Finisterra, LVI(130), 2025, e39994
ISSN: 0430-5027
doi: 10.18055/Finis39994
Artigo
SPATIAL DISPARITIES IN POPULATION AGEING IN BOSNIA AND
HERZEGOVINA:
K-MEANS CLUSTERING APPROACH
ALMA KADUŠIĆ
1
MARIANA LUKIĆ TANOVIĆ
2
NEDIMA SMAJIĆ
1
AIDA AVDIĆ
3
ABSTRACT – Population ageing is one of the main demographic issues in Bosnia and Herzegovina. This country
has been facing significant ageing, with spatial differences in the pace and level of ageing. To determine spatial
disparities in population ageing in Bosnia and Herzegovina, data on ageing coefficient, ageing index, dependency ratio,
old-age dependency ratio, and the average age in the period from 2013 to 2023 were used. Cluster analysis is applied
to determine groups of municipalities with similar levels of ageing. To group municipalities into clusters, a non-
hierarchical clustering method, k-means clustering, is used. Analysis was performed by experimenting with various
clustering alternatives between 2 and 5 clusters, while the Elbow method, Calinski-Harabasz pseudo F-statistic, and
Tukey`s Honestly Significant Difference test were used to validate the appropriate number of clusters. Results
confirmed clusters with different levels of population ageing in Bosnia and Herzegovina. The most adverse trends in
population ageing are confirmed in the rural municipalities of Western Herzegovina and Eastern Bosnia. The results of
this study can serve as a basis for further research on the population ageing process and the planning of future
demographic development in Bosnia and Herzegovina.
Keywords: Bosnia and Herzegovina; population ageing; spatial disparities; cluster analysis; k-means clustering.
RESUMO – DISPARIDADES ESPACIAIS NO ENVELHECIMENTO POPULACIONAL NA BÓSNIA E HERZEGOVINA:
ABORDAGEM DE CLUSTERING K-MEANS. O envelhecimento da população é um dos principais problemas demográficos
na Bósnia e Herzegovina. Este país tem enfrentado um envelhecimento significativo, com diferenças espaciais no ritmo
e no nível de envelhecimento. Para determinar as disparidades espaciais no envelhecimento da população na Bósnia e
Herzegovina, foram utilizados dados sobre o percentual da população com 65 anos ou mais na população total, o índice
de envelhecimento, o rácio de dependência, o rácio de dependência dos idosos e a idade média no período de 2013 a
2023. A análise de clusters foi aplicada para identificar grupos de municípios com níveis de envelhecimento
semelhantes. Para agrupar os municípios em clusters, foi utilizado o método de agrupamento não hierárquico k-means.
A análise foi realizada experimentando várias alternativas de agrupamento entre 2 e 5 clusters, enquanto o método
Elbow, a estatística pseudo-F de Calinski-Harabasz e o teste de diferença significativa de honestidade de Tukey foram
usados para validar o número apropriado de clusters. Os resultados confirmaram a existência de agrupamentos de
municípios com diferentes níveis de envelhecimento populacional na Bósnia e Herzegovina. As tendências mais
adversas no envelhecimento da população foram observadas nos municípios rurais da Herzegovina Ocidental e da
Bósnia Oriental. Os resultados deste estudo podem servir como base para futuras investigações sobre o processo de
envelhecimento da população e para o planeamento do desenvolvimento demográfico futuro na Bósnia e Herzegovina.
Palavras-chave: Bósnia e Herzegovina; envelhecimento populacional; disparidades espaciais; análise de
agrupamento; agrupamento k-means.
Recebido: 21/01/2025. Aceite: 29/07/2025. Publicado: 01/10/2025.
1
Department of Geography, Faculty of Natural Sciences and Mathematics, University of Tuzla, Urfeta Vejzagića 4, 75 000, Tuzla, Bosnia
and Herzegovina. E-mail:
alma.kadusic@untz.ba, nedima.smajic@untz.ba
2
Department of Geography, Faculty of Philosophy – Pale, University of East Sarajevo, Pale, Bosnia and Herzegovina. E-mail:
mariana.lukic.tanovic@ff.ues.rs.ba
3
Department of Geography, Faculty of Science, University of Sarajevo, Sarajevo, Bosnia and Herzegovina. E-mail:
aidaavdic@pmf.uns.ba
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
2
HIGHLIGHTS
• Determined spatial patterns of population ageing in Bosnia and Herzegovina.
• Applied k-means clustering to identify spatial disparities in the level of population ageing in
municipalities of Bosnia and Herzegovina.
• Identified three distinct clusters with different levels of population ageing.
• Determined that Bosnia and Herzegovina is facing adverse population ageing trends.
• Results of the study can be used as a basis for future demographic research and suggest the
necessity of creating regional strategies of demographic development in Bosnia and
Herzegovina.
1. INTRODUCTION
Population ageing can be considered a global phenomenon characterized by an increasing
proportion of the population older than 65 years of age in populations worldwide. In the past decades,
the issue of population ageing has received attention from numerous authors. Studies of importance
highlight that global population ageing is a consequence of declining fertility rates and increasing life
expectancy (Kong, 2018), while Lo (2023) and Zlotnik (2016) highlight the influence of globalisation,
or economic, political, and social changes caused by globalisation, on population ageing. Europe is
among areas with high levels of ageing (Cristea et al., 2021; Kočanova et al., 2023), and recent studies
of demographic ageing in Europe suggest a significant increase in population ageing indicators in the
countries of Southeastern and Eastern Europe (Jakovljevic et al., 2021; Jakovljevic & Laaser, 2015).
Therefore, a considerable number of studies explored the trends of population ageing in countries of
the region. Gabor et al. (2022) and Jemna and David (2021) have researched the demographic ageing
trends in Romania, Lillova (2021) in Bulgaria, Roszkowska (2018) in Poland, Gnjatovic and Devedzic
(2016) in Serbia, etc. Several studies have been conducted on population ageing in Bosnia and
Herzegovina that highlight the challenges caused by this demographic process. Gekić et al. (2020) and
Gekić et al. (2019) reported a rapid increase in elderly population, which poses challenges to the
economic and social system and causes serious issues for the demographic development of Bosnia and
Herzegovina. Kadušić et al. (2016) researched the causes and consequences of population ageing in
Bosnia and Herzegovina and noticed a rapid decline in population bio-dynamics and an increase in
ageing coefficients and the average age of the population.
