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Article

Detecting Serbia’s Settlement Patterns: A Fuzzy Logic-Based Approach to Rural–Urban Area Delimitation for Spatial Planning

by
Aleksandra Gajić Protić
1,
Nikola Krunić
1 and
Branko Protić
2,*
1
Institute of Architecture and Urban & Spatial Planning of Serbia, Bulevar kralja Aleksandra 73/II, 11000 Belgrade, Serbia
2
Department of Spatial Planning, Faculty of Geography, University of Belgrade, Studentski trg 3/III, 11000 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Land 2024, 13(12), 1981; https://doi.org/10.3390/land13121981
Submission received: 11 October 2024 / Revised: 14 November 2024 / Accepted: 19 November 2024 / Published: 21 November 2024
(This article belongs to the Special Issue Recent Progress in Spatial Modeling in Urban Land Dynamics and Planning)
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Abstract

:
Over the past decades, numerous studies have attempted to capture the heterogeneity of rural areas from different perspectives. Meanwhile, rural areas have undergone various changes, primarily due to urban pressure. Rejecting a dichotomous approach, the majority of studies focus exclusively on the analysis of either rural or urban areas, attempting to demonstrate their diversity and propose a method for their delimitation. This study maps rural and urban areas in Serbia. We present an approach for rural–urban area delimitation that integrates a comprehensive, multidimensional approach based on fuzzy logic. Conducted on a settlement level, this applied approach highly recognizes different settlement types ranging from rural to urban. Based on selected variables and employing a fuzzy inference system, we extract different distinctive settlement features varying from urban (0) to rural (1). The results demonstrate the nuanced classification of settlements varying from completely rural to urban. In addition, such an approach detects settlements that have functional significance in the settlement system. Therefore, it is possible to identify different forms of rural and urban areas (from the rural periphery to metropolitan areas). This method offers a framework that could be used in urban and spatial research and planning and defining rural/urban development strategies and policies. This study provides valuable insights into prioritized areas for planning strategies and support toward mitigating village loss and improving urban and rural sustainability. Our findings can contribute to future functional settlement zoning in Serbia, or the monitoring of planning decisions on different territorial levels.

1. Introduction

Over the past few decades, rural areas have undergone significant transformations, due to demographic dynamics, changes in settlement intensity, and economic specialization [1,2,3,4,5,6,7]. There are almost no rural areas in Europe that are not dependent on cities, and the influence of urban centers is present everywhere, but to different degrees [8]. This creates a kind of territorial continuum, blending urban and rural characteristics and encompassing various landscape types, including agricultural spaces, as well as consolidated and dispersed built-up urban areas [1,7,9,10,11]. Typical rural areas are characterized by a low population density, a low amount of built-up areas, a large presence of open natural spaces, etc. [12]. Due to rapid urbanization, it is becoming more difficult to determine urban and rural areas. Monitoring urbanization processes is crucial because they frequently involve significant changes in landscape structure and diverse socio-economic transformations [13]. However, identifying a suitable nuanced gradient definition for a specific research purpose can be challenging [12]. In urban and spatial planning, it is a special challenge to determine urban and rural areas, especially when it comes to areas with mixed land use patterns, i.e., peri-urban areas or areas in the vicinity of metropolises. A special planning approach requires the detection of small towns or villages that can play an integrative role in space and that require different planning measures than developed areas or completely peripheral areas. Although numerous researchers have long agreed that the urban–rural space is nuanced [14] and requires a complex planning approach, many studies suggest that the identification of rural and urban areas is necessary primarily for practical reasons, as many development policies base their decisions on such spatial divisions [15]. Cattivelli emphasizes the need for delimitation that is locally relevant and internationally comparable contemporarily [16]. This further imposes numerous quantitative and qualitative criteria to determine where the city ends and the village begins [7,17]. Many studies focus just on rural areas, trying to capture their diversity, leaving urban areas behind and vice versa.
Reig Martínez et al. emphasize that it is not possible to form a classification of rural areas without considering urban areas, as the territory must be viewed as a whole, regardless of whether it is examined administratively or hierarchically. Otherwise, the classification lacks consistency [18]. It is crucial to consider the heterogeneity of rural areas, where peri-urban areas coexist, as it dictates the approach and conceptualization of development policies [19].
In this paper, we present a fuzzy logic-based approach for defining rural and urban areas that integrates the multidimensional aspect of settlements in Serbia important for sustainable spatial development. The approach proposed in this research captures more settlements’ complexity and better reflects the unique combination of settlements’ characteristics found in landscapes on the local level.
The presented results could be used to facilitate the design of urban and spatial plans, policies and program management, and the conservation of rural and urban settlements in Serbia.

