities and determination of significance weights; •evaluation of data obtained from statistical methods. The subobjectives are met through the following procedure: • an overview of successfully revitalized brownfield sites which took place more than five years ago (municipalities with less than 5000 inhabitants); •defining relevant indicators (disparities) that significantly affected regeneration; • selection of small municipalities (including an overview of successfully revitalized brownfield sites) based on criteria and knowledge of the current state of the monitored issues; • determination of disparities affecting the success of revitalization and subsequent comparison of data, with data identified in the initial phase of research; • using an appropriately selected statistical method to establish an overview of relevant indicators; • evaluation of disparities and determination of their significance in the revitalization process; Sustainability 2021,13, 1232 4 of 18 •proposal of a suitable approach to the decision-making process in the field of brownfield regeneration. In the initial phase of the research, data were collected from publicly available sources. Subsequently, at least 10 successful projects were selected, which were implemented approximately five years ago, regardless of the size of the municipality and country of origin. The disparities that contributed most to influencing the success of these projects were monitored. Then, at least 10 revitalized brownfield sites in selected small municipalities were selected. The identified disparities were subjected to graphical and statistical evaluation, the result of which is the evaluation of which of these disparities are the most significant (and, respectively, the least significant) in the process of brownfield revitalization. An equally important part of the work was the evaluation of the approaches of small and large municipalities to the monitored issues. In the final phase of elaboration, a document was created, the main purpose of which was to create a practical methodological guide for the decision-making process for representatives of small municipalities and cities to help assess the potential of individual areas. For more effective knowledge of the monitored issues and verification of the suitability of the model, the following research questions were defined, which were tested during the research. Based on the obtained results, it will be possible, for example, to express the potential of small towns and municipalities as areas suitable for investment. • What kind of financing prevails in the process of brownfield revitalization in small municipalities? • Which attributes (disparities) most influence investors in the investment decisionmaking process? • Is the process of brownfield revitalization more demanding in the territory of small municipalities? If so, can this be expressed through disparities? • Are there differences in indicators (disparities) in the process of brownfield revitalization in large and small municipalities? • Is there a direct link between the success of brownfield revitalization and the size of the municipality in which the site is located? 2.2. Data File Sources Determining a clear database was a key part of the research. Many portals of regions, cities, municipalities, and various organizations provide data that had to be clearly grouped, modified, and supplemented so that they could then be used appropriately for analysis. All data came from publicly available servers and additional information was found on the portals of municipalities, the Czech Surveying and Cadastre Office, and data from the Czech Statistical Office. For analyzes where the population was taken into account, Prague (the capital city of the Czech Republic) was not included, as the results could be skewed. Municipalities with a larger population are more concerned with brownfields, and therefore the scope of information is more extensive. They are given greater importance in places where they pose a major problem or obstacle to the development of the area, such as an environmental problem due to their contamination, and municipalities therefore consider it necessary to deal with these sites effectively. In summary, it can be said that the approach of individual municipalities is similar. For now, they are becoming acquainted with the term brownfield and do not attach much importance to it. The analysis of individual territorial analytical data shows that many municipalities do not deal with the issue of brownfields at all. For the purposes of analysis and further use of information, GIS applications are very helpful, which provide more specific, especially topographic, information about localities. However, there are very few municipalities that can financially provide such a service, specifically four out of a total of 38 investigated municipalities with extended powers (Most, Karviná, Ostrava, Ústínad Labem). Table 1shows an excerpt from the authors’ prepared database. Sustainability 2021,13, 1232 5 of 18 Table 1. Output from multicriteria analysis. A: Number of Inhabitants— Year 2001 B: Number of Inhabitants— Year 2017 Difference [A−B] District Cadaster Cadaster Area [m2] Settled Area [m2] Number of Buildings Name Area [ha] Usage Ownership GPS Past Usage 211 2811 2600 Kromˇeˇríž Blišice (Koryˇcany) 3,783,736 63,988 160 Farma Blišice 0.81 partially abandoned private 49◦7024.62” N, 17◦9041.37” E agricultural 4768 4369 −399 Uherské Hradištˇe Bojkovice 18,358,640 455,667 1422 Statek Bojkovice 6.42 partially abandoned private 49◦2030.12” N, 17◦47016.44” E agricultural 6091 5574 −517 Zlín Brumov 21,496,397 290,959 1056 Škola Brumov 0.89 abandoned public 49◦5010.78” N, 18◦1028.91” E education 6091 5574 −517 Zlín Brumov 21,496,397 290,959 1056 Pivovar Brumov 4.96 abandoned private 49◦5026.41” N, 18◦1012.79” E industrial 363 371 8 Kromˇeˇríž Brusné, Slavkov pod Hostýnem 8,170,444 64,974 248 Farma BrusnéSlavkov 3.38 partially abandoned combination 49◦2207.44” N, 17◦40010.14” E agricultural 2448 2445 −3Uherské Hradištˇe Buchlovice 31,961,913 465,202 1438 Farma Buchlovice 6.31 partially abandoned combination 49◦4046.83” N, 17◦20059.19” E agricultural 226 180 −46 Kromˇeˇríž Cetechovice 7,486,341 67,184 168 Farma Cetechovice 1.26 partially abandoned private 49◦10033.81” N, 17◦15050.57” E agricultural 226 180 −46 Kromˇeˇríž Cetechovice 7,486,341 67,184 168 Sýpka Cetechovice 1.3 abandoned private 49◦10026.24” N, 17◦15043.47” E agricultural 316 370 54 ZlínDivnice, Lipová7,235,337 152,095 331 Pr˚umyslový areál Slaviˇcín110 partially abandoned private 49◦604.99” N, 17◦54023.09” E industrial 316 370 54 Zlín Divnice 7,235,337 152,095 331 Vojenskýareál Divnice 7.48 partially abandoned combination 49◦5040.63” N, 17◦54025.76” E military Sustainability 2021,13, 1232 6 of 18 2.3. Data Processing Methods Based on the available data, it is clear that a substantial part of the research methods consists of so-called exploitative (descriptive) statistics. Using these statistics, the obtained data were clarified for their subsequent application in other more sophisticated statistical methods. For the purposes of determining disparities in the territory of small municipalities and cities, the method of field research was essential. An important method for this research is regression analysis, which allows the determination of the dependence between individual (quantitative) variables, independent (explanatory, i.e., cause) variables, and dependent (explained, consequence) variables—in this case the dependence of regeneration time on distance from the village center. Another method used is ANOVA (analysis of variance), to compare several mean values of independent random samples—in this case Population, Distance from the city center, Distance from a major road, Distance from the railway and Distance from the state border. Within the research related to the size of municipalities, multicriteria analysis was also used, one of the most used types of analysis of qualitative and quantitative criteria on a given problem. These statistical methods were also supplemented by the χ2 test of independence in the contingency table, which serves to evaluate the dependence of the obtained results, or by refuting the assumed hypotheses. For the purposes of graphical analysis, MS EXCEL 2016 was used; with the extension of the 3D Maps module, data supplemented with GPS coordinates can be imported into the prepared map data. Unfortunately, data containing information on the location of individual objects are among the basic shortcomings of almost all records of brownfield sites, and for this reason it was necessary to find at least their approximate location in all localities. This process was very lengthy and demanding, and it was often only stated that the site was located in a certain region, and on which street it lay, so it was necessary to use the possibilities of google.maps.com and ortho-photomaps, and to view the site via StreetView to find GPS coordinates. The set of all acquired, modified, and supplemented data was subsequently processed using STATISTICA®and r-studio software tools. 3. Results—Application of Statistical Methods Graphical and statistical analysis of the input data was performed using the application of statistical methods to the created data file. Although the possibilities of using many other statistical methods are unlimited, it is necessary to take into account not only the nature of the available data, but above all what the desired solution is and what is to be explained. 3.1. Graphic Analysis of Input Data Through the Historical Lexicon, which is published in [ 27 ], the analyzed data were also supplemented by data on the population in the respective previous years, i.e., in 1980, 1991, 2001 and 2017. This analysis was performed to evaluate the generally accepted theory, which states that the existence brownfield sites in municipalities is resulting in a rapid decline in population, see in Figure 1. Sustainability 2021,13, 1232 7 of 18 Sustainability 2021, 12, x FOR PEER REVIEW 7 of 19 3. Results—Application of Statistical Methods Graphical and statistical analysis of the input data was performed using the application of statistical methods to the created data file. Although the possibilities of using many other statistical methods are unlimited, it is necessary to take into account not only the nature of the available data, but above all what the desired solution is and what is to be explained. 