Recent studies have shown that population ageing is accelerating with spatial and temporal
disparities in different world regions (Li et al., 2019; Wan et al., 2022;). Therefore, researchers have
shown an increased interest in spatial disparities or regional differences in population ageing. Wan et
al. (2022) investigated population ageing on a global scale, noticing significant regional disparities in
ageing patterns, with developed countries ageing faster, while Li et al. (2019) have reported that
differences in ageing rates in world countries have been increasing, suggesting that global population
ageing is becoming more spatially differentiated. In Turkey, Yakar and Özgür (2024) analysed ageing
patterns and identified regions with different levels of ageing. Basile et al. (2023) addressed the
spatial-temporal aspect of population ageing in Italy and identified how ageing varies across different
regions of the country. Zhang et al. (2022) reported that the spatial distribution of the population in
Jiangsu Province in China is also showing significant spatial discrepancy, etc. In Bosnia and
Herzegovina, Avdic and Avdic (2023) analysed spatial disparities in regional development and noted
significant demographic challenges in peripheral regions of this country, while Avdić et al. (2022)
noticed that population ageing has more adverse characteristics in border regions of Bosnia and
Herzegovina. Kadušić et al. (2023a) analysed the spatial distribution of ageing coefficient and ageing
index, and research results confirmed the concentration of these indicators, suggesting unequal ageing
in different areas of Bosnia and Herzegovina. Pronounced polarization of demographic development
and population ageing, at the national, regional, and local levels in Bosnia and Herzegovina, is also
mentioned in the research study by Gekić et al. (2019) highlighting the necessity for adopting adequate
population policy for future demographic development.
Therefore, population ageing is a demographic process with pronounced spatial differences, and
peripheral areas are very often exposed to more adverse ageing trends (Krisjane et al., 2023). In recent
years numerous conducted studies applied cluster analysis to identify spatial patterns of demographic
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
3
processes and phenomena, including population ageing (Ahmadov, 2023; Jurun et al., 2017; Yakar &
Özgür, 2024). According to Kastreva and Patarchanova (2021), clustering is a process of classifying
data into clusters, while the clustering method can be defined as a technique used for grouping
statistical data based on similarities in variables.
Various techniques were used to research or determine spatial disparities in population ageing,
and in recent studies, k-means clustering is often recognized as a very useful technique for determining
spatial differences in ageing (Abbas et al., 2020; Jurun et al., 2017; Yakar & Özgür, 2024). According to
Rašić-Bakarić (2007) in social sciences cluster analysis is recognized as the most suitable method of
classifying units of similar characteristics, while Ismail et al. (2016) indicate that k-means clustering
can effectively identify spatial patterns in demographic ageing. In recent years, k-means clustering has
been often applied in demographic analysis and in researching spatial disparities of population ageing.
For example, Inoue and Inoue (2024) applied the k-means algorithm to analyse prospects of
population ageing in Japan, Yakar and Özgür (2024) used this technique to identify distinct ageing
regions in Turkey, while Ismail et al. (2016) utilized k-means to investigate the demographic change
in Malaysia, etc. Together these studies provide important insights into the utilization of k-means
clustering in identifying spatial patterns of ageing.
Previous research on population ageing in Bosnia and Herzegovina has primarily focused on
descriptive analysis at the national level (Gekić et al., 2020; Kadušić et al., 2016). Only a few studies
addressed spatial differences in this demographic process (Avdic & Avdic, 2023; Avdić et al., 2022;
Gekić et al., 2019). While Kadušić et al. (2023a) used spatial autocorrelation to analyse the clustering
of population ageing indicators, there is a lack of studies that used advanced spatial statistical
techniques to identify spatial differences in demographic ageing in Bosnia and Herzegovina.
This study aims to address this research gap by applying k-means clustering to identify
population ageing patterns on municipal level in Bosnia and Herzegovina. The Elbow method, Calinski-
Harabasz pseudo F-statistic, and Tukey`s Honestly Significant Difference test will be used to validate
the number of clusters and determine differences between ageing clusters. This approach offers a new
perspective in demographic research in Bosnia and Herzegovina by applying a non-hierarchical
multivariate statistical method to determine spatial disparities in ageing and the effect of spatial
factors on the ageing process. Accordingly, the study addresses the following questions: Are there
areas in Bosnia and Herzegovina particularly vulnerable to adverse population ageing? Which regions
and municipalities are experiencing the highest levels and rates of ageing? Is there an evident
clustering of the ageing process? What are the potential causes of the current spatial distribution of
ageing? What are the next steps in spatial demographic development of the country? What
implications do spatial disparities have for regional development in Bosnia and Herzegovina?
By addressing these questions, this study contributes to the understanding of a spatial aspect of
population ageing in Bosnia and Herzegovina. The findings of the study can be useful for spatial and
strategic planning of regional development in Bosnia and Herzegovina, supporting the development of
measures to address economic and social issues caused by the ageing process, particularly in regions
affected by adverse population ageing.
2. THEORETICAL BACKGROUND: SPATIAL DEMOGRAPHY AND CLUSTERING APPROACHES
TO POPULATION AGEING ANALYSIS
Population ageing research has experienced a theoretical expansion, moving beyond analyses
at the national level. As a result, numerous studies explore spatial and regional differences in this
demographic phenomenon (Yakar & Özgür, 2024; Zhang et al., 2022). Indicators of the ageing process,
such as the ageing coefficient, ageing index, dependency ratio, average age, etc., provide insight into
the intensity of population ageing (Harasty & Ostermeier, 2020; Nejašmić, 2005; Wu et al., 2021).
Spatial-demographic research indicates that the geographic distribution of these ageing indicators is
not uniform, and that the ageing process is a complex demographic process shaped by physical and
social features of the geographic environment, natural population change, migration processes, socio-
economic, and other factors (Káčerová et al., 2014; Li et al., 2019).
In Bosnia and Herzegovina, the level of population ageing is also influenced by geographic
location and exhibits spatially uneven patterns. Research by Kadušić et al. (2023a) showed that certain
municipalities in the western, northwestern, central, and northeastern regions of the country
demonstrate clustering in terms of the value of the ageing index and the ageing coefficient, and
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
4
suggests that geographical factors play a key role in the ageing process and adverse demographic
trends. Similarly, the research of Gekić et al. (2019) identified a pronounced demographic polarization
between urban and rural municipalities in Bosnia and Herzegovina. In addition, the peripheral and
border areas of Bosnia and Herzegovina are faced with a more intensive ageing process, higher
emigration rate, and less favourable socio-economic conditions (Avdic & Avdic, 2023; Avdic et al.,
2022). Therefore, these findings indicate that the process of population ageing in Bosnia and
Herzegovina is not random but spatially determined, with clear grouping patterns influenced by
geographic and socio-economic factors.