2. Literature Review

Rural Typologies

In the academic literature, the inceptions of rural typology development are often associated with the “rurality index” designed by Cloke. Cloke [20] formulated the “rurality index” based on statistical indicators and applied it at the local administrative level. The rurality index stimulated a series of studies in different national contexts and was applied multiple times in England and Wales [21,22], with subsequent modifications in Spain [23,24], the Czech Republic [25], Turkey [26], Slovakia [27], and Poland [28].
Efforts in the classification and mapping of rural and urban areas have been further intensified with the political–economic shift in defining rurality/urbanity [19,29]. Woods and Heley identify the development of spatial planning and the promotion of a territorial approach in development policies as a response to the marginalization of rural areas as key drivers for creating revised and sophisticated typologies of rural and urban areas [29].
Internationally, the most widely accepted methodology is undoubtedly the one proposed by the OECD [30,31], later modified by Eurostat [32,33].
Although empirically grounded, this approach had significant theoretical implications as a successful attempt to formulate a simple classification of rural areas at the international level. However, due to its oversimplification, it has been sharply criticized for overlooking the heterogeneity of rural areas [16,19].
Regardless of officially accepted definitions, many studies, emphasizing heterogeneity, have promoted complex models of rural or rural–urban classification based on numerous socio-economic indicators [34,35,36,37,38,39], combined with high-resolution geospatial data [18,40,41,42,43,44]. These studies demonstrate a wide range of methods that can be applied at various territorial levels.
In summary, definitions of rurality/urbanity have transformed from dichotomous models of urban and non-urban areas to highly complex models incorporating highly detailed data and modern techniques which encourage researchers and policymakers in various fields to consider their application in decision-making processes ranging from urban planning to health policy to education and agricultural policy. While the conceptualization of rurality/urbanity since the 1970s has not fundamentally changed, a significant shift has occurred in terms of the techniques used and the availability of data, with a greater emphasis on planning and formulating frameworks for development policies [44].

3. Fuzzy Logic in Rural–Urban Research

Fuzzy logic is part of the fuzzy set theory formulated and widely promoted by Lotfi Zadeh [45]. Fuzzy logic is a set of mathematical principles of knowledge representation based on degrees of membership and the degrees of truth of statements. Unlike crisp values in binary, i.e., Boolean logic where the degree of truth of statements can only be 0 or 1, fuzzy logic uses a continuum of logical values between 0 (completely false) and 1 (completely true). In this way, statements can be partially true and partially false at the same time [46,47]. Fuzzy sets and fuzzy logic represent a basic concept when describing problems that contain approximate quantities, which is close to the human (expert) way of thinking [48].
In spatial research, the application of fuzzy logic provides the opportunity to classify an area/settlement or region not strictly as urban or rural but to determine the degree of membership in both categories. Models based on fuzzy logic are referred to as fuzzy inference systems (FIS) [49]. FIS are rule-based systems that allow for the construction of structures used to generate responses (outputs, classifications) to specific stimulations (inputs or variables) based on stored knowledge on how the responses and stimulations are related [50].
In general, the FIS as a decision-making mechanism is much closer to real expert knowledge. In recent decades, the capabilities of Geographic Information Systems (GIS) have allowed numerous studies to rely on fuzzy logic for the precise selection of optimal locations and evaluation of alternative solutions [51,52,53,54,55]. Although numerous contemporary studies indicate that the boundaries between urban and rural areas are blurred [6,56,57], the use of fuzzy logic to address this issue is still in the initial phase. Pászto et al. [58] highlighted the benefits of using fuzzy sets in combination with basic arithmetic operations to mitigate the sharp boundaries between urban and rural municipalities in the Czech Republic. In the same study, a comparison was made between the results obtained with a model of delimitation of urban and rural areas that included five indicators representing input variables (population size, population density, the ratio of dwellings for family houses and permanently occupied dwellings, the percentage of built-up dwellings in the period 1993–2006, and the population change index from 1993 to 2006). The comparison showed that the output of the model with five indicators resulted in a broader transitional area between rural and urban municipalities, indicating a wider range of values [58]. The same authors created an FIS with seven input variables; each variable was assigned certain weighting values, and then 254 rules describing rural and urban areas were created [43]. This model provided far more sophisticated results of the rural–urban mosaic compared to previous studies.
Pagliacci [59] offered a new perception of the European urban–rural typology based on fuzzy logic. He constructed a composite indicator of rurality, the Fuzzy Rurality Indicator (FRI). Through selected indicators, the FRI covers the role of agriculture, population density, and land use characteristics, with output values ranging from 0 to 1. The research covered 27 EU countries at the NUTS 3 level. The author emphasized that the results align with Eurostat’s typology regarding predominantly urban and predominantly rural regions. However, the spatial distribution of the FRI provides more information about the urban–rural continuum and the level of rurality compared to Eurostat’s transitional areas, which are homogenous [59].

4. Study Area

Serbia is characterized by small settlements, an asymmetric urban system, and an uneven spatial distribution of the population [60]. Our study area covered 4604 settlements. The use of settlements as a spatial unit facilitates analysis and the decision-making process in urban and spatial planning. The settlements vary considerably in demographics, socio-economic characteristics, land cover, and accessibility. As much as 80.8% of the total number of settlements have fewer than a thousand inhabitants, with 16.2% of the total population residing in these small settlements. On the other hand, only the city of Belgrade exhibits a similar population size [19]. The Republic of Serbia lacks an official definition of rural areas, complicating the research of rural and urban areas and the understanding of their interdependence, particularly evident in peri-urban zones of major cities [61]. In urban settlements, the population size ranges from 106 in Kuršumlijska Banja to 1.2 million in Belgrade. In the network of urban settlements, 16 settlements have fewer than 2000 inhabitants, while other settlements with over 10,000 inhabitants exist in the vicinity of Belgrade and Novi Sad. Settlements that are not legally designated as urban are considered rural (Figure 1) without a deeper understanding of the settlements and their functions and structure.

Rural–Urban Research in Serbia

Contemporary research in Serbia encompasses complex typologies of rural areas, taking into account numerous different indicators. Bogdanov et al. (2008) propose a classification model for rural municipalities in Serbia based on forty-one indicators, utilizing principal component analysis and cluster analysis [62]. Martinović and Ratkaj (2015), relying on sixteen indicators, suggest a framework for forming a quantitative typology of rural municipalities in Serbia, highlighting four main components of Serbia’s rural space (demographic potential, economic potential, agricultural potential, and urban influence) [63]. Pantić (2016) emphasizes the significance of a trichotomous settlement typology based on thirty-two indicators and cluster analysis, specifically identifying transitional settlement types between rural and urban [64]. Drobnjaković (2016) employs settlement typology as a method for delineating developmental nuclei within the rural space of Central Serbia [65]. Gajić et al. (2018, 2021) define types of rural and urban areas in Serbia using the methods of principal component analysis and cluster analysis, considering fifteen different spatial and socio-economic indicators, and proposing possible frameworks for their application in spatial planning [19,61]. The mentioned typologies indicate the heterogeneity of rural areas in Serbia and indirectly point to the lack of an adequate methodology for their delineation.

5. Methodology

The research methodology followed several successive steps (Figure 2).
The initial analysis included around 20 different indicators tested in previous research [19,61,66]; the correlation matrix was formed by eliminating indicators with a high correlation level. This approach avoided the overlapping of influences from individual variables, and the research focus was shifted towards forming a smaller set of indicators that have the most impact on the delineation of rural and urban areas. The final set of indicators was reduced to seven input indicators (variables) that are most often used in the interpretation of rural and urban areas and that can be relatively easily monitored and adjusted in accordance with the characteristics of a certain area (Table 1). To determine the significance of each variable, the principal component analysis—PCA—was performed, which sorts the input variables (components) according to their participation in the total variance of all analyzed variables.
The second step involved applying fuzzy logic to determine the degree of membership of settlements to rural or urban areas. This step required establishing an FIS, which involved determining threshold values and their weighting coefficients and defining rules that enable the decision-making system.
In addition to determining the threshold for each input variable, it was necessary to define the weighting values (weights) taken into account when establishing the base rules. Individual weight values for the first four indicators were determined by examining different combinations of weight values changed by 0.05, ensuring that the sum of all weights was equal to 1 and the values were proportional to their significance. The final coefficients adopted were those that, based on visual validation, provided the most adequate results. These values were further verified and confirmed by spatial planning experts (Table 2).
In forming the FIS, the first step was to perform a fuzzification process. In this way, the numerical values of input variables were transformed into fuzzy numbers with values between 0 and 1 in accordance with the selected membership function. A linear trapezoidal function was chosen for the fuzzification of input variables. If the fuzzified value of a variable for a particular settlement was greater or less than the set threshold, the settlement would be assigned a complete affiliation to urban or rural areas. This process was performed for each input variable [43,78].
In the next step, it was necessary to form a rule base as a replacement for an expert way of thinking. Due to there being seven input indicators, a combination of 254 rules was defined [43,78]. The rule base in the FIS allows for the transformation of fuzzified values of input variables into the output space. For each rule, two outputs (0 or 1) were defined. Each rule was assigned an output based on the sum of the given inputs. Two rules contained a clear decision on the membership to rural or urban areas. A triangular membership function was applied to the output results, where the output for rural and urban areas was modeled between the values 0 and 1. The remaining rules had outputs with reduced validity. The output result is a file where each settlement is assigned a value between 0 and 1, where OUTPUT = 1 indicates a complete affiliation (equal to 1) to rural space, and OUTPUT = 0 indicates an affiliation to urban areas.
Therefore, using the fuzzy inference system algorithm, input variables were transformed based on rules stored in the rule base, and the output was defined as a fuzzy set. After that, the output fuzzy set was defuzzified based on the selected membership function and converted into a clear value for each settlement. The Center of Gravity method, supporting the Mamdani FIS, was used for defuzzification.

6. Results

The results are presented in Figure 3. This figure confirms the heterogeneity in terms of the physical–geographical characteristics, demographic characteristics, and socio-economic features of Serbia’s settlements. Based on the figure, it is possible to determine which settlements have a rural or urban character by applying an FIS. Each settlement is assigned a unique output value (ranging from 0 to 1), representing the degree of membership to rural/urban areas. Unfortunately, a single figure cannot represent a specific value for each of the 4604 settlements. However, for simplicity, ten categories of settlements were marked, even though it is possible to distinguish specific classes of rurality (Table 3).
The role and significance of settlements in the spatial–functional organization of Serbia can be determined based on the degree of membership to rural or urban areas. The obtained results reveal the following trends in the structure and spatial distribution of rural and urban areas in Serbia:
  • The settlements with the highest degree of membership to rural areas (values ranging from 0.9 to 1) are located in border regions, predominantly mountainous areas. Their development is largely constrained by natural conditions. These include the mountainous areas of southwestern, southern, and eastern Serbia, and to a lesser extent, western Serbia. This category encompasses 29% of all analyzed settlements with a total population of 129,563. The average population size of settlements in this group is 98 residents, making the possibility of revitalizing these settlements quite unrealistic.
  • Settlements with a slightly lower degree of membership to rural areas have output values ranging from 0.8 to 0.9. The settlements in this category have a population of 271,801, accounting for 4% of Serbia’s total population. This group of settlements, together with the previously described category, could be characterized as the rural periphery, although the variable values are more favorable. In the mountainous areas of southwestern, eastern, western, and southern Serbia, these clusters of settlements are adjacent to the previously described areas, collectively forming zones of rural periphery.
  • Clusters of settlements with a rural membership degree ranging from 0.7 to 0.8 exhibit more favorable average values in the observed variables. Although these settlements are not demographically vital, they can conditionally be characterized as “sustainable.” This category of settlements is the most numerous, covering over a quarter (29%) of the observed part of the national territory, comprising 31% of the analyzed settlements with a population of 822,137, or 11% of Serbia’s total population. These settlements display a variety of socio-economic, functional, and morphological characteristics and are unevenly distributed across different landscapes (found in mountainous, valley, and plain areas, as well as near rivers, roads, etc.); thus, a generalized characterization would be inappropriate.
  • Settlements with values ranging from 0.6 to 0.7 comprise approximately 17% of the analyzed settlements, with a population of 788,040 or 11% of Serbia’s total population. These settlements, due to specialization or specific functions, have a more developed secondary or tertiary sector of activity, giving them certain comparative advantages or a higher level of development compared to previously described groups. The spatial distribution of these settlements reveals that they are primarily located near urban centers and along major transportation routes, characterized by established daily commuting patterns and greater economic diversification. These settlements follow urbanization trends, and their development and dynamics are directly linked to urban centers in both productive and socio-economic terms.
  • Settlements with lower rurality levels than the previously described group have values ranging between 0.5 and 0.6, with a population of 241,813 (3% of Serbia’s total population). This group includes only 3% of the analyzed settlements. In terms of rurality, these areas can be described as less urbanized settlements that function as micro-development centers, integrating their immediate rural surroundings. As such, they may serve as development hubs within their local territories.
  • Settlements with a degree of membership to rural areas ranging from 0.4 to 0.5 exhibit a lower degree of rurality. These areas comprise less than 1% of the observed settlements, with a population of 118,270. Although they have a higher degree of rurality compared to urban areas, these settlements play an integrative role within their immediate surroundings. They appear sporadically across all regions.
  • Settlements with values ranging from 0.3 to 0.4 should be considered in the context of the previously described areas. At a higher level of regionalization, these two groups can be classified into the same category. This group of settlements has a population of 130,902, with a relatively equal distribution of settlements in Vojvodina (northern parts of Serbia) and Central Serbia. These are more urbanized centers within a rural environment, with weak functional dependence on urban centers, as well as some suburban settlements within the gravitational area of Belgrade, Novi Pazar, Sremska Mitrovica, and Kruševac.
  • Settlements with a higher degree of urbanity than the previous category have output values ranging from 0.2 to 0.3, comprising slightly more than 1% of the observed settlements. In 2011, these settlements had a population of 451,072. These are mainly peri-urban settlements that are part of the urban area of the City of Belgrade. Belgrade’s peri-urban settlements represent a continuous zone of positive demographic and functional characteristics, emphasizing the significance of Belgrade in the functional organization of Serbia. The area has a tendency to expand along the highway (Corridor X), forming an urbanization corridor with settlements within the gravitational sphere of influence of Novi Sad. In this category, there are peri-urban settlements surrounding the most populated cities in Serbia: Subotica, Zrenjanin, Kraljevo, Novi Pazar, and Niš.
  • A group of settlements with output values from 0.1 to 0.2 consists of 51 settlements with characteristics that can be considered urban. The population of these settlements is 444,397. Their spatial distribution indicates a higher representation in Vojvodina (northern parts of Serbia), where villages are generally more “urbanized” compared to central parts of Serbia. The other half consists of settlements that can be characterized as morphological and functional parts of established urban areas in Belgrade, Novi Sad, Niš, Smederevo, Užice, and Šabac.
  • The highest degree of membership to urban areas (0–0.1) was observed in 85 settlements, where, according to the 2011 census data, 52% of Serbia’s total population lived. Ten settlements had the highest degree of membership to urban areas (Belgrade, Niš, Novi Sad, Čačak, Novi Pazar, Leskovac, Valjevo, Kruševac, Šabac, Jagodina, Gornji Milanovac, Loznica, Kuršumlija, Tutin, Bajina Bašta).

7. Discussion

Our fuzzy logic-based typology describes a whole gradient of settlements in Serbia, ranging from purely rural to completely urban. This approach is appropriate for achieving the research objective, that is, creating a methodology that considers the functional, demographic, and socio-economic characteristics of settlements in Serbia.
The use of fuzzy logic provided additional flexibility to the settlement research, aligning with the concept of the rural–urban continuum and contemporary spatial concepts. On the other hand, this approach is suitable for settlements that are challenging to classify as typically urban or rural, especially in areas where these influences overlap. From the perspective of spatial planning, the application of fuzzy logic allowed for the examination of the entire spectrum of transitional areas that potentially require different treatment in urban and spatial planning documents. The proposed classification method is not deterministic; the obtained results can be further generalized or disaggregated according to the planner’s needs. Generally, fuzzy logic enabled the consideration of the entire continuum of areas differing in urbanity/rurality levels. These results align with previous studies that used fuzzy logic in delineating rural and urban areas [43,58,59].
Generally speaking, the obtained results correspond to the results of similar studies conducted in Serbia, confirming that rural areas are very heterogeneous in their demographic, economic, natural, and functional characteristics [19,61,62,63,64,65]. A direct comparison with studies conducted in Serbia that fully or partially address this issue is not entirely adequate, primarily because these mentioned studies differ in their objectives, spatial coverage of research, and selected variables. In most of these studies, the focus is on rural areas, while urban areas have been previously separated by statistical or OECD criteria and are not further considered. This research provides a more sensitive approach to the delimitation of urban and rural areas. A recent study by Drobnjakovic and Steinführer (2024) attempts to apply the concept of a rural–urban continuum through the rural–urban gradient [79]. However, this approach does not offer novel insights, as the main hypotheses and several indicators have already been explored in previous research [19,61,66]. Additionally, a similar methodological framework employed had been previously presented in similar research by Gajić et al. (2018, 2021). On the other hand, this study only covered Central Serbia, and as such, the results are not comparable, as for the purposes of this research, a comprehensive set of indicators was created to be applicable across the entire territory of Serbia. As seen in the research results, the northern parts of Serbia are more urbanized compared to Central Serbia, which is exceptionally heterogeneous. Therefore, the selection of relevant indicators that would accurately reflect rural and urban areas in a whole territory was a challenging task.
An appropriate comparison would be with the study from 2021 (Gajić et al.), which was conducted for the entire territory of Serbia based on 16 indicators and PCA and cluster analysis at the settlement level [19]. In this context, the primary advantages of using fuzzy logic can be clearly observed; unlike typologies based on multivariate techniques where a certain degree of information is lost by classifying it into specific components/dimensions with clearly defined boundaries, the presented model is significantly more sensitive to the local specificities of the settlement itself.
Compared to similar research [62,63,64,65], the presented method differs in two ways. First, the broad measures used in similar typologies do not capture variation in settlement patterns, therefore this research represents a new perspective on the rural–urban delineation problem. Secondly, it considers both rural and urban differences without opposing them as the method often does. Although in this research the emphasis was placed on rural areas and settlements, the same methodology can be applied for the classification of urban areas.
It is important to note that the selected variables do not represent a complete picture of the different aspects of rurality/urbanity, as well as their relationships and connections in space. The selection of variables was directed at pointing out the heterogeneity of the geospace of Serbia, which must be considered in spatial planning and management. Additionally, it should highlight the issue of data collection, since many data are not available at the settlement level. An attempt was made to overcome this by using publicly available geospatial data, but it cannot be denied that the choice of indicators was not conditioned by the availability of data. The research covered the period from 2011 to 2019. Most of the variables were calculated based on data from the 2011 Population Census [67], while road network data were obtained from OpenStreetMap 2019 [77] and updated by the authors.
In forming the final set of indicators, efforts were made to select variables that strongly reflect the concept of rurality and the relationship to urban areas, without burdening the model with numerous variables that may have little significance in the dataset. The model formed for settlements in Serbia can be considered an operational approach to defining rurality in this geospace.
For operational reasons, the variables used in this study were limited only to those that meet the following criteria:
  • Reflect the concept of rurality and urbanity in Serbia in line with the proposed theoretical framework;
  • Are available at the settlement level;
  • Can be updated at regular periodical intervals;
  • Can be easily adapted according to the needs of potential users.
The population density, one of the most frequently used variables in this type of research, had diminished significance in this study. The reduced significance is indicated by previous research based on multivariate statistical analysis (see [19,61]). Although demographic indicators are essential in most rural–urban typologies, they often are insufficient to describe the existing heterogeneity of rural and urban areas. With the aim to overcome this problem, spatially oriented indicators related to land use, the population density in relation to populated areas, and accessibility were introduced. By assigning weights, the role of the population remained dominant in this study through indicators such as the population size and the population change index, along with their weights.
The use of variables on commuting aimed to determine spatial and functional connections between settlements, while the analysis of the proportion of the active population engaged in the secondary and tertiary sectors determined the degree of the spatial–functional transformation of settlements.
The determination of the thresholds was based on the statistical distribution of data, which was based on the scientific and professional literature. When determining weight coefficients, efforts were made to reduce subjectivity by applying statistical analyses and additional verification with experts. Although the weighting method is subjective and may be subject to further debate, such an approach is often necessary for a more precise model, especially since measuring rurality and urbanity is challenging. As Gallego [80] emphasizes, assigning an absolutely objective criterion for classifying geographic areas is impossible, especially in the case of rural and urban areas. Every method requires the selection of thresholds, which is to some extent subjective. A good method should be flexible, allowing potential users to easily adjust thresholds and weights according to the specificities of the space [80].
The main advantages of constructing an FIS are working with linguistic variables, allowing for a broad application for various types of research, tolerance to data imprecision, forming a decision-making system much closer to real expert knowledge, etc. The main disadvantage is the large number of rules necessary for the functioning of the system in this research.
The proposed model is based on a multidimensional approach (spatial, demographic, socio-economic), contributing to defining the degree of rurality and urbanity in Serbia’s geographical space. At the local level, the proposed methodology can be used as input information for conceiving the organization of settlement networks. Settlements with a lower membership to rural areas indicate the development of a specific function, thus having greater significance and a role in the observed settlement system. Based on the obtained results, the degree of the spatial–functional transformation of settlements within the observed local self-government unit can be observed.

8. Spatial Planning and Policy Implications

Typologies of international organizations emphasize the necessity of shaping developmental measures based on specific geographical spaces. Spatial planning directly addressing rural–urban interactions, within European frameworks, is considered an instrument to overcome the dualism between cities and villages and a crucial precondition for achieving territorial cohesion [81].
The presented methodology is just one way of observing these spatial differences. The development opportunities for rural areas are conditioned by numerous prerequisites such as the demographic and economic vitality, infrastructure capacity and connectivity, and the quality of life (housing, the development of health and social services, education, etc.). For example, settlements identified in this research as rural peripheries require a different planning approach and the creation of measures at the regional level, unlike sustainable villages where measures must focus on stimulating production opportunities and retaining the existing population in villages. Rural development is challenging in demographically empty spaces with unfavorable physical–geographical characteristics. In this context, alternative uses for such spaces, like tourism, could activate the remaining local population and potentially attract seasonal residents [82,83]. On the other hand, settlements basing their development on established connections with urban areas should be considered in the context of strengthening and encouraging these connections in a qualitative sense. Similarly to rural areas, the development of urban areas should be based on established connections at the regional level, and in this context, urban areas whose centers do not play a role in driving development and socio-economic transformations in the traditional sense (small urban centers in southern and eastern Serbia and Vojvodina) must be differentiated from networks of medium-sized cities that have an integrative role and influence socio-economic transformation in the surrounding areas. Additionally, the Belgrade–Novi Sad metropolitan area represents the dominant pole of development and spatial–functional transformation, with its influences permeating throughout the entire national territory.
In the coming period, settlement development arising from current trends will result in strong spatial polarization. The population will be concentrated mainly in urban areas. In areas with a low degree of rurality, planning measures should support the strengthening of rural–urban connections, i.e., commuting. Although peri-urban areas are often viewed negatively due to the chaotic mixture of urban and rural functions, this approach can have positive effects on reducing the excessive concentration (of the population and functions) in urban areas.
Following the COVID-19 pandemic, opportunities for rural development are evident in the evolving rural–urban dynamics. Recent research [84,85] highlights a notable decline in out-migration rates from rural municipalities, accompanied with a notable rise in in-migration, especially to rural holiday villages near urban centers. These areas, known for their high concentrations of second homes and appealing natural surroundings, have become increasingly desirable for relocation [86,87]. Migration trends reveal a preference for rural areas which have a lower population density while are still close to urban areas; on the other hand, more remote rural areas received less internal migrants [84].

9. Conclusions

The research results contribute to the placement and functional organization of settlements in Serbia, providing a significantly more sophisticated picture of the geographical space of Serbia, ranking settlements from completely rural to completely urban. In this way, it is possible to observe the entire spectrum of transitional areas that differ in the degree of membership to rural or urban areas. The presented methodology has the potential to facilitate the decision-making process in spatial and urban planning and management. This study provides valuable insights into prioritized settlements and offers practical contributions toward mitigating settlement loss and improving sustainable urban development. It also provides a framework for evaluating landscape structure and capacity, allowing for a nuanced categorization of areas by their potential and sensitivity [12,88], particularly in areas where the distinction between urban and rural landscapes is blurred or transitional [6].
The differentiation of various coexisting areas in the territory of Serbia provides decision-makers with the opportunity to more precisely orient development strategies. The perspectives on rural development are conditioned by the overall demographic and economic development of Serbia. The presented methodology can be useful in identifying key spatial issues and their localization, setting relevant revitalization goals and measures. Adequate institutional support (at all levels) and the construction of appropriate infrastructure (technical and social) are necessary for mitigating spatial inequalities and implementing measures related to rural and regional development.

Author Contributions

Conceptualization, A.G.P. and N.K.; methodology, A.G.P.; software, A.G.P.; validation, A.G.P. and N.K.; formal analysis, A.G.P. and B.P.; investigation, A.G.P. and B.P.; data curation, A.G.P.; writing—original draft preparation, A.G.P. and B.P.; writing—review and editing, A.G.P., B.P. and N.K.; visualization, A.G.P. and B.P.; supervision, N.K. All authors have read and agreed to the published version of the manuscript.

Funding

Funds for the implementation of the research presented in this paper were provided by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, registration number 451-03-66/2024-03/200006.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Land 13 01981 g001
Figure 1. Study area.
Figure 1. Study area.
Land 13 01981 g001
Land 13 01981 g002
Figure 2. Methodological scheme.
Figure 2. Methodological scheme.
Land 13 01981 g002
Land 13 01981 g003
Figure 3. Degree of membership to rural/urban areas.
Figure 3. Degree of membership to rural/urban areas.
Land 13 01981 g003
Table 1. Description of variables with associated hypotheses.
Table 1. Description of variables with associated hypotheses.
VariableReferences and HypothesesData Source
Population size
(Number of inhabitants)		A higher number of inhabitants indicates a higher degree of urbanity [19].Census, 2011 [67]
Commuting
(Share of commuters
in total number of active populations who perform occupation		Settlements with pronounced commuting lack developed economic or educational functions, and, therefore, they have characteristics of rural or peri-urban settlement types. Typical rural areas are predominantly mono-functional and have a weakly pronounced daily mobility of the population, as urban influences reach them less strongly [19,61,68,69].Census, 2011 [67]
Active population engaged in the secondary and tertiary sectors
(Share of active population engaged in the secondary and tertiary sectors in total number of active population who perform occupation)		A higher share of employees who work in the secondary and tertiary sector indicates that the settlement has certain characteristics of the transitional or urban type [19,61].Census, 2011 [67]
Population change
(Population change index between 2011 and 1981)		Rural areas are exposed to negative demographic trends [19,43,61,70].Census, 2011 [67]
Population density
(Number of inhabitants per ha of built-up area)		Rural areas have a lower population density than urban areas [19,43,61,71].Census, 2011 [67]
Share of natural and seminatural areas
(Share of agricultural, forest, and other natural and seminatural areas in the total area of the settlement)		The presence of a high proportion of surfaces in natural or seminatural conditions under anthropogenic influence or their use in agriculture is one of the characteristics that clearly differentiates rural from urban. Urban areas have a greater proportion of land covered by artificial surfaces—residential areas, land intended for infrastructure, land for commercial and industrial use, etc. [8,72].SPRS, 2020 [73]
Accessibility
Travel time (by car) to the closest city center; travel time <15 min, 15–30 min, 30–45 min, and >45 min		Accessibility analysis enables the determination of accessible or peripheral rural areas, and, in line with this, the shaping of measures for their development [8,18,43,74,75,76].OSM, 2019 [77]
Table 2. Input variables with threshold values and weights.
Table 2. Input variables with threshold values and weights.
VariablesThresholdWeights
Rural AreasUrban Areas
Population size<2000>50000.45
Commuting 1<5<150.15
Active population engaged in the secondary and tertiary sectors<30>950.15
Population change<60>1000.10
Population density<10>250.05
Share of natural and seminatural areas>90<550.05
Accessibility<4>10.05
1 For the easier modeling and construction of the membership function, the total value of this indicator was subtracted from 100, and in the FIS these values were entered as 0–5% for rural areas and from 85 to 100% for urban areas.
Table 3. The range and mean values of the observed variables in the dataset.
Table 3. The range and mean values of the observed variables in the dataset.
Category Population SizeCommutingActive Population Engaged in the Secondary and Tertiary SectorsPopulation ChangePopulation DensityShare of Natural and Seminatural AreasAccessibility
Number of SettlementsMeanRangeMeanRangeMeanRangeMeanRangeMeanRangeMeanRangeMeanRange
0–0.18544,3261,162,127174396141141164373687610
0.1–0.251871422,722417890291192242853806013
0.2–0.361739542,261597788681243332449874713
0.3–0.43043634669497983431001742151885223
0.4–0.53731962926477684391011452451799313
0.5–0.614316913310458477521103202290906213
0.6–0.78059793219579976869785719111928913
0.7–0.81432574266445100571006644615188956913
0.8–0.964042522152510036100562451290968423
0.9–1132098958310085233799101983123
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Gajić Protić, A.; Krunić, N.; Protić, B. Detecting Serbia’s Settlement Patterns: A Fuzzy Logic-Based Approach to Rural–Urban Area Delimitation for Spatial Planning. Land 2024, 13, 1981. https://doi.org/10.3390/land13121981

AMA Style

Gajić Protić A, Krunić N, Protić B. Detecting Serbia’s Settlement Patterns: A Fuzzy Logic-Based Approach to Rural–Urban Area Delimitation for Spatial Planning. Land. 2024; 13(12):1981. https://doi.org/10.3390/land13121981

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Gajić Protić, Aleksandra, Nikola Krunić, and Branko Protić. 2024. "Detecting Serbia’s Settlement Patterns: A Fuzzy Logic-Based Approach to Rural–Urban Area Delimitation for Spatial Planning" Land 13, no. 12: 1981. https://doi.org/10.3390/land13121981

APA Style

Gajić Protić, A., Krunić, N., & Protić, B. (2024). Detecting Serbia’s Settlement Patterns: A Fuzzy Logic-Based Approach to Rural–Urban Area Delimitation for Spatial Planning. Land, 13(12), 1981. https://doi.org/10.3390/land13121981

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