3.1. Graphic Analysis of Input Data Through the Historical Lexicon, which is published in [27], the analyzed data were also supplemented by data on the population in the respective previous years, i.e., in 1980, 1991, 2001 and 2017. This analysis was performed to evaluate the generally accepted theory, which states that the existence brownfield sites in municipalities is resulting in a rapid decline in population, see in Figure 1. Figure 1. The difference between the increase and decrease in the number of inhabitants in the Czech Republic—own processing according to [28]. Below is a part of the graphical data analysis, prepared on the basis of documents from the portal www.brownfieldy.eu [29], which is managed by CzechInvest. This source is one of the most important, because it includes a database for the entire Czech Republic. It is divided into a public and a non-public section, and it is necessary to log in to the nonpublic part using a username and password. The data are current as of 2017. The data are continuously supplemented and updated. In the modified database, for the purposes of this research, the following indicators were selected for the source [29]: • number of buildings in municipalities in 1980, 1990, 2001, and 2017; • the difference in the number of buildings in municipalities between 1980, 1990, 2001, and 2017; • population in 1980, 1990, 2001 and 2017; • population difference between 1980, 1990, 2001, and 2017; • name of the municipality; • site name; • name of the cadastral area; • area of cadastral territory; • built-up area in the cadastral area; • number of objects located in the cadastral territory; • built-up area of the site; • number of objects in the locality; Figure 1. The difference between the increase and decrease in the number of inhabitants in the Czech Republic—own processing according to [28]. Below is a part of the graphical data analysis, prepared on the basis of documents from the portal www.brownfieldy.eu [29], which is managed by CzechInvest. This source is one of the most important, because it includes a database for the entire Czech Republic. It is divided into a public and a non-public section, and it is necessary to log in to the nonpublic part using a username and password. The data are current as of 2017. The data are continuously supplemented and updated. In the modified database, for the purposes of this research, the following indicators were selected for the source [29]: •number of buildings in municipalities in 1980, 1990, 2001, and 2017; • the difference in the number of buildings in municipalities between 1980, 1990, 2001, and 2017; •population in 1980, 1990, 2001 and 2017; •population difference between 1980, 1990, 2001, and 2017; •name of the municipality; •site name; •name of the cadastral area; •area of cadastral territory; •built-up area in the cadastral area; •number of objects located in the cadastral territory; •built-up area of the site; •number of objects in the locality; •site area; •GPS coordinates; •use of the site; •area/cadastral area; •built-up area/number of buildings; •area of cadastral territory/number of objects; •population/number of buildings. Out of the total number of 450 localities, it was evaluated that 258 localities are located in municipalities with less than 5000 inhabitants, which in percentage terms is 58%. In municipalities with less than 10,000 inhabitants, there are 331 localities, which in percentage terms is 74%. The assumption that most brownfield sites are located in small municipalities has been confirmed and can be seen in Figure 2. Sustainability 2021,13, 1232 8 of 18 Sustainability 2021, 12, x FOR PEER REVIEW 8 of 19 • site area; • GPS coordinates; • use of the site; • area/cadastral area; • built-up area/number of buildings; • area of cadastral territory/number of objects; • population/number of buildings. Out of the total number of 450 localities, it was evaluated that 258 localities are located in municipalities with less than 5000 inhabitants, which in percentage terms is 58%. In municipalities with less than 10,000 inhabitants, there are 331 localities, which in percentage terms is 74%. The assumption that most brownfield sites are located in small municipalities has been confirmed and can be seen in Figure 2. Figure 2. Overview of the number of brownfield sites according to the number of inhabitants in the Czech Republic—own processing according to [29]. Another evaluation concerns the representation of the number of brownfields according to their position in relation to the regions (see Figure 3). Figure 4 shows that the Liberec Region, the South Moravian Region, the Moravian-Silesian Region, the Pardubice Region, and the Usti Region have the largest share of these areas. In terms of small municipalities, the largest share falls on the Liberec region, the Usti nad Labem region and the South Moravian region. Figure 2. Overview of the number of brownfield sites according to the number of inhabitants in the Czech Republic—own processing according to [29]. Another evaluation concerns the representation of the number of brownfields according to their position in relation to the regions (see Figure 3). Figure 4shows that the Liberec Region, the South Moravian Region, the Moravian-Silesian Region, the Pardubice Region, and the Usti Region have the largest share of these areas. In terms of small municipalities, the largest share falls on the Liberec region, the Usti nad Labem region and the South Moravian region. Sustainability 2021, 12, x FOR PEER REVIEW 9 of 19 Figure 3. Graphic analysis of data describing the location of individual brownfield sites in relation to the location of the regions of the Czech Republic—own processing according to [29]. Figure 4. An overview of the number of brownfield sites in individual regions of the Czech Republic—own processing according to [29]. 3.2. Statistical Analysis of Input Data The data file defined in Section 2.2 was suitably supplemented and expanded with other data, e.g., from field surveys. The statistical methods defined in Section 2.3 were then applied to this data set. 3.2.1. Regression Analysis The data used relate only to small municipalities [30]. Their dependence was verified using STATISTICA software and the results are given below. Example: “Dependence of regeneration time with respect to the distance from the village center”. At the beginning of the regression analysis, it is necessary to determine two variables. In this case, this is the length of time for which the building was unused (i.e., the time for regeneration) and the distance of brownfields from the city center or village. 0 10 20 30 40 50 60 70 Number of brownfields Number of brownfields Numbers of brownfields up to 10,000 inhabitans city Figure 3. Graphic analysis of data describing the location of individual brownfield sites in relation to the location of the regions of the Czech Republic—own processing according to [29]. Sustainability 2021,13, 1232 9 of 18 Sustainability 2021, 12, x FOR PEER REVIEW 9 of 19 Figure 3. Graphic analysis of data describing the location of individual brownfield sites in relation to the location of the regions of the Czech Republic—own processing according to [29]. Figure 4. An overview of the number of brownfield sites in individual regions of the Czech Republic—own processing according to [29]. 3.2. Statistical Analysis of Input Data The data file defined in Section 2.2 was suitably supplemented and expanded with other data, e.g., from field surveys. The statistical methods defined in Section 2.3 were then applied to this data set. 3.2.1. Regression Analysis The data used relate only to small municipalities [30]. Their dependence was verified using STATISTICA software and the results are given below. Example: “Dependence of regeneration time with respect to the distance from the village center”. At the beginning of the regression analysis, it is necessary to determine two variables. In this case, this is the length of time for which the building was unused (i.e., the time for regeneration) and the distance of brownfields from the city center or village. 0 10 20 30 40 50 60 70 Number of brownfields Number of brownfields Numbers of brownfields up to 10,000 inhabitans city Figure 4. An overview of the number of brownfield sites in individual regions of the Czech Republic— own processing according to [29]. 3.2. Statistical Analysis of Input Data The data file defined in Section 2.2 was suitably supplemented and expanded with other data, e.g., from field surveys. The statistical methods defined in Section 2.3 were then applied to this data set. 3.2.1. Regression Analysis The data used relate only to small municipalities [ 30 ]. Their dependence was verified using STATISTICA software and the results are given below. Example: “Dependence of regeneration time with respect to the distance from the village center”. At the beginning of the regression analysis, it is necessary to determine two variables. In this case, this is the length of time for which the building was unused (i.e., the time for regeneration) and the distance of brownfields from the city center or village. Using a statistic called a “correlation matrix”, the value of the correlation coefficient was found to be 0.27. We can talk about low dependencies of variables (where the value of the correlation coefficient is greater than 0). The next step is to use “regression analysis” statistics (Figure 5). The most important data includes the value of R 2 , which expresses what proportion of the total variability in the dependent variable was solved by the model. The value of R 2 = 0.7294 can be read from Figure 5. From the values given in Figure 5it is possible to determine the equation of the model: #Regeneration time = 24.28 + 0.0019 * distance from the town center + E Residues are not evenly distributed around the zero mean, which means that the model was not determined correctly. This result could be caused by a low number of observations or other errors. Residual analysis can be performed using a normal p-graph of residues. The course is the boundary, and the points do not lie around the line (see Figure 6 ). Rather, we can say that the values do not come from a normal distribution. Another way to determine the normality of the data is to use a histogram (see Figure 7), where it is clearly visible that the normality of the data has again not been confirmed. Sustainability 2021,13, 1232 16 of 18 of subjectivity in compiling the order of attributes. The real differences between the indicators in large and small municipalities were not proven by any statistical method. Is there a direct link between the success of brownfield revitalization and the size of the municipality in which the site is located? • This hypothesis was monitored using statistical methods, but its validity was not confirmed. 5. Conclusions The research dealt with the area of brownfield revitalization as a broad concept. The introductory part dealt with the position of brownfield sites in spatial planning and urban development both in the Czech Republic and abroad. A large part of the work was devoted to the creation of records (database), which lists the brownfield sites before regeneration, as well as successfully revitalized brownfield sites in the Czech Republic. This inventory was a necessary step before the actual statistical evaluation of the inventory data. The key statistical methods used were regression analysis, ANOVA, χ2 contingency table independence test, and multicriteria analysis. Statistical methods were selected for effective applicability of the data set [35,36]. The volume of defined indicators (disparities) is limited, due to problems with the availability of data files, which do not always contain the necessary data. Publicly available data from the Czech Statistical Office or other sources were presented in such a form and format that for statistical analysis most disparities, which are commonly reported in the literature, were found [ 37 ]. The presented research using the application of statistical methods found that the most significant disparities include the distance of the locality from the state borders, the difference in population between 2001 and 2017, the type of ownership and the distance from a first class road or highway. Their significance was confirmed by several statistical methods simultaneously. The distance from the state borders was confirmed to be statistically the most significant factor in all analyzes. It would therefore be appropriate for border areas to support their development potential (so far there has been no specific support for border municipalities). Small municipalities need more promotion for their successful development so that they are not overshadowed by larger cities. It has also been shown that the type of ownership is statistically significant, which means that municipalities should focus their interest on supporting privately owned sites. Publicly owned buildings are among the less revitalized municipalities that cannot compete with private owners, because they cannot consider profits resulting from new use. The dynamics of brownfield-related data significantly influence the statistical analysis. All used statistical methods brought an interesting view of the observed issues. The generally accepted connections with the existence of brownfield sites have not been directly confirmed, but they have also not been directly refuted. This means that more research is needed for a more detailed analysis of data on a wider range of variables that could lead to clearer results. The main advantage of this analysis is the reference to the possibility of statistical assessment of data related to the revitalization of brownfield sites in the broadest sense. One of the common conclusions of this analysis was, surprisingly, that the regeneration of brownfield sites is significantly dependent on the distance of the site from the state borders. This theory was confirmed in all three important analyzes, which clearly confirms the significance of this variable. The main benefit of this paper is to point out the possibility of solving the problem of brownfield sites and the possibilities of their successful, revitalization, not only in terms of humanities and urban planning, but also with the help of statistical methods. Converting individual attributes into numerical values and weights will allow a wide range of uses. Author Contributions: N.S. and M.T. provided the core idea, collected the data, wrote the manuscript and analyzed the data statistically. F.K. revised and constructively commented on the paper, checked the formal correctness. All authors have read and agreed to the published version of the manuscript. Sustainability 2021,13, 1232 17 of 18 Funding: The work was supported by funds for Conceptual Development of Science, Research and Innovation for 2021 allocated to VSB–Technical University of Ostrava by the Ministry of Education, Youth and Sports of the Czech Republic. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Publicly available datasets were analyzed in this study. This data ´ s links can be found in the references. Conflicts of Interest: The authors declare no conflict of interest. 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