The concept of spatially uneven population ageing is present and widely researched in
numerous countries of the world. In Turkey, Yakar & Özgür (2024) used data on the total population,
age and gender features of the population, birth rates, and migration to separate regions concerning
the intensity of ageing. In Japan, Inoue & Inoue (2024) introduced the concept of "stages of population
ageing" using the data of the elderly population proportion and the elderly population change index at
the municipal level to determine ageing clusters. These studies have shown that advanced techniques
of spatial analysis and spatial statistics can reveal hidden demographic structures and patterns, and
indicate early signs of negative demographic trends in potentially threatened regions.
One of the more effective methods for identifying spatial patterns of demographic phenomena
is cluster analysis, which groups spatial units based on the similarity of data of selected variables
(Kastreva & Patarchanova, 2021), while in the social sciences, it is recognized as an effective method
for classifying territorial units according to the values of demographic characteristics (Rašić-Bakarić,
2007). Among cluster analysis techniques, k-means clustering is a machine learning algorithm
particularly popular due to its simplicity, efficiency, and interpretability of results (Ahmed et al., 2020).
It is a non-hierarchical multivariate statistical method that groups data into a certain number of
clusters so that values in each cluster are more similar than values in other clusters (Hassan et al.,
2021).
The number of studies in which k-means clustering was used indicates the relevance of this
technique in demographic research. Abbas et al. (2020) indicated the effectiveness of the k-means
algorithm in identifying spatial patterns of fertility, which is indirectly related to the future dynamics
of population ageing in Muzaffarabad (Kashmir). Ismail et al. (2016) applied the same technique to
estimate the demographic transition in Malaysia, highlighting its simplicity and effectiveness in
analyzing demographic data. In particular, the studies of Yakar and Özgür (2024) and Inoue and Inoue
(2024) should be highlighted, which indicated the effectiveness of the method in determining different
levels of ageing from a spatial aspect. The k-means clustering approach allows, not only mapping the
proportion of the old population, but also the understanding of factors that cause spatial patterns of
ageing.
However, the efficiency of k-means clustering largely depends on the initial selection of the
centroid and the predetermined number of clusters, and the selection of the appropriate number of
clusters is crucial for achieving optimal clustering results (Hassan et al., 2021; Matsuga & Sheremet,
2023). Elbow method, Calinski-Harabasz pseudo F-statistic and Tukey's Honestly Significant
Difference test are techniques that can be used to determine the optimal number of clusters and their
validation (Hassan et al., 2021; Wang & Xu, 2019). The mentioned techniques reduce the subjectivity
of the analysis and improve the reliability of data grouping, but they can be sensitive to data
distribution, number of clusters, outliers, and other factors.
Spatial-demographic research provides a framework for understanding the spatial dimension
of demographic processes. The integration of the spatial aspect with the methods of spatial statistics
and cluster analysis enables the determination of complex patterns of demographic development in
the research area. Complex administrative arrangements, and complex political, economic, and social
conditions in Bosnia and Herzegovina contribute to uneven regional development. Therefore, the
results of spatial-demographic research can serve as a basis for creating evidence-based strategies for
demographic development of Bosnia and Herzegovina.
3. DATA AND METHODS
According to Anselin (2005), k-means is a clustering algorithm used in data science and machine
learning that classifies data into k-distinct clusters based on feature similarities. It is a multivariate
non-hierarchical statistical method used for determining groups or clusters by minimizing within-
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
5
cluster distance and maximizing between-cluster distance (Fahmiyah & Ningrum, 2023). An important
step in k-means clustering is the identification of the optimal number of clusters. Different approaches
can be used, e.g., the rule of thumb, elbow method, information criterion approach, information-
theoretic approach, silhouette method, cross-validation, etc. (Kodinariya & Makwana, 2013).
To understand complex demographic patterns of population ageing in Bosnia and Herzegovina
k-means clustering was applied. K-means clustering enables the identification of trends and
characteristics that are not apparent when using analytical methods. As stated by Inoue and Inoue,
(2024) cluster analysis helps define demographically homogenous groups of ageing by dividing larger
areas into smaller and uniform clusters. This way it is easier to understand specific characteristics and
trends of population ageing. Moreover, k-means clustering can be performed on multiple indicators or
variables, which allows more comprehensive analysis considering various dimensions of the data.
Therefore, k-means clustering was specifically chosen for this study because multiple
demographic indicators of ageing can be used in the analysis. It enables the identification of regions
with varying levels of ageing allowing comparison between clusters to identify potential causes or
factors of the uneven ageing process and complex spatial relationship between demographic
processes. Furthermore, it is one of the more practical ways to identify municipalities with extreme
ageing trends, while the results of the analysis can be useful to policymakers. However, Chong (2021)
and Ahmed et al. (2020) noted that k-means clustering has certain limitations, including the necessity
of the pre-defined number of clusters, sensitivity to initial positions of centroids, outliers can affect
centroids and skew clusters, etc.
The quantitative and exploratory study of spatial disparities of population ageing in Bosnia and
Herzegovina involves statistical data from publications and sources of statistical agencies in this
country. Ageing coefficient, ageing index, dependency ratio, old-age dependency ratio, and average age
were used as indicators of demographic ageing. The ageing coefficient is a share of the population aged
65 and over in the total population, and ageing index is the ratio of the population older than 65 years
per 100 young people (0 to 14 years of age). Young-age dependency ratio can be defined as the number
of young people (0-14 years) per 100 people of working age, the old-age dependency ratio as the
number of people 65 years or older per 100 people of working age, while the sum of both indicators is
defined as a total dependency ratio (Harasty & Ostermeier, 2020; Pekarek, 2018). The average age is
the arithmetic mean of age within a specific population (Nejašmić, 2005). Data for mentioned
indicators were analysed for the period 2013-2023 at the municipal level, and collected from the
Agency for Statistics of Bosnia and Herzegovina (BHAS), Institute for Statistics of the Federation of
Bosnia and Herzegovina (FZS), and the Republic of Srpska Institute of Statistics (RZSRS). Using official
data ensured the reliability and validity of the analysis.
The first step of the analysis was cleaning and preparing data for cluster analysis by removing
errors, inconsistencies in the data, and outliers (fig. 1). Bosnia and Herzegovina has several
municipalities with a very small population, which affected ageing indicators indexes and the cluster
analysis. Therefore, a winsorization of the data was performed after the initial clustering of the data.
Winsorization is a statistical technique used to limit the influence of extreme values or outliers on the
statistical analysis of the data (Hamadani et al., 2021).
Fig. 1 – Research methodology phases. Colour figure available online.
Fig. 1 – Fases da metodologia da pesquisa. Figura a cores disponível online.
Source: Authors’ elaboration
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
6
To enhance data accuracy, extreme values in the dataset were replaced with 1
st
and 99
th
percentile. Values below the 1
st
percentile were replaced with that percentile value, and values above
the 99
th
percentile were set to that upper percentile value.
The second step of the analysis was standardization of the data which is necessary for effective
cluster analysis and significantly affects the identification of clusters (Nogueira & Munita, 2020, 2021).
Z-score standardization is a statistical technique used to transform the data into a standardized normal
distribution (Al-Mekhlafi et al., 2024), and it is applied in this study to manage diverse scales of ageing
indicators to ensure that indicators equally contribute to the distance calculations used in k-means
clustering algorithm. Therefore, standardization equalizes the influence of variables with different
units and scales, preserves the distance structure, and maintains the distribution of the data.
The next step in the analysis was cluster validation since k-means clustering requires a
predefined number of clusters (fig. 1). The Elbow method, Calinski-Harabasz pseudo F-statistic, and
Tukey`s Honestly Significant Difference (HSD) test were used as the statistical validation techniques
to determine the optimal number of clusters. The analysis was performed in GeoDa and SPSS by testing
various alternatives between 2 and 5 clusters. Although the Elbow method has certain limitations and
can be subjective in determining the optimal number of clusters (Morissette & Chartier, 2013), it is a
widely utilized technique for determining optimal number of clusters in k-means clustering by
identifying the point where the sum of squared distances between points and cluster centroids shows
no significant decrease (Marisa et al., 2023; Matsuga & Sheremet, 2023).
The Elbow method can be applied by plotting the sum of squared errors (SSE) against the
number of clusters (k) and identifying the point where the SSE starts to decrease (Fahmiyah &
Ningrum, 2023; Hassan et al., 2021). Calinski-Harabasz pseudo F-statistic or Calinski-Harabasz Index
can be useful in identifying the optimal number of clusters, especially when combined with other
techniques (Wang & Xu, 2019; Zhang & Li, 2013). It is a measure used in cluster analysis to identify the
significance and quality of clusters by comparing the ratio of between-cluster dispersion (how
separated identified clusters are) to within-cluster dispersion (how similar or close data points are
within a cluster), and higher values of F-statistic indicate more distinct and well-separated clusters
(Ashari et al., 2023; Hassan et al., 2021).
After the identification of the optimal number of clusters, a post-hoc analysis was conducted
using Tukey's Honestly Significant Difference (HSD) test to validate the difference between cluster
means (Wang, 2024). Tukey's HSD test is a post-hoc statistical test used to make pairwise comparisons
between means of different clusters after the analysis of variance is performed, and the assumption of
equal variances is met. According to Yakar and Özgür (2024), this test is used after the analysis of
variance indicated that there are significant differences between clusters’ means. This test calculates
the difference between each pair of means and compares it to a critical value that is based on the
number of clusters, the total number of observations, and the desired confidence level. This test is
useful in determining how identified clusters are significantly different from one another, and
validating the relationship and findings in data by confirming that the results are statistically
significant and not the result of random chance.
The presented methodological framework ensures the reliability of the research findings and
provides a foundation for future research analysis of population ageing. The results of the clustering
will be visualized in GeoDa, QGIS, and SPSS using maps, charts, and tables allowing for a clearer
understanding of the ageing spatial patterns in Bosnia and Herzegovina and the application of k-means
clustering in the analysis of population ageing.
4. RESULTS
4.1. Demographic Characteristics of Bosnia and Herzegovina
Bosnia and Herzegovina is a Southeast European country with access to the Adriatic Sea along
a 21.2 kilometres coastal stretch in the area of the Neum Bay. Its main administrative, educational,
political and healthcare centre is the capital city of Sarajevo. Bosnia and Herzegovina represents a
unique case within the European space, having undergone profound and multidimensional
transformations over the past three decades.
The dissolution of Yugoslavia and the subsequent war from 1992 to 1995 acted as both direct
and indirect catalysts for significant shifts in the country's demographic and socioeconomic
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
7
development. This period was marked by an induced demographic transition, resulting in persistently
low and, more recently, negative natural population change rates, as well as an intensive wave of
emigration, particularly pronounced in the recent period. The transition from a planned to a market-
based economy has brought numerous structural challenges. Bosnia and Herzegovina’s current
development is further shaped by complex European integration processes, the limited functionality
of its administrative-territorial organization and socioeconomic spatial disparities, all of which make
it a compelling subject for demographic and socioeconomic analysis.
With a Gross Domestic Product (GDP) per capita of approximately 7500 USD in 2022 (Agency
for Statistics of Bosnia and Herzegovina [BHAS], 2023), the country is categorized as upper middle
income; however, ongoing issues such as high youth unemployment, depopulation and population
ageing, as well as the pressing need for the reorganization of health and education infrastructure, pose
the relevance of such research for much needed policymaking measures. Analysing spatial disparities
in population ageing contributes not only to the national planning agenda but also offers significant
insight for broader European strategies aimed at eliminating the asymmetric impacts of demographic
ageing across regions with diverse historical, political and institutional legacies.
Bosnia and Herzegovina has been witnessing significant demographic changes in the first
decades of the 21st century (Avdić et al., 2022; Gekic et al., 2020). Those changes are characterized by
decreasing birth rates, increasing death rates, emigration of the young population, and intensive
population ageing (Gekić et al., 2019; Kadušić et al., 2023b). A primary cause of population ageing in
numerous world countries is a significant decline in fertility rates, which are below replacement levels,
and the increase in life expectancy, caused by improvements in healthcare and living conditions (Li et
al., 2019; Zhang et al., 2022). The analysis revealed similar demographic trends in Bosnia and
Herzegovina in the period 2013-2023. In this period in Bosnia and Herzegovina, fertility rates
continued decreasing from 37.5‰ to 34.9‰, mortality rates increased from 10.1‰ to 10.5‰, and
average age increased from 39.6 to 42.5 years (table I).
Table I – Basic demographic indicators in Bosnia and Herzegovina in 2013 and 2023.
Quadro I – Indicadores demográficos básicos na Bósnia e Herzegovina em 2013 e 2023.
Demographic indicator
2013
2023
Birth rate (‰)
8.7
7.7
Mortality rate (‰)
10.1
10.5
Natural population change (‰)
-1.4
-2.7
Fertility rate (‰)
37.5
34.9
Total fertility rate (TFR)
1.276
1.182
Source: Authors’ elaboration based on BHAS, FZS, RZSRS (2013-2024)
The data presented in table I indicate that the birth rate has been declining, while the mortality
rate has been increasing in the period from 2013 to 2023. The negative rate of natural population
change ranged from -1.4‰ to -2.7‰, the total fertility rate declined from 1.276 to 1.182, while the
average age of mothers at the birth of their first child rose from 26.69 to 28.13 years.
According to Gekić et al. (2019), the demographic development of Bosnia and Herzegovina in
the early 21st century is marked by depopulation and adverse population ageing, accompanied by
pronounced spatial, regional, and urban-rural polarization. Population ageing is a demographic
process with significant spatial disparities. As observed by Basile et al. (2023) population ageing varies
across regions, while Yakar and Özgür (2024) noted that urban and rural areas very often exhibit
different ageing patterns, and according to Wan et al. (2022) findings, spatiotemporal differences in
population ageing are mostly caused by socio-economic and environmental factors.
Descriptive statistical analysis of ageing indicators pointed to adverse demographic ageing
trends in Bosnia and Herzegovina during the study period. Table II presents the data on basic ageing
indicators in Bosnia and Herzegovina in a period from 2013 to 2023.
A notable trend is that all ageing indicators show a consistent increase in this period. Ageing
coefficient increased from 14.4% to 19.2%, ageing index from 93.5% to 141.8%, the dependency ratio
from 42.5% to 48.5%, the old-age dependency ratio from 20.5% to 28.4%, and the average age
increased from 39.6 to 42.5 years, indicating advanced demographic ageing at the national level,
highlighting the increase in the share of the elderly in the total population of Bosnia and Herzegovina.
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
8
To visualize the changes in the ageing process in Bosnia and Herzegovina, data for three key
ageing indicators (ageing coefficient, ageing index and dependency ratio) are classified in QGIS into
three groups (fig. 2, fig. 3 and fig. 4).
Table II – Ageing coefficient, ageing index, dependency ratio, old-age dependency ratio and average age in Bosnia and
Herzegovina (2013-2023).
Quadro II – Coeficiente de envelhecimento, índice de envelhecimento, taxa de dependência, taxa de dependência dos
idosos e idade média na Bósnia e Herzegovina (2013-2023).
Year
Ageing
coefficient
Ageing
index
Dependency
ratio
Old-age
dependency
ratio
Average
age
2013
14.4
93.5
42.5
20.5
39.6
2014
14.8
97.7
42.7
21.1
39.9
2015
15.3
101.8
43.1
21.7
40.3
2016
15.3
105.9
43.4
22.3
40.6
2017
16.0
110.3
43.9
23.0
40.8
2018
16.6
115.5
44.9
24.1
41.1
2019
17.2
120.5
45.8
25.0
41.4
2020
17.8
126.5
46.8
26.1
41.7
2021
18.1
130.2
47.2
26.7
41.9
2022
18.6
135.1
47.8
27.5
42.2
2023
19.2
141.8
48.5
28.4
42.5
Source: Authors’ elaboration based on BHAS, FZS, RZSRS (2013-2024)
Classification of ageing coefficient, ageing index, and dependency ratio values suggest significant
changes in ageing in period from 2013 to 2023 (figs. 2, 3 and 4). It is visible that ageing is present in a
large part of Bosnia and Herzegovina, and significantly varies across time and space. Data visualized
in figure 2 suggest that most of the municipalities of Bosnia and Herzegovina have reached the
moderate and advanced stage of ageing by 2023, and only a few municipalities have an early-stage
ageing coefficient, which varies between 7.3% and 14.6%. The value of ageing index also indicates
highly adverse demographic trends in Bosnia and Herzegovina, as well as spatial disparities in
demographic development.
Fig. 2 – Spatial distribution of ageing coefficient in Bosnia and Herzegovina, 2013 and 2023. Colour figure available
online.
Fig. 2 – Distribuição espacial do coeficiente de envelhecimento na Bósnia e Herzegovina, 2013 e 2023. Figura a cores
disponível online.
Source: Authors’ elaboration
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
9
Figure 3 shows that the value of this index exceeds 100% in many municipalities across the
country. Data presented in figure 4 indicate that the dependency ratio increased in a large number of
municipalities of Bosnia and Herzegovina in the researched period. All mentioned ageing indicators
reveal temporal and spatial changes in the ageing process across Bosnia and Herzegovina in the period
from 2013 to 2023. Over researched period, numerous municipalities have transitioned to moderate
or advanced stages of ageing, with notable spatial differences.
Fig. 3 – Spatial distribution of ageing index in Bosnia and Herzegovina, 2013 and 2023. Colour figure available online.
Fig. 3 – Distribuição espacial do índice de envelhecimento na Bósnia e Herzegovina, 2013 e 2023. Figura a cores
disponível online.
Source: Authors’ elaboration
Fig. 4 – Spatial distribution of dependency ratio in Bosnia and Herzegovina, 2013 and 2023. Colour figure available
online.
Fig. 4 – Distribuição espacial da taxa de dependência na Bósnia e Herzegovina, 2013 e 2023. Figura a cores disponível
online.
Source: Authors’ elaboration
4.2. Cluster Analysis of Population Ageing in Bosnia and Herzegovina
Performed cluster analysis, with the following statistical validation techniques, indicated
several interesting observations and outcomes related to spatial patterns of population ageing during
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
10
the study period in Bosnia and Herzegovina. To determine the optimal number of clusters with
different levels of ageing in the Bosnia and Herzegovina Elbow method, the Calinski-Harabasz F-
pseudo statistic, and Tukey`s Honestly Significant Difference (HSD) test were applied. To define the
optimal number of ageing clusters in Bosnia and Herzegovina SSE was determined for 2k (324.28), 3k
(215.18), 4k (164.71), and 5k (130.26). Therefore, the optimal ageing clusters in Bosnia and
Herzegovina are three. However, according to Inoue and Inoue (2024) and Hassan et al. (2021) one of
the challenges of the Elbow method is the difficulty in precisely identifying the elbow point. Therefore,
additional statistical tests, Calinski-Harabasz pseudo F-statistic and Tukey`s HSD test were performed
to validate identified three ageing clusters. Calinski-Harabasz pseudo F-statistic was determined for
every variable or ageing indicator in the defined three clusters.
Calinski-Harabasz Index is used to assess the quality of clustering by measuring the ratio of the
sum between-cluster dispersion to within-cluster dispersion (Wang, 2024). It assesses the quality of
the clustering by evaluating how well identified clusters are separated, and how compact data within
clusters are. Higher values of the Calinski-Harabasz pseudo F-statistic indicate better clustering with
well-separated and compact clusters.
The F-values presented in table III are all relatively high and indicate significant between-cluster
variance compared to within-cluster variance. This confirms that the identified ageing clusters are well
separated. Variables with higher F-values (ageing coefficient, old-age dependency ratio, and average
age) contribute more to the separation of clusters than those variables with lower F-statistic (ageing
index and dependency ratio). Moreover, p-values for all ageing indicators are <0.05, which confirms
that the differences in means between clusters for each ageing indicator are statistically significant.
Table III – Calinski-Harabasz pseudo F-statistic for selected ageing indicators in Bosnia and Herzegovina, 2023.
Quadro III – Pseudo-estatística F de Calinski-Harabasz para Indicadores de Envelhecimento Selecionados na Bósnia e
Herzegovina, 2023.
Variable
Cluster
Error
F
Sig.
Mean Square
df
Mean Square
df
Ageing coefficient
57.160
2
.198
140
289.108
.000
Ageing index
41.773
2
.418
140
100.049
.000
Dependency ratio
41.479
2
.422
140
98.355
.000
Old-age dependency ratio
56.196
2
.211
140
265.714
.000
Average age
50.773
2
.289
140
175.716
.000
Source: Authors’ elaboration
After analysis of variance has been performed and F-statistic was determined, the post-hoc
Tukey's Honestly Significant Difference (HSD) test was applied, given that the assumption of equal
variances was met (table III). Post-hoc test results presented in table IV confirm that the absolute
difference between clustersʼ means is greater than a critical value, and consequently, the difference
between clusters can be considered statistically significant. Pairwise comparisons between clusters
show statistically significant differences in population ageing indicators at the 0.01 level. These results
confirm distinct clusters with significant differences in ageing variables between all pairs of clusters.
The results of k-means clustering and the results of performed cluster validation tests indicate
that the clustering was effective in capturing ageing patterns, and identified clusters are valid and
useful for further population ageing analysis in Bosnia and Herzegovina. Visualization of clusters
performed in GeoDa and QGIS indicates three distinct clusters with different levels of population
ageing in Bosnia and Herzegovina.
Figure 5 shows three ageing clusters in Bosnia and Herzegovina with different levels of ageing.
Cluster 1 represents the highest values across all variables or advanced levels of ageing. Cluster 2
represents the lowest values across all ageing indicators or municipalities with a lower stage of ageing.
Cluster 3 represents medium values across all ageing indicators or municipalities with a moderate
level of ageing. The ratio between the cluster sum of squares (494.825) to the total sum of squares
(710) is 0.6969, which indicates that about 69.69% of the variance in the data is explained by the
performed clustering.
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
11
Fig. 5 – Ageing clusters in Bosnia and Herzegovina, 2023. Colour figure available online.
Fig. 5 – Clusters de envelhecimento na Bósnia e Herzegovina, 2023. Figura a cores disponível online.
Source: Authors’ elaboration
Table IV – Tukey`s Honestly Significant Difference (HSD) test for ageing indicators in Bosnia and Herzegovina, 2023.
Quadro IV – Teste de Diferença Honestamente Significativa (HSD) de Tukey para Indicadores de Envelhecimento, 2023.
Dependent
Variable
(I) Cluster
Number of Case
(J) Cluster
Number of Case
Mean Difference
(I-J)
Std. Error
Sig.
99% Confidence Interval
Lower Bound
Upper Bound
Ageing
coefficient
1
2
2.77167552
*
.12893786
.000
2.3898064
3.1535446
3
1.51414493
*
.13444899
.000
1.1159538
1.9123361
2
1
-2.77167552
*
.12893786
.000
-3.1535446
-2.3898064
3
-1.25753060
*
.08035499
.000
-1.4955141
-1.0195470
3
1
-1.51414493
*
.13444899
.000
-1.9123361
-1.1159538
2
1.25753060
*
.08035499
.000
1.0195470
1.4955141
Ageing
index
1
2
2.61735020
*
.18737233
.000
2.0624185
3.1722819
3
1.97062270
*
.19538108
.000
1.3919719
2.5492735
2
1
-2.61735020
*
.18737233
.000
-3.1722819
-2.0624185
3
-.64672750
*
.11677177
.000
-.9925649
-.3008901
3
1
-1.97062270
*
.19538108
.000
-2.5492735
-1.3919719
2
.64672750
*
.11677177
.000
.3008901
.9925649
Dependency
ratio
1
2
2.28709437
*
.18831284
.000
1.7293772
2.8448115
3
1.14395877
*
.19636180
.000
.5624034
1.7255141
2
1
-2.28709437
*
.18831284
.000
-2.8448115
-1.7293772
3
-1.14313560
*
.11735790
.000
-1.4907089
-.7955623
3
1
-1.14395877
*
.19636180
.000
-1.7255141
-.5624034
2
1.14313560
*
.11735790
.000
.7955623
1.4907089
Old-age
dependency
ratio
1
2
2.78878816
*
.13335480
.000
2.3938376
3.1837387
3
1.58668744
*
.13905471
.000
1.1748557
1.9985191
2
1
-2.78878816
*
.13335480
.000
-3.1837387
-2.3938376
3
-1.20210072
*
.08310766
.000
-1.4482367
-.9559647
3
1
-1.58668744
*
.13905471
.000
-1.9985191
-1.1748557
2
1.20210072
*
.08310766
.000
.9559647
1.4482367
Average
age
1
2
2.69469286
*
.15587505
.000
2.2330452
3.1563406
3
1.60554375
*
.16253753
.000
1.1241641
2.0869234
2
1
-2.69469286
*
.15587505
.000
-3.1563406
-2.2330452
3
-1.08914911
*
.09714244
.000
-1.3768512
-.8014470
3
1
-1.60554375
*
.16253753
.000
-2.0869234
-1.1241641
2
1.08914911
*
.09714244
.000
.8014470
1.3768512
Source: Authors’ elaboration. *. The mean difference is significant at the 0.01 level
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
12
These results suggest moderate to good clustering results since almost 70% of the variability of
the data can be explained by the differences between clusters, while the remaining 30% is the
variability due to differences within the clusters. This indicates well-separated and effective clusters
that capture a substantial portion of the total variance in the data.
It can be seen from the data in table V that the performed clustering reported significantly
different ageing levels in identified clusters. In Cluster 1 ageing coefficient varies from 29.3 to 38.6%,
the ageing index from 252.5 to 799.0%, the dependency ratio from 53.1 to 90.0%, the old-age
dependency ratio from 44.9 to 72.9%, and the average age from 48.7 to 54.7 years. This cluster consists
mostly of rural municipalities with small populations, which have the most adverse population ageing
trends or advanced ageing. Cluster 1 comprises 14 cases, and is the least represented ageing cluster
which includes municipalities Bosansko Grahovo, Donji Žabar, Drvar, Istočni Mostar, Istočni Stari Grad,
Glamoč, Kalinovik, Kupres-RS, Novo Goražde, Pelagićevo, Petrovac, Ravno, Rudo and Trnovo-RS.
Table V – Descriptive statistics of ageing indicators by identified ageing clusters in Bosnia and Herzegovina, 2023.
Quadro V – Estatísticas Descritivas dos Indicadores de Envelhecimento por Grupos de Envelhecimento Identificados na
Bósnia e Herzegovina, 2023.
Cluster Number of Case
Ageing
coefficient
Ageing
index
Dependency
ratio
Old-age
dependency ratio
Average
age
Cluster 1
Higher
ageing
Mean
33.1
517.6
69.7
56.3
51.2
N
14
14
14
14
14
Std. Deviation
2.8262
185.0829
12.8918
8.6885
1.9544
Sum
462.6
7246.5
975.1
787.7
716.5
Minimum
29.3
252.5
53.1
44.9
48.7
Maximum
38.6
799.0
90.0
72.9
54.7
Cluster 2
Lower
ageing
Mean
17.8
145.4
44.5
25.8
42.1
N
79
79
79
79
79
Std. Deviation
2.4652
46.4090
5.2802
4.1064
1.8609
Sum
1405.1
11484.2
3514.1
2035.9
3326.9
Minimum
12.8
77.9
32.9
17.4
38.4
Maximum
21.7
295.8
57.3
32.7
45.9
Cluster 3
Moderate
ageing
Mean
24.7
237.4
57.1
38.9
45.8
N
50
50
50
50
50
Std. Deviation
2.3091
107.7602
7.5785
5.0382
1.6784
Sum
1235.5
11867.4
2853.1
1945.8
2288.8
Minimum
21.0
141.5
32.9
27.8
42.7
Maximum
28.9
799.0
72.7
49.2
49.2
Source: Authors’ elaboration
On the other side, Cluster 2 includes municipalities with the lowest values of ageing indicators
or municipalities that are in the lower stadium of ageing. In Cluster 2 ageing coefficient varies from
12.8 to 21.7%, the ageing index from 77.9 to 295.8%, the dependency ratio from 32.9 to 57.3%, the
old-age dependency ratio from 17.4 to 32.7%, and the average age from 38.4 to 45.9 years. This cluster
consists large number of urban municipalities of Bosnia and Herzegovina, including Banja Luka, Bihać,
Bijeljina, Doboj, Gračanica, Gradačac, Mostar, Sarajevo, Široki Brijeg, Zenica etc. However, it is also
visible that the entire Bosnia and Herzegovina is facing adverse ageing trends, except for a very small
number of municipalities that have trends that are more favourable.
In many cases population ageing is associated with declining fertility rates, increasing life
expectancy, and emigration (Yakar & Özgür, 2024), and these have been important factors of
population ageing in Bosnia and Herzegovina in the last decades (Kadušić & Smajić, 2019; Kadušić et
al., 2015). As already stated, in Bosnia and Herzegovina fertility rates have decreased from 37.5‰ to
34.9‰ in researched period, and lowest fertility rates and highest average age correlate with the
values of ageing indicators in municipalities of Cluster 1 (Bosansko Grahovo, Donji Žabar, Pelagićevo,
Novo Goražde, Istočni Mostar itd.) (BHAS, 2024). Migration also plays a significant role in population
ageing in Bosnia and Herzegovina. According to data of Ministry of security of Bosnia and Herzegovina
in 2023 about 2777 persons emigrated from Bosnia and Herzegovina to foreign countries, out of which
85% of them emigrated to Croatia, Austria, Germany and Slovenia. Data of Institute for Statistics of the
Federation of Bosnia and Herzegovina indicate that on average about 3731 people emigrated from
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
13
Bosnia and Herzegovina every year in the period 2013 to 2023 (FZS [Federation of Bosnia and
Herzegovina], 2024). According to the same source most of the emigrants from the Federation of
Bosnia and Herzegovina, for example, are aged from 20 to 40 years (45.7%).
5. DISCUSSION
There is an increased interest in researching the phenomenon of population ageing, the causes
and consequences of this process at the global (Guerin et al., 2015; Li et al., 2019; Sabri et al., 2022)
and national level (Cristea et al., 2021; Jemna & David, 2021; Milena, 2022). Studies such as that
conducted by Li et al. (2019) and Bucher (2014) showed that ageing rates are growing consistently on
all continents, with Europe experiencing the highest pace of ageing.
Wan et al. (2022) findings reveal that socioeconomic and environmental factors are the main
cause of global ageing, with significant geographical disparities. Research have shown spatiotemporal
variation in population ageing (Li et al., 2019; Wan et al., 2022; Zhang et al., 2022), and a considerable
amount of literature has been published on spatial disparities of population ageing in many world
countries (Jukic & Khan, 2015; Reynaud et al., 2018; Wu et al., 2021).
Inoue and Inoue (2024) provided a quantitative analysis of Japan’s ageing population and
introduced a new concept of stages in the population ageing process, Kočanová et al. (2023) reported
relationships and patterns of ageing in European Union countries, and Chen et al. (2019) determined
spatial disparities in population ageing across various regions in China. All these studies provided
insights into various aspects of population ageing, including causes, consequences, and spatial
differences.
Various methods have been used to analyze spatial disparities in population ageing, each
offering unique insights and application (Kočanová et al., 2023; Zhang et al., 2022), and in recent years
k-means clustering has been applied in several studies on the demographic variables, including
population ageing (Inoue & Inoue, 2024; Yakar & Özgür, 2024). For instance, Yakar and Özgür (2024)
identified ageing regions in Turkey using k-means clustering.
The aforementioned studies indicate that population ageing is a demographic trend that affects
many world countries, including Bosnia and Herzegovina. Conducted population ageing studies in
Bosnia and Herzegovina by Gekić et al. (2019) and Kadušić et al. (2016) indicate a significant increase
of elderly in the total population. According to Pijalović et al. (2018) and Bošnjak (2016) elderly
population is expected to increase in the future which will put additional pressure on the pension
system, affecting economic growth and public spending. Since population ageing is a process with
pronounced spatial differences, this study aimed to determine spatial disparities in population ageing,
determine whether there is a grouping of population ageing data, record the municipalities that are in
the advanced stage of ageing, and identify factors of spatial differences in population ageing in Bosnia
and Herzegovina.
Previous studies of demographic development in Bosnia and Herzegovina have focused on
demographic trends in general, and only a few studies have treated the issue of population ageing
(Gekić et al., 2019; Kadušić et al., 2016), while Kadušić et al. (2023a) performed spatial analysis of
population ageing in Bosnia and Herzegovina using spatial autocorrelation method. Therefore, this
study aimed to provide a more comprehensive analysis of multiple ageing indicators using k-means
clustering. Since one of the shortcomings of the k-means clustering method is determining the optimal
number of clusters (Matsuga & Sheremet, 2023), the Elbow method is used for the identification of
clusters (Hassan et al., 2021), while Calinski-Harabasz pseudo F-statistic and Tukey`s Honestly
Significant Difference test were used to validate identified clusters (Fahmiyah & Ningrum, 2023;
Hassan et al., 2021).
The results of this study contradict the assumption that demographic ageing in Bosnia and
Herzegovina is uniform across different regions (Kadušić et al., 2016), unlike neighbouring Croatia
where the ageing process is more spatially consistent (Nejašmić & Toskić, 2013). This highlights the
need for targeted interventions to address spatial disparities of ageing in Bosnia and Herzegovina. The
clustering of data on ageing coefficient, the ageing index, dependency ratio, old-age dependency ratio,
and average age, for the period from 2013 to 2023, has revealed clusters with different values of
analyzed ageing indicators. The entire Bosnian population is facing significant ageing with different
levels of ageing across different areas of this country. Three clusters were identified with lower,
moderate, and advanced ageing. These clusters indicate areas where the population is ageing more
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
14
rapidly and the necessity for targeted policy interventions. As stated by Litra (2014) increase of the
elderly in the total population puts a growing demand on the healthcare and pension system, and
pressure on the public finances and the social system of a country. Since similar trends are evident in
Bosnia and Herzegovina, many studies indicate an urgent policy change to ensure adequate living
standards for the elderly and the economic stability of the country (Bošnjak, 2016; Pijalović et al.,
2018).
According to Avdic & Avdic (2023) and Kadušić et al. (2023b), spatial disparities in demographic
and socio-economic trends in Bosnia and Herzegovina are mostly caused by social, economic, and
political factors. The most significant factors of uneven demographic development of Bosnia and
Herzegovina are socio-economic factors (unemployment rate, unemployment rate of highly educated
population, net salary, number of inhabitants per doctor, migration, etc.), political-social factors
(political instability, social injustice, lack of trust in institutions, general feeling of insecurity),
psychological factors (attitude of younger generations towards marriage, family, personal and
professional ambitions, feminization of migration), administrative division of Bosnia and Herzegovina,
urban-rural polarization, etc. (Begović et al., 2020; Efendić, 2016; Jahić et al., 2024).
Adverse population ageing is particularly present in Cluster 1, or in rural municipalities that are
founded after the Dayton Peace Agreement, and are situated along the administrative entity border
within Bosnia and Herzegovina. However, this research identified an exception to this general rule.
Municipalities such as Tešanj, Teslić, Doboj, Doboj Jug, Doboj Istok, etc., despite being situated along
entity lines, have more favourable demographic ageing trends. This implies that socio-economic
factors like population size, human resources, unemployment rate, the unemployment rate of the
population with a university degree, net salary, number of inhabitants per medical doctor, etc., play a
significant role in demographic development in Bosnia and Herzegovina. Socioeconomic factors affect
the living standard of the population and cause the emigration of the younger population to highly
developed European countries (Efendic, 2016; Efendic et al., 2023; Lukić-Tanović & Marinković, 2024).
As stated by Kadušić et al. (2023b) and Gekic et al. (2020) emigration of young and educated
individuals aggravates the population ageing by reducing the proportion of the younger population.
Unfortunately, one of the limitations of this study is the availability of official statistical data on
the demographics of Bosnia and Herzegovina. The last population census in Bosnia and Herzegovina
took place in 2013, and consequently, there are no available demographic data on a settlement level.
To carry out the clustering of data on the ageing process in Bosnia and Herzegovina, data on the
municipal level were used, which contributes to the generalization of the data and can affect the
clustering results. Furthermore, it was not possible to perform a spatial analysis of emigration by
municipalities because there is no systematic and continuous monitoring of this demographic
phenomenon in all areas of Bosnia and Herzegovina. The Republic of Srpska Institute of Statistics does
not record and publish data on emigration by municipalities. However, existing data and recent studies
all point that emigration is a serious issue for the present and future demographic development of
Bosnia and Herzegovina (Avdić et al., 2022; Gekic et al., 2020).
Although data on emigration could not have been included in the analysis, the results obtained
by this study indicate significant spatial differences in demographic trends in Bosnia and Herzegovina,
including population ageing. This study is a novelty in a spatial approach to analyze multiple ageing
indicators and identify spatial disparities in demographic ageing in Bosnia and Herzegovina.
Furthermore, a 70% ratio of the between-cluster sum of squares to the total sum of squares indicates
that k-means clustering was effective in capturing the structure of the data on ageing indicators.
Although the applied methodology proved suitable for deriving clusters of demographic ageing,
it is important to emphasize that clustering reveals spatial patterns but does not quantify underlying
causes. Therefore, future research should, among other aspects, focus on identifying and analyzing the
factors driving such spatial manifestations. The partially limited spatial resolution may affect the
accuracy of classification, as municipal boundaries, used as the primary statistical units, constrain the
ability to capture intra-local variations in demographic ageing. Additionally, temporal limitations must
be acknowledged, arising from the absence of regular population censuses in Bosnia and Herzegovina
and the lack of an up-to-date population register.
To enhance the analytical framework of future studies on population ageing in Bosnia and
Herzegovina, several methodological refinements are proposed. One important direction involves the
incorporation of spatial contiguity into the clustering process. While k-means clustering effectively
groups units based on attribute similarity, it does not account for geographic proximity, which is an
important dimension in spatial demography. Therefore, the application of spatially constrained
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
15
hierarchical clustering (SCHC) methods, as proposed by Anselin (2020), could improve the
identification of demographically coherent and spatially contiguous clusters. Such an approach would
generate more policy-relevant regional delineations for spatial planning and targeted interventions.
In addition, the application of geographically weighted clustering could prove particularly valuable in
detecting local variations in ageing structures. Given the demographic complexity of Bosnia and
Herzegovina, where adjacent municipalities often exhibit vastly different demographic profiles due to
historical, socio-economic and administrative factors, this technique would allow for more localized
and context-sensitive analysis of ageing patterns.
When it comes to data preprocessing, future research should continue to employ techniques
such as winsorization or natural logarithm transformation to address the influence of outliers. These
outliers frequently correspond to small municipalities established in the post-war period through the
new administrative organization of the country. Due to their low population base and atypical
demographic structures, these municipalities pose specific challenges in spatial statistical analysis and
require detailed methodological approach. Ideally, such municipalities should be the focus of
separated demographic case studies to better understand their actual conditions.
It would be beneficial to compare the results of quantitative clustering with existing official
typologies or classifications, or to validate findings using qualitative methods. This would enhance the
interpretability and credibility of the identified clusters. Furthermore, the inclusion of data at lower
administrative levels, such as settlements, would greatly increase the resolution and analytical
precision of spatial ageing studies. However, due to the absence of disaggregated demographic data
after the 2013 census, such analyses remain limited. Additionally, the inclusion of migