Research of the Disparities in the Revitalization of Brownfields PDF

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 1 Department of Spatial Planning and the Environ...

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 1 Department of Spatial Planning and the Environment, Karvina City Authority, Frystatska 72/1, 733 24 Karvina, Czech Republic; [email protected] 2 Department 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: [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 Citation: Szeligova, N.; Teichmann, affect the success of revitalization. In small municipalities, the issue of brownfields is not emphasized M.; Kuda, F. Research of the but, in terms of maintaining community strength and reducing population turnover, the reuse of Disparities in the Process of brownfields is a crucial theme. Revitalization of Brownfields in Small Towns and Cities. Sustainability 2021, Keywords: brownfield; disparities; indicators; regeneration; land use 13, 1232. https://doi.org/10.3390/ su13031232 Academic Editors: Enzo Martinelli 1. Introduction and Radim Cajka The importance of brownfield regeneration is closely connected with the protection Received: 27 November 2020 Accepted: 21 January 2021 of the agricultural land fund and the open landscape, which is one of the exhaustible Published: 25 January 2021 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 Publisher’s Note: MDPI stays neutral longer used today, abandoned, usually burdened by a certain degree of contamination and with regard to jurisdictional claims in affected by the original purpose of use [1,2]. Most of them are located on very lucrative published maps and institutional affil- lands in the built-up area of towns and villages. They thus become one of the elements iations. 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 Copyright: © 2021 by the authors. phenomena. However, the use of these brownfields can lead to a reduction in the Licensee MDPI, Basel, Switzerland. amount of unproductive dilapidated real estate and at the same time to an influx of new This article is an open access article investors, but above all to the preservation of greenfield sites. distributed under the terms and The term brownfield is not enacted in the Czech legislation, as is the case in many other conditions of the Creative Commons countries of the European Union. In the Czech Republic, a definition from the National Attribution (CC BY) license (https:// Brownfield Regeneration Strategy can be given: “A brownfield is a property (land, building, creativecommons.org/licenses/by/ area) that is underused, neglected and can even be contaminated. It arises as a remnant of 4.0/). 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.”. 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 in- clude old industrial bodies of water, abandoned mines, former railway stations, abandoned gas stations and former treatment plants”. 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 build- ings, 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 orga- nizations 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 organi- zations 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. 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 re- gional 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.)”. 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 ever- changing situation; decision-making: it is easier to make a decision based on the information found. 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 ap- proach 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. 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 moni- tored 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 revitaliza- tion process; Sustainability 2021, 13, 1232 4 of 18 proposal of a suitable approach to the decision-making process in the field of brown- field 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 ap- proximately 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 mon- itored. 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 decision- making 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 revitaliza- tion 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, Ústí nad Labem). Table 1 shows an excerpt from the authors’ prepared database. Sustainability 2021, 13, 1232 5 of 18 Table 1. Output from multicriteria analysis. A: Number of B: Number of Difference Cadaster Settled Number of Inhabitants— Inhabitants— District Cadaster Name Area [ha] Usage Ownership GPS Past Usage [A−B] Area [m2 ] Area [m2 ] Buildings Year 2001 Year 2017 Blišice partially 49◦ 70 24.62” N, 211 2811 2600 Kroměříž (Ko- 3,783,736 63,988 160 Farma Blišice 0.81 aban- private agricultural 17◦ 90 41.37” E ryčany) doned partially Uherské Statek 49◦ 20 30.12” N, 4768 4369 −399 Bojkovice 18,358,640 455,667 1422 6.42 aban- private agricultural Hradiště Bojkovice 17◦ 470 16.44” E doned 49◦ 50 10.78” N, 6091 5574 −517 Zlín Brumov 21,496,397 290,959 1056 Škola Brumov 0.89 abandoned public education 18◦ 10 28.91” E Pivovar 49◦ 50 26.41” N, 6091 5574 −517 Zlín Brumov 21,496,397 290,959 1056 4.96 abandoned private industrial Brumov 18◦ 10 12.79” E Brusné, partially Slavkov Farma Brusné- 49◦ 220 7.44” N, 363 371 8 Kroměříž 8,170,444 64,974 248 3.38 aban- combination agricultural pod Slavkov 17◦ 400 10.14” E doned Hostýnem partially Uherské Farma 49◦ 40 46.83” N, 2448 2445 −3 Buchlovice 31,961,913 465,202 1438 6.31 aban- combination agricultural Hradiště Buchlovice 17◦ 200 59.19” E doned partially Farma 49◦ 100 33.81” N, 226 180 −46 Kroměříž Cetechovice 7,486,341 67,184 168 1.26 aban- private agricultural Cetechovice 17◦ 150 50.57” E doned Sýpka 49◦ 100 26.24” N, 226 180 −46 Kroměříž Cetechovice 7,486,341 67,184 168 1.3 abandoned private agricultural Cetechovice 17◦ 150 43.47” E partially Divnice, Průmyslový 49◦ 60 4.99” N, 316 370 54 Zlín 7,235,337 152,095 331 110 aban- private industrial Lipová areál Slavičín 17◦ 540 23.09” E doned partially Vojenský areál 49◦ 50 40.63” N, 316 370 54 Zlín Divnice 7,235,337 152,095 331 7.48 aban- combination military Divnice 17◦ 540 25.76” E doned 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 de- termination 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 inde- pendent 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 appli- cation 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 , 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.  3.1. Graphic Analysis of Input Data Through the Historical Lexicon, which is published in , 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 the- Sustainability 2021, 13, 1232 7 of 18 ory, which states that the existence brownfield sites in municipalities is resulting in a rapid decline in population, see in Figure 1. Thedifference Figure1.1.The Figure differencebetween betweenthe theincrease increase and and decrease decrease in in the the number number of ofinhabitants inhabitantsin inthe theCzech Republic—own processing according to. Czech Republic—own processing according to. Belowisisaapart Below partofofthe thegraphical graphicaldata dataanalysis, analysis,prepared preparedon onthe thebasis basisof ofdocuments documents from the portal www.brownfieldy.eu , which is managed by CzechInvest. This source from the portal www.brownfieldy.eu , which is managed by CzechInvest. This source is one of the most important, because it includes a database for the entire Czech Republic. 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 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 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 are continuously supplemented and updated. In the modified database, for the purposes of this research, the following indicators were selected for the source : of this research, the following indicators were selected for the source : number of buildings in municipalities in 1980, 1990, 2001, and 2017; number of buildings in municipalities in 1980, 1990, 2001, and 2017; the difference in the number of buildings in municipalities between 1980, 1990, 2001, the difference in the number of buildings in municipalities between 1980, 1990, 2001, and 2017; and 2017; population in 1980, 1990, 2001 and 2017; population in 1980, 1990, 2001 and 2017; population difference between 1980, 1990, 2001, and 2017; population name of the difference between 1980, 1990, 2001, and 2017; municipality; name of the site name; municipality; sitenamename; of the cadastral area; name of cadastral area of the cadastral area; territory; area of cadastral territory; built-up area in the cadastral area; built-up numberarea in the located of objects cadastralin area; the cadastral territory; number of objects located built-up area of the site; in the cadastral territory; built-up numberarea of the in of objects site; the locality; 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.  population/number of buildings. Out of the total number of 450 localities, it was evaluated that 258 localities are lo- cated 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 per- Sustainability 2021, 13, 1232 8 of 18 centage terms is 74%. The assumption that most brownfield sites are located in small mu- nicipalities 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. Figure 2. Overview of the number of brownfield sites according to the number of inhabitants in the Czech Republic—own Another processingthe evaluation concerns according to. of the number of brownfields accord- representation ing to their position in relation to the regions (see Figure 3). Figure 4 shows that the Liberec Another Region, evaluation the South Moravian concerns Region, the representation ofRegion, the Moravian-Silesian the number of brownfields the Pardubice Region, ac- cording to their and the Usti position Region in relation have the to the largest share ofregions (see In these areas. Figure terms3). of Figure 4 shows that the small municipalities, Sustainability 2021, 12, x FOR PEERLiberec the Region, largest REVIEW sharethe South falls Moravian on the Liberec Region, theUsti region, the Moravian-Silesian nad Labem region Region, and the the Pardubice South 9 of 19 Moravian region. Region, and the Usti Region have the largest share of these areas. In terms of small mu- nicipalities, the largest share falls on the Liberec region, the Usti nad Labem region and the South Moravian region. Figure Figure 3. 3. Graphic Graphic analysis analysis ofof data data describingthe describing the locationofof location individualbrownfield individual brownfieldsites sitesininrelation relation toto the the location location ofof the the regions of the Czech Republic—own processing according to. regions of the Czech Republic—own processing according to. 70 Number of brownfields 60 50 40 30 20 10 0 Sustainability 2021, 13, 1232 9 of 18 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. 70 Number of brownfields 60 50 40 30 20 10 0 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— Figure 4. An overview of the number of brownfield sites in individual regions of the Czech Re- own processing according to. public—own processing according to. 3.2. Statistical Analysis of Input Data 3.2. Statistical The data Analysis of Input file defined Data 2.2 was suitably supplemented and expanded with in Section otherThe data data, filefrom e.g., defined field in SectionThe surveys. 2.2 statistical was suitably supplemented methods defined in and expanded Section 2.3 werewiththen other data, applied to e.g., fromset. this data field surveys. The statistical methods defined in Section 2.3 were then applied to this data set. 3.2.1. Regression Analysis 3.2.1. Regression The data used Analysis relate only to small municipalities. Their dependence was verified using STATISTICA The data used relate software only and the results to small are given. municipalities below. Their dependence was verified using Example: STATISTICA “Dependence software and of the regeneration results aretime givenwith respect to the distance from the below. village center”.“Dependence Example: At the beginning of the regression of regeneration time analysis, it is necessary with respect to determine to the distance from the two variables. In this case, this is the length of time for which the building village center”. At the beginning of the regression analysis, it is necessary to determine was unused (i.e., the time for regeneration) and the distance of brownfields from the city two variables. In this case, this is the length of time for which the building was unused center or village. Using (i.e., the timea for statistic called a “correlation regeneration) matrix”, and the distance of the value of the brownfields correlation from the city coefficient center or was found to be 0.27. We can talk about low dependencies of variables (where the value of village. 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 R2 , which expresses what proportion of the total variability in the dependent variable was solved by the model. The value of R2 = 0.7294 can be read from Figure 5. From the values given in Figure 5 it 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.  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 Sustainability 2021, 13, 1232 data includes the value of R2, which expresses what proportion of the total variability 10 of 18 in the dependent variable was solved by the model. The value of R2 = 0.7294 can be read from Figure 5. Sustainability 2021, 12, x FOR PEER REVIEW 11 of 19 Figure Figure 5. 5. TheThe resulting resulting linear linear regression regression table table for Example. for the the Example. From the values given in Figure 5 it is possible to determine the equation of the model: o 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 ob- servations 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. Figure 6. Residual analysis Figure 6. foranalysis Residual the Example. for the Example. Sustainability 2021, 13, 1232 11 of 18 Figure 6. Residual analysis for the Example. Figure7.7.Histogram Figure Histogramfor forthe theExample. Example. 3.2.2. 3.2.2.Anova Anova Analysis Analysisofofvariance variancewas wasused usedfor fordata databased basedon on..The Thesignificance significanceofofthe thevariables variables was observed: Population, Distance from the city center, Distance from was observed: Population, Distance from the city center, Distance from a major road, a major road, Dis- Distance Sustainability 2021, 12, x FOR PEER REVIEW from the railway, and Distance from the state tance from the railway, and Distance from the state border. border. 12 of 19 Using Usingaagraph graphandandthe theShapiro-Wilk Shapiro-Wilktest test(see (seeFigure Figure8), 8),ititwas wasfound foundthat thatthe thevariables variables Distance Distancefrom fromthethecenter centerand andDistance Distancefrom fromthe therailway railwaydiddidnot notmeet meetthe theassumption assumptionofof normality, normality,and andtherefore thereforethe theKruskal-Wallis Kruskal-Wallistest testwas wasused, used,forforwhich whichdata datanormality normalitydoes does not matter (see Figure 9). not matter (see Figure 9). Figure 8. Scatter plots, Shapiro-Wilk test for the Example. Figure 8. Scatter plots, Shapiro-Wilk test for the Example. Sustainability 2021, 13, 1232 12 of 18 Figure 8. Scatter plots, Shapiro-Wilk test for the Example. Figure9.9.Kruskal-Wallis Figure Kruskal-Wallistest testfor forthe theExample. Example. The Kruskal-Wallis The Kruskal-Wallistest testevaluated evaluatedthat thatthe thestatistically statisticallymost mostsignificant significantfactor factorisisthe the Distance from Distance from the the state stateborder, border,and andthe theleast leaststatistically statisticallysignificant significantisisthe theDistance Distancefrom from theroad. the road. This Sustainability 2021, 12, x FOR PEER REVIEW This theory theory isis also alsoconfirmed confirmedby bythe thebox boxgraph graph(see (seeFigure Figure10), 10),from fromwhich which the 13 ofthe 19 importance importanceof ofthe thedistance distanceof ofthe thelocality localityfrom fromthe thestate stateborders bordersclearly clearlyfollows. follows. Figure10. Figure 10.Box Box chart chart for for the the Example. Example. 3.2.3. 3.2.3.Multicriteria Multicriteria Analysis Analysis Another Anotherstatistical statisticalapplication applicationwas wasmulticriteria multicriteriaanalysis. analysis.AsAsananalternative, alternative,the thesizes of sizes municipalities were determined according to the given range of inhabitants. of municipalities were determined according to the given range of inhabitants. The ana- The analytical criteria were compiled lytical criteria based on were compiled the analysis based of the data. on the analysis of theThe order data. Themethod was used order method wasto calculate the weights. used to calculate the A value ofA1 value weights. was setoffor the most 1 was sensitive set for the mostcriteria and acriteria sensitive value of 5 for and a the least value of sensitive criteria. 5 for the least The resulting sensitive dataresulting criteria. The from thedata multicriteria analysis are recorded from the multicriteria analysis in Table are 2, from recorded in which Table 2,the following from conclusions which the followingwere drawn: were drawn: conclusions municipalities municipalitieswithwithless lessthan than3000 3000inhabitants inhabitantsarearethe most the mostsensitive sensitiveand andit is it necessary is neces- to paytomore sary attention pay more to them attention in theinmonitored to them criteria; the monitored criteria; in incontrast, contrast,inin municipalities municipalities between 2000 and 5000 inhabitants,inhabitants, this this sensitivity sensitivitywas was evaluated evaluatedas asthe the lowest. lowest. Table 2. Output from multicriteria analysis. Result (Product of Weights) Sequence 2.87466 × 10−13 1 Sustainability 2021, 13, 1232 13 of 18 Table 2. Output from multicriteria analysis. Result (Product of Weights) Sequence 2.87466 × 10−13 1 1.84188 × 10−10 5 9.01057 × 10−11 4 3.18294 × 10−12 2 1.95001 × 10−11 3 3.2.4. χ2 Test of Independence in the Contingency Table The last selected statistic was the χ2 test of independence in the contingency table, which is used to evaluate the dependence of two variables, the frequencies of which are written into the so-called contingency table. EXCEL software (MS Office 2007) was used for this analysis. Data were evaluated that corresponded to those brownfield sites that had already been successfully regenerated (source: own processing according to [30,31]). The dependence or independence of the time when the premises/buildings were without use (i.e., the period from the end of operation to the year of commissioning) was analyzed. Example: In the following example “Test for municipalities up to 5000 inhabitants”, hypotheses are tested, where it is determined that: Hypothesis 1 (H1). The regeneration time does not depend on the type of ownership (Table 3). and in contrast: Hypothesis 2 (H2). The regeneration time depends on the type of ownership (Table 4). Table 3. Actual frequencies for the example. Type of Ownership Up to 20 Years 20–40 Years Over 40 Years Sum of Frequencies ni Private 9 10 2 21 Public 3 2 0 5 Ecclesiastical 0 0 2 2 Sums of frequencies nj 12 12 4 28 Table 4. Expected frequencies for the Example. Type of Ownership Up to 20 Years 20–40 Years Over 40 Years Sum of Frequencies ni Private 9 9 3 21 Public 2.14 2.14 0.71 5 Ecclesiastical 0.86 0.86 0.29 2 Sums of frequencies nj 12 12 4 28 Test criterion:
2 r s nij − n0ij G = ∑∑ n0ij = 13.312 (1) i =1 j =1 Critical value: X (1 − α); d f = 9.488 (2) At the significance level of 5%, we reject the null hypothesis H1 → There is a certain de- pendence between the regeneration period and the type of ownership, i.e., H2: Regeneration time depends on the type of ownership. As part of the research, several tests were performed, but for the sake of clarity only one sample example of the calculation was given—“Test for municipalities with less than 5000 inhabitants”. Based on the performed tests and their evaluation, the key results for the research are the listing of variables that proved to be dependent on the test. These variables include: Sustainability 2021, 13, 1232 14 of 18 the time from the end of the operation to the start of operation depends on the type of ownership of the site (data source ). Only municipalities with a population of up to 5000 were included; the time from the end of the operation to the start of the operation depends on the distance of the site from the state borders. (data source ). All municipalities were included; the distance from a first class road or motorway depends on the difference in the number of inhabitants in the period between 2001–2017 (data sources own processing and ). Other tests showed the statistical independence of the monitored variables. The result of this analysis could be influenced by the choice of data source, an inappropriately determined null hypothesis, or insufficiently large data sets. 3.3. Final Evaluation of Statistical Methods 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 for further research, an even more detailed analysis of the data with a wider range of variables is needed, which 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. A major obstacle in the statistical assessment of brownfield sites is the mobility and instability of the monitored data, which means that data are presented according to the subjective evaluation of the creator of the source, which can lead to significant data bias and thus major shortcomings in the analysis. Therefore, it is necessary to present and consider these statistical analyzes as indicative and as a tool for database processors, records, owners of brownfields, and municipalities, which allows clear targeting of the right variables. However, the use of a simple independence test can be very easy to apply to the selected territory; there are also no demands on the software equipment, and therefore it is more than suitable for selecting suitable indicators, disparities, or other variables. At the same time, the graphical expression using tables, which naturally follows from the test in a clear way, describes the basic statistics, i.e., the frequencies of individual variables. 4. Research Results and Discussion The main goal of the research was defined as: “The aim of the research is to determine the disparities affecting the success of brownfield revitalization, especially in small munic- ipalities and cities, and then assess the data using appropriate statistical methods.” The character and structure of the data was determined by analyzing publicly available data on brownfield sites in the regions, municipalities, and cities of the Czech Republic. Prior to the actual application of statistical methods, it was necessary to ensure that the data met the basic requirements for the use of statistical methods. If the data did not correspond to the conditions, statistical methods could be used, but the conclusions from the statistical methods can only be considered as indicative. The conclusions of the statistical methods led to the establishment of a list of important indicators that were monitored and evaluated. They are: location; distance from state borders; distance from first class road, motorway; distance from the railway; population; the difference in the number of inhabitants in individual years; Sustainability 2021, 13, 1232 15 of 18 listed building; community strength; territorial pollution—contamination; land ownership; the time from the end of operation to the resumption of operation; sources of funding; number of objects in the monitored locality; site area; number of site owners; original and new use. The analyzes show the dynamic character of individual monitored disparities and a certain degree of subjectivity in determining values [32,33]. Therefore, it is not possible to unify the statement about the meaning of individual attributes in general. However, we cannot talk about the limited importance of these analyzes, because they can be used to obtain more specific information about the importance and significance of disparities. The original agricultural, military, food, and textile orientation of rural areas was replaced by the demand for services of various kinds (accommodation, sports and recreation, tourism, etc.). The most common use for abandoned buildings is the establishment of a community center. Answering Research Questions What kind of financing prevails in the process of brownfield revitalization in small municipalities? Small municipalities in the Czech Republic most often use financial resources from various subsidies for development and remediation projects. This statement is condi- tioned by a certain weight of subjectivity, because information on the type of financing in the Czech Republic is very limited and often unavailable. An overview of subsidy programs suitable for brownfield regeneration is given. In recent years, rural areas and small towns have been supported in solving problems with neglected and abandoned areas. From foreign sources [6,35] it was found that in the territory of small municipalities, the most effective financing was with private funds, and only rarely is there a partnership between the private and public sector (i.e., financing through subsidies and bank loans). Which attributes (disparities) most influence investors in the investment decision- making process? It was not possible to determine the overview of disparities influencing investors, using the selected statistical methods. Relevant data and necessary information on the type and kind of investors, or on cooperation between the private and public sectors, were not available. The original goal of the work was to trace the properties of this attribute, but this research question could not be answered. Is the process of brownfield revitalization more demanding in the territory of small municipalities? If so, can this be expressed through disparities? In the territory of small municipalities, the process is more 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. Are there differences in indicators (disparities) in the process of brownfield revitaliza- tion in large and small municipalities? All indicators that have been analyzed to some extent affect the revitalization process in all municipalities. What their influence is and the difference between them can be described and evaluated using multicriteria analysis. The most understan