1 Introduction

Urban vitality refers to the intensity and diversity of human activities in urban spaces, reflecting interactions between spatial structure, functional mix, and socioeconomic dynamics. In urban planning and urban studies, vitality is closely associated with walkability, land-use diversity, accessibility, and the presence of active public spaces (Cardoso & Meijers, 2016; Istrate et al., 2020). Recent scholarship further conceptualizes urban vitality as an emergent, dynamic phenomenon shaped by temporal activity patterns, social interaction, and community engagement (Garau & Annunziata, 2022; Osunkoya & Partanen, 2024).

Urban vitality has been studied more in large metropolitan regions, whereas medium-sized and second-tier cities have been insufficiently explored despite their crucial role in regional development and territorial cohesion (Cardoso, 2016). Unlike large cities around the world, medium-sized cities often experience depopulation (Parkinson & Meegan, 2013). They also undergo functional mono-structuring following industrial restructuring (Berroir et al., 2019) and gradual centre devitalization (Chouraqui, 2021). These transformations often call for context-specific analytical approaches able to capture intra-urban disparities rather than relying solely on aggregate city-level indicators.

European research highlights several spatial dimensions of vitality. Functional density and mixed land use have been shown to strengthen everyday urban activity (Istrate et al., 2020). Socioeconomic diversity and connectivity contribute to resilient local economies (Gao et al., 2024). Moreover, cultural initiatives (Kara et al., 2025) and culture-led regeneration programs (Tzatzadaki, 2024) are useful tools for enhancing urban identity and social cohesion, particularly in second-tier cities (Błaszczyk & Krysiński, 2023).

In post-socialist cities, institutional transformation and economic restructuring have shaped urban spatial dynamics (Cvetinović et al., 2016). Fragmented planning systems and uneven development patterns have contributed to disparities between central and peripheral areas (Stojić & Timotijević, 2024). Urban planning in Serbia often fails to address structural inequalities (Petrović, 2009), remaining detached from the specific needs of peripheral and sub-municipal zones (Vujošević et al., 2012). Research on major cities such as Belgrade, Novi Sad, and Niš identifies demographic change, migration, and infrastructural inequality as important drivers of spatial differentiation (Antonić, 2024; Šantić & Đorđević, 2023). Integrating cultural initiatives, participatory governance (Nedučin & Krklješ, 2022), and infrastructure quality (Đorđević et al., 2023) is increasingly highlighted as a pathway to maintain vibrant and inclusive urban spaces (Protić et al., 2020). Nevertheless, systematic, fine-grained spatial assessments of urban vitality at the sub-municipal level remain limited.

Methodologically, European studies increasingly combine spatial statistics (Gao et al., 2024), GIS modelling, big data (Osunkoya & Partanen, 2024), and spatial analysis (Galaktionova & Istrate, 2025) to capture both static and temporal dimensions of urban vitality. On the other hand, although multicriteria decision-making (MCDM) methods, due to their robustness, are increasingly employed to integrate heterogeneous indicator datasets (Ali et al., 2023; Ginting et al., 2017), they are less present in this domain. Such approaches can allow the identification of intra-urban variability and provide transparent support for evidence-based planning. However, they are rarely applied in medium-sized post-socialist cities, particularly in Serbia.

This study focuses on Niš, Serbia, a representative second-tier city with a compact urban core, a mixed urban–rural administrative structure, and pronounced spatial disparities. Using entropy-based weighting and the TOPSIS method coupled with GIS, the research develops a composite vitality index for a fine-grained understanding of intra-urban differences. The study offers a spatially explicit and transferable MCDM model tailored to medium-sized cities and, by identifying the indicators with the greatest differentiating capacity at the urban level, it supports evidence-based planning beyond municipal aggregates. It addresses the following research question: How can a hybrid GIS–MCDM model be effectively applied to evaluate urban vitality by assessing various levels of urban vitality at the sub-municipal level using data from publicly managed infrastructure assets, environmental resources, and social facilities?

To address this primary question, the study explores the following sub-questions: 1) To what extent can this GIS-based entropy–TOPSIS model serve as a replicable tool for evidence-based planning in other regional centres with limited data integration? 2) How is the vitality disparity spatially manifested? and 3) Which indicators carry the highest weights that can strongly impact vitality index results?

2 Literature review

Urban researchers have highlighted the spatial dimension as a main factor determining urban vitality. For instance, Cardoso and Meijers (2016) reported that urban areas experience greater daily activity when accessibility and land-use diversity, together with density, reach optimal levels. Findings from multiple European cities show that pedestrian traffic and socioeconomic connections increase when cities have functional density combined with mixed-use development (Istrate et al., 2020). On the other hand, Gao et al. (2024) employed GIS-based spatial metrics to show that medium-sized urban areas experience higher vitality levels when their land uses show more diversity and their street networks provide better access to different areas. Furthermore, Liu et al. (2023) demonstrated that, in medium-sized contexts, fine-grained analysis of spatial environmental data is essential to uncover the “sub-municipal” realities of urban vitality. Osunkoya and Partanen (2024) combined spatial data with activity patterns by analysing mobile phone data and reported that vitality levels depend on mobility patterns and access to services. A study by Garau and Annunziata (2022) demonstrated that compact urban areas with proximity to public amenities foster both tangible and perceived vitality among their inhabitants. These findings indicate that a single infrastructure indicator cannot adequately capture vitality; instead, a multidimensional evaluation is necessary.

Considering medium-sized and second-tier cities, European spatial policy discussions have started to recognize their rising significance in their development plans. Cardoso (2016) argued that such cities play a stabilizing role in regional systems, despite lacking the global competitiveness of metropolitan centres. However, several important factors affect their vitality. Parkinson and Meegan (2013) identified demographic decline as a common trend in post-industrial second-tier cities. Berroir et al. (2019) showed how functional mono-structuring as a consequence of the industrial restructuring of cities reduces urban diversity and weakens local resilience. Chouraqui (2021) demonstrated that suburbanization, with its limited service node development, is the root cause of the decline in central areas. Studies have also reported that culture-led regeneration initiatives have potential as a community revitalization method. Kara et al. (2025) examined the impact of European Capital of Culture initiatives on urban identity and local economic dynamics. Tzatzadaki (2024) found that cultural programming can stimulate social cohesion when embedded in long-term governance frameworks. However, Błaszczyk and Krysiński (2023) warned that the outcomes from such projects depend on their specific environment, resulting in different effects across different areas. There is an evident incline in research toward larger cities; thus, there is a limited understanding of vitality in a wider spectrum of European cities.

Research on post-socialist cities recognizes three main factors that affect their urban vitality: institutional changes, privatization, and alterations to governing systems. Cvetinović et al. (2016) argued that deregulation and fragmented planning practices largely influenced uneven urban development patterns in Serbia. Stojić and Timotijević (2024) showed that peripheral settlements often experience infrastructural deficits compared to central zones. Serbian scholarship provides additional context. For instance, Antonić et al. (2024) reported that demographic changes in major Serbian cities mainly result from migration and aging. On the other hand, Šantić and Đorđević (2023) showed that infrastructure distribution creates persistent uneven development patterns between different areas. Protić et al. (2020) and Nedučin & Krklješ (2022) argued that improved accessibility to public areas positively affects their use, which leads to increased urban activities. The research conducted in Serbia primarily focuses on single aspects of urban sustainability and vibrancy, and lacks attempts at integrated vitality analysis. Due to the regional differences in post-socialist urban transformations, mapping urban vitality in Serbian cities requires a context-sensitive framework.

Urban vitality is examined using various methodologies. For instance, Osunkoya and Partanen (2024) combined traditional metrics with mobile phone data through GIS analysis to understand how urban diversity and socioeconomic features correlate with vitality. Gao et al. (2024) employed data such as nighttime light, housing prices, social media, points of interest (POIs), and NDVI data to measure various aspects of urban vibrancy using the Geodetector and geographically weighted regression models. Galaktionova and Istrate (2025) assessed street vitality by using functional density as a proxy, which was derived from OpenStreetMap (OSM) data and analysed through a spatial lag regression model. Lopes and Camanho (2013) used data envelopment analysis to understand how public green spaces contribute to urban vitality. Garau and Annunziata (2022) combined space syntax and GIS to understand how urban form components impact vitality potential.

Despite the different methodological approaches, challenges related to data alignment and resolution discrepancies persist. There is also variability in data availability, applied technology, and level of analysis. Some studies focus on a fine-grained local scale, and others consider a broader, metropolitan scale. Furthermore, the use of aggregated data sources (such as mobile phone data or POI datasets) may oversimplify contextual information.

Several significant challenges are identified in the literature reviewed, such as the multidimensional nature of urban vitality, which challenges “one size fits all” urban theories and supports context-specific frameworks for urban vitality analysis, the distinct structural challenges affecting urban vitality in medium-sized and post-socialist cities, and the dearth of studies using MCDM in this setting.

As a result, this study contributes by operationalizing a spatial MCDM-GIS model tailored to a medium-sized post-socialist city and by providing a transparent ranking of settlements based on publicly governed infrastructural, environmental, and social assets, expanding the scope of commonly used determinants of urban vitality (such as spatial diversity, mixed land uses, urban density, and so on).

3 Methodology

3.1 Study area

Niš was chosen as an illustrative case of a medium-sized Serbian city facing spatial disparities and functional challenges. As one of the few urban centres outside the capital region with a strong regional role, Niš serves as an administrative, economic, and educational hub for southern Serbia while showing marked differences in accessibility, land use, and socioeconomic activity between central and peripheral areas (Figure 1). Its compact urban structure, combined with areas of both decline and emerging development, makes Niš suitable for examining how different characteristics influence vitality. Since 2004, Niš has been administratively divided into five municipalities. These municipalities have diverse spatial, demographic, and functional characteristics. For instance, the municipality of Medijana, although the smallest (16 km²), is the most densely populated, with 82,360 residents. In contrast, Niška Banja, the largest in area, is predominantly rural and features the lowest population density. Differences in urban development levels, infrastructure provision, and access to services combined with the coexistence of urban and rural lifestyles generate divergent quality-of-life outcomes. Demographic disparities further reinforce this fragmentation: some settlements have an average age as low as thirty-eight, and others as high as sixty-nine. These spatial and demographic differences necessitate detailed sub-municipal analysis. The presence of sixty-nine rural settlements within the five city municipalities emphasizes the limitations of using the municipal scale as the primary unit of urban analysis. Therefore, these settlements are considered as study units because they lack sufficient data and are frequently excluded from conventional planning processes.

Figure 1: Study area.

3.2 Indicators

The indicators used for this study primarily focus on physical resources and important infrastructure under the municipal government. Although privately owned facilities, such as grocery stores or banks, contribute to daily life and urban vitality, their presence is market-dependent and reflects commercial decisions rather than public-sector support, and so they are not included on the indicator list. The final list of indicators was developed in accordance with relevant literature and following an extensive field investigation, which included an inventory of all infrastructure elements (e.g., roads, schools, and sport facilities) and resources (e.g., watercourses, forests, and caves). The indicators are classified according to five criteria briefly outlined below.

1. Geographical location, natural potential, and environmental protection

– Proximity to the city centre indicates accessibility to diverse economic opportunities, public services, cultural amenities, and transportation infrastructure. According to Jacobs (1961), the quality of urban life depends on dense mixed-use urban environments that provide better closeness and social networks, and where everyday activities are close together. Montgomery (1998) supported this, stating that urban vitality diminishes with distance from the urban core.

– Proximity to watercourses offers ecosystem services such as provisions for agriculture, natural flood regulation, and recreational values, which MEA (2005) recognized as essential for long-term local development.

– Proximity to forests reflects potential for eco-tourism, sustainable resource use, recreation, improved microclimate, and overall well-being (Tzoulas et al., 2007).

– Protected natural areas function as part of a green infrastructure network linking environmental conservation with community well-being and supporting local livelihoods through regulated low-impact activities such as eco-tourism, speleology, or collecting herbs, allowing an economy in line with long-term ecological preservation (MEA, 2005).

– Geological resources include features such as caves, thermal springs, and spas, which support tourism, small business development, sports, and recreation (Farsani et al., 2011).

2. Infrastructure and communications

– Types of access roads from the city centre, roads within the settlement, and accessibility to public transportation are indicators of physical connectivity, economic potential, emergency response, and community integration (Litman, 2021).

– Frequency of organized public transport.

– Presence of petrol stations refers to the spatial proximity of these to agricultural and residential clusters, cutting transport costs, and ensuring farmers have the fuel they need close by. This can support infrastructure integration, economic vitality, and support for tourism and logistics.

– Presence of a post office indicates developed communication infrastructure, access to financial and logistical services, and information dissemination, which is especially important in rural areas (Castells, 2010). It is an important physical gateway for e-commerce, providing the “last mile” logistical link needed for community integration in rural or remote areas.

– Mobile network coverage serves as the fundamental digital system allowing suburban areas to develop into connected economic centres through the provision of final data transmission and communication services while also supporting the social and cultural activities that drive suburban community life.

3. Healthcare

Niš’s suburban settlements are already experiencing demographic decline. Most of these settlements lack access to healthcare facilities. Thus, the following two indicators can contribute to local vitality:

– Access to primary healthcare facilities.

– Access to pharmacies.

4. Education

– Access to educational institutions: a lack of primary schools causes families to migrate toward the city. Thus, a primary school in a settlement reflects a capacity to retain residents.

5. Social development

– Number of public outdoor sports fields and recreational facilities, and sports clubs: such facilities are “bonding” spaces (Putnam, 2000), essential for creating trust and cooperation. Sports fields and sport clubs are a proxy for healthy and multigenerational demographics, and a base for social interaction and community well-being.

– Number of cultural institutions and local events refers to cultural festivals and creative hubs, mainly in the urban core of Niš, that draw the younger generation out from metropolitan edges. This creates stagnant areas of peripheral community, dull and without the social vibrancy and institutional “glue” that Florida (2002) proposed as a requirement for maintaining a productive long-term multi-generational population.

– Number of religious buildings reflects the potential for social cohesion, community inclusion, and opportunities for volunteerism and civic participation. A diversity of religious institutions may also suggest cultural tolerance and pluralism (Putnam & Campbell, 2010).

3.3 Scoring method and data sources

Class boundaries were established by considering both accepted norms and empirical evidence. We utilized the walkability thresholds established by UN-Habitat (2018) to determine distance-based measures. For indicators with established classifications, such as mobile network coverage and road categories, the corresponding institutional categories were utilized. For the remaining indicators, the complete dataset was analysed and classes that accurately reflected the distribution of values were established. For instance, if the inventory showed that the maximum number of geological resources within the settlement boundary is more than three, we assigned three classes to this indicator (see Table 1); if the maximum number of schools in all settlements is 1, we assigned binary classes. The scoring system was thus both evidence-based and aligned with current planning and regulatory frameworks. Consequently, the scoring system may accurately represent actual differences and respond to minor variations within communities while adhering to a four-point or binary rating framework. The settlement boundaries (SB) were taken into account when evaluating indicators.

The values of the four-point scale are 3 = significant potential for local development, 2 = moderate potential, 1 = weak potential, 0 = absent. Scoring is based on data collected from publicly available sources, institutional records, field visits, and planning documentation, ensuring a comprehensive and reliable data foundation (Vranić et al., 2026a).

Table 1: Indicator values and rationale.

*Based on the number of departures to each municipality per workday. The final number was standardized to a range of 0–1 using the min–max approach, with 0 being the least frequency and 1 the most. For uniformity of evaluation, the normalized range was divided into three frequency groups.

**The signal area for the three national operators was compared to the total area of the settlement using the Regulatory Authority for Electronic Communications and Postal Services (RATEL) classification levels: excellent (expected very good connection), good (expected good connection), and satisfactory (expected acceptable connection with interruptions). The final grade was determined by averaging the scores of all three operators based on signal dominance: 3 = combined territory of excellent/good signal exceeds satisfactory signal area, 2 = satisfactory signal area exceeds combined excellent/good signal coverage, 1 = satisfactory signal level covers more than 80% of the territory.

3.4 Prioritization methodology

The objective of applying the multi-criteria method was to rank local communities according to predetermined criteria to assess their vitality. Shannon’s entropy method was used to calculate the weight values of the criteria and determine the weight coefficients. The term entropy denotes disorder and uniformity in a data set (Shannon, 1948). The relationship between the entropy value and weight coefficient is inverse (Zakeri et al., 2025): criteria with a higher entropy value have low coefficients and vice versa (Ali et al., 2023). Criteria with a minimum difference between their values have a higher entropy value because the data are uniformly distributed and provide limited information. In contrast, criteria with a greater difference between individual values provide a variety of information for ranking, the degree of entropy is lower, and their weight value is greater (Chen, 2020). Criteria with lower entropy can contribute to the observation of differences among local communities because they imply variability in the data and facilitate ranking. Applying this method seeks to avoid subjectivity in the ranking process. The TOPSIS method then combined the weighted indicators into a composite urban vitality index, ranking settlements by how close they are to an “ideal” high-vitality scenario and how far from the “least desirable” scenario. More information about each method can be found in the Vranić et al., (2026b).

4 Results

4.1 Indicators

The spatial distribution of indicators is presented in Figure 2. For proximity to the city centre, isochrone maps were generated to estimate travel times to the city center of Niš under optimal driving conditions by car (excluding rush hour), measured from the settlement center. Fifty per cent of settlements lie within the third zone (20–30 minutes), 47.1% in the second zone (10–20 minutes), and only two within ten minutes. Several settlements fall on isochrone boundaries. Sixty per cent are in the second zone, indicating most residents drive eighteen to twenty minutes to the city centre (Figure 2a). Residents generally walk five to fifteen minutes (400–1,200 m) to the nearest watercourse (51.4% are within 400 m, 18.6% within 400–800 m, 15.7% within 800–1,200 m, and 14.3% beyond 1,200 m; Figure 2b). Proximity to forest area considered the distance between the centre of the settlement and the closest forest; 58.6% of residents live near forests: 19% within five to ten minutes walking, 14% within fifteen minutes (800–1,200 m), and 8% within five minutes. The remaining 41.4% are over 1,200 meters away (Figure 2c). Protected areas cover 10.4% of the study area, mainly in the east. Thirteen of sixty-nine settlements are partially or fully within nature parks and reserves such as the Sićevo Gorge and Mount Suva (Figure 2d). Only seven settlements (10.1%) have geological resources within their boundaries. Some even have thermal springs and caves with national or international significance, such as Cerje and Pešturina caves (Figure 2e). Regarding types of access roads from the city centre and roads within the settlement, 10% of settlements are accessible from the city centre only via tertiary roads, 29% via secondary roads, and 61% via primary roads. Within settlements, 31% rely on tertiary roads, 33% on secondary roads, and 6% on primary roads (Figure 2f).

Figure 2: a) proximity to the city centre, b) proximity to watercourses, c) proximity to forests, d) protected natural areas, e) geological resources, f) types of roads within the settlement and types of roads leading there, g) accessibility to public transportation, h) frequency of organized public transport, i) presence of petrol stations, j) presence of a post office, k) mobile network coverage, l) access to educational institutions, m) primary healthcare facilities, n) access to pharmacies, o) number of public outdoor sports fields and recreational facilities, p) number of religious buildings, q) number of cultural institutions, and r) number of sports clubs.

Approximately 80% of settlements have organized public transport, mostly buses; 11.4% also have rail service. If accessible neighbouring areas are included, rail access rises to 35.7%. Four percent of settlements lack organized transport entirely (Figure 2a). In terms of frequency of public transport, 12.9% have the highest frequency (0.69–1.0), 12.9% medium (0.31–0.53), and 74.2% low (up to 0.30), decreasing with distance from the city centre (Figure 2b). The spatial distribution of petrol stations corresponds to the expected directions of the main highways and access roads. Along these, petrol stations are present in 14.2% of settlements. Including neighbouring settlements that can be driven to in ten minutes or less, an additional 41.4% of settlements are direct users of this resource. The number of post offices decreases with distance from the centre of Niš, but coverage remains good, at 49% overall. Including settlements bordering those with post offices, the coverage increases to 95.7% of the population. There is great spatial variation in mobile network signal quality for local communities: 19.4% of settlements have very good to excellent signal coverage (≥ 2.7), with acceptable mobile access for digital services, online communication, and smart infrastructure. However, 23.6% of settlements are in the poor to very poor range (≤ 1.3), meaning that direct, good mobile signal coverage exists only 19.4% of the local settlements.

Primary schools are present in half of the settlements; 28.5% are branch schools. The remainder lack primary schools, requiring residents to attend school in nearby settlements. Voronoi diagrams showed half the settlements share a health centre with two or more others, 37.6% share with three or more, 14.3% share with only one other, and 35.7% have their own. Pharmacy coverage is slightly lower than that of healthcare facilities and diminishes with distance from the centre of Niš: 87.1% of settlements share a pharmacy with two or more others, and 64.3% with four or more settlements. Only 12.9% of settlements have their own pharmacy, and 4.3% share with one other. Regarding sports and recreational facilities, 11.4% of settlements lack facilities, 51.4% have at least one, and 37.2% have two or more. Cultural institutions are absent in 88.6% of settlements, and 11.4% have at least one. Among religious buildings and cultural institutions, Orthodox monasteries and churches dominate, although activity data are limited. Half the settlements lack religious buildings, 40% have one, and 10% have multiple. Most communities (85.7%) do not host cultural, sports, or traditional events, 5.8% hold one or two, and 8.6% host more than two. Regarding sports clubs, 35.7% of settlements lack these, 57.1% have one, and 5.7% have two or more.

4.2 Weights and ranks

The applied entropy method highlights the indicators’ relative importance through weight coefficients (Table 2). A higher weight coefficient means that the observed indicator has a high impact on the vitality index ranking, and a low weight coefficient mean that that the indicator has limited impact. The highest-weighted indicators are the number of events (0.139), geological resources (0.136), and cultural institutions (0.124). Along with indicators such as pharmacies (0.101) and petrol stations (0.111), these have the highest influence on the vitality ranking. Their determined entropy is lower, and the data make it possible to differentiate settlements, which is important for developing the vitality index. On the other hand, the indicators with the lowest weight, such as road hierarchy (0.002), proximity to the city centre (0.004), and primary healthcare access (0.006), were not very influential for differentiation. Their entropy is higher because the criteria values are homogenous and do not distinguish settlements. This does not imply that these indicators are irrelevant; rather, their relatively uniform distribution or limited variability across settlements reduces their ability to shape overall vitality ranking. These outcomes indicate that, although basic infrastructure is important, Niš is mainly defined in terms of culture, ecology, and community-based assets.

Table 2: Objective weight coefficients by Shannon’s entropy method.

Table 3: TOPSIS prioritization list.

The TOPSIS ranking results in Table 3 show discrepancy among the settlements: Malča (Ci = 0.690) and Nikola Tesla (Ci = 0.622) lead, whereas Berčinac, Rujnik, and Manastir stand at the bottom because they differ only minimally from the most negative solution. Based on natural breaks and considering the complete vitality ratings (from 0.690 to 0.017), three vitality categories can be recognized: 1) the high-vitality tier, with only 7% of settlements that attain a score over 0.400; 2) the predominant middle tier, with 68% of settlements with values between 0.100 and 0.399; and 3) the low-vitality tier, with 25% of settlements with vitality ratings below 0.100. Niška Banja and Donje Vlase are as the closest to advancing to higher vitality status, and Čamurlija and Oreovac are the closest to dropping to lower vitality status. In the higher vitality group, Komren is at risk of becoming a member of the medium vitality group if conditions change. The spatial distribution of vitality rankings in Figure 3 reveals significant variances within the administrative boundaries of Niš, without a distinct centrality pattern, indicating that proximity to the city centre is not directly related to improved vitality.

Figure 3: Visual representation of the vitality index (source: authors).

5 Discussion

5.1 Interpretation of findings

This research has developed a spatially explicit hybrid MCDM model to assess urban vitality at the sub-municipal level. It was initially tested and validated in Niš, Serbia. In line with the first research sub-question, by merging entropy-based weighting with TOPSIS methodology, a composite vitality index sought to capture disparities among five key dimensions: geography and natural assets, infrastructure and communications, healthcare, education, and social development. The model can serve as a highly replicable tool by using public indicators that can be easily mapped in any urban area. It is suitable for identification of spatial disparity with use of basic data requirements while creating a clear evidence-based foundation for planning in data-scarce post-socialist regions.

The findings demonstrate a significant gap in settlements’ capacity to foster vitality, primarily in terms of accessible physical infrastructure and resources. In spatial terms, and in line with the second research sub-question, the analysis showed no connection between proximity to the city centre and vitality. Instead of a monotonic outflow from the urban centre, the lowest vitality rankings (67–69) are found in the intermediate zone between the urban core and the northern and northeastern peripheries, indicating pockets of low vitality in this zone rather than a single-channel peripheral decline. This shows that the potential vitality of the settlement is influenced more by the mix of its assets than by its distance from the urban core.

The study also shows a prominent digital and cultural gap. Although most settlements have easy access to resources and infrastructure, mobile phone signal coverage remains an issue, with around 25% of settlements having poor connections. This can limit their residents’ digital integration – that is, access to associated options such as online jobs, online education, and e-commerce, which contributes not only to overall vitality but also to emergency-response options. Furthermore, nearly 90% of settlements lack cultural institutions, which are critical to the development of local social life and identity.

In addition, based on the distribution of vitality, the findings imply that planning interventions should focus on the settlements that belong to the predominant middle vitality tier, which comprise most of the research area.

5.2 Relation to previous studies

The results strengthen the multidimensionality of vitality, meaning that it must include both material and intangible services that constitute cultural and ecological resources. The disparities identified are not reflected in Niš’s planning documents, which employ municipal-level statistics, masking sub-municipal reality. In this respect, the vitality index meets the need for fine-grained spatially indexed approaches to embody intra-urban heterogeneity (Liu et al., 2023). By revealing inequalities between different settlements, this index provides a clear guide for making local decisions that are more responsive to each settlement’s specific needs.

In line with the third research sub-question, the results also confirm a key role of social development for vitality. Receiving relatively high weightings through the entropy method, social development indicators (such as number of events and cultural institutions) demonstrate that even modest variations can strongly influence vitality rankings. This resonates with Jacobs’s (1961) and Putnam’s (2000) emphasis on social cohesion and civic life as vital components of urban sustainability, and with Osunkoya and Partanen (2024), who reported that socioeconomic variables significantly correlate and influence the vitality value. Furthermore, the TOPSIS ranking points to deviation from the centrality pattern, frequently noted in post-socialist cities, which generally entails the marginalization of peripheral areas because investment increasingly focuses on urban cores, as discussed by Vujošević et al. (2012) and Petrović (2009). Therefore, the spatial arrangement observed appears to highlight the importance of relational rather than positional aspects of urban vitality.

The landscape of vitality indicators is diverse and well covered in the literature. For instance, Lopes and Camanho (2013) focused on the use of public green spaces as a measurable indicator of urban vitality. Galaktionova and Istrate (2025) suggested using “functional density” as a proxy to assess street-level vitality. Osunkoya and Partanen (2024) proposed integration of traditional metrics with big data, such as mobile phone records. Garau and Annunziata (2022) applied built-up and population density, various centrality indexes, density of POIs, and environmental quality indicators. Putnam (2000) and Putnam and Campbell (2010) highlighted social capital indicators such as civic engagement, religious participation, and community networks as a “social” side of vitality. Many of these indicators were included directly or indirectly in this study, such as proximity to geological resources as a proxy for Garau and Annunziata’s environmental quality, the presence of religious buildings as a proxy for Putnam’s religious participation, and mobile network coverage as a proxy for Olukoya and Partanen’s phone records. However, the purpose of this study was to address a set of indicators that cover the segment of infrastructure and resources under public authority (i.e., municipal governance) and focus on rural settlements within the administrative boundaries of the city, relying on an extensive field inventory that provided an initial set of indicators for existing assets. As a result, many of the literature’s proposed indicators, which are largely concerned with urban centres, were not directly applicable. Although the selected set of indicators meets the contextual analysis requirements, it also limits the scope of a more extensive investigation.

5.3 Limitations

Because this research was intentionally framed to emphasize the essential physical assets that can support vitality, one limitation is the absence of indicators that map human activity in the settlements analysed. A second limitation is the use of categorical metrics (e.g., the presence of cultural institutions or healthcare facilities), which might oversimplify the complexities of service quality, accessibility, and usage. A selected set of indicators also mapped the current state of the settlements, but they lacked a temporal component that may reflect seasonal or daily patterns of vitality. Finally, by examining the indicators that represent municipal responsibility, the researchers have purposely neglected market-driven and community-led activities that clearly contribute equally to local vibrancy.

Recognizing that a “one size fits all” framework is not attainable, the primary objective of this research was to provide a fundamental spatial baseline with field-validated indicators, offering a clear actionable map for municipal planners.

5.4 Future research

In line with the limitations identified, future research should explore more fine-grained data, such as facility capacity, quality of provision, or user satisfaction, to better capture community-level differences. Second, and in line with what Osunkoya and Partanen (2024) proposed, the results should be overlayed with big data (such as social media analysis, GPS, and location-based services, integrating real-time data on business/service openings and closures). Third, the analysis reflects a single temporal snapshot of vitality. Incorporating temporal dimensions would allow for a more comprehensive understanding of vitality rhythms. Fourth, although the entropy–TOPSIS methodology reduced subjectivity in weighting, it may inadvertently undervalue indicators with low variability but high conceptual importance (e.g., education and healthcare). A potential avenue is the use of hybrid approaches that combine objective statistical variation with expert choices. By integrating these dynamic measurements, future research can move toward a predictive analytic framework that can show how adding a particular physical asset can affect settlement vitality.

6 Conclusion

This study presented the vitality index, a flexible tool for identifying inequities and developing spatial strategies at the sub-municipal level. However, it is important to acknowledge that the observed weightings are context specific, reflecting the data structure and variability of the local sample. This emphasizes the need to tailor vitality evaluations to unique local conditions and demonstrates that the approach may be adjusted to detect substantial variations across urban areas.

In practice, the findings and methods in this study can help enhance evidence-based decision making by contributing to the asset-based strategy in the local planning process, which takes into account the unique characteristics of each settlement. It also provides a platform for further improvements with dynamic and demographic indicators for greater insights into urban vitality.

Petar Vranić (corresponding author), Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia

E-mail: petarvvv@mi.sanu.ac.rs

ORCID: 0000-0002-9671-992X

Ljiljana Vasilevska, Faculty of Civil Engineering and Architecture, University of Niš, Niš, Serbia

E-mail: ljiljana.vasilevska@gaf.ni.ac.rs

ORCID: 0000-0001-7692-8436

Ivana Petkovski, Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia

E-mail: ivana993@turing.mi.sanu.ac.rs

ORCID: 0000-0002-6836-0139

Acknowledgments

This work was supported by the Serbian Ministry of Science, Technological Development, and Innovation 1) through the Mathematical Institute of the Serbian Academy of Sciences and Art and 2) under the Agreement on Financing Research of Teaching Staff at the Faculty of Civil Engineering and Architecture, University of Niš (registration no. 451-03-137/2025- 03/200095, dated 4 February 2025).

Data availability statement

The data used in this article can be accessed from Zenodo at https://zenodo.org/records/18670892 (Vranić et al., 2026); this article must be cited if these data are used in other publications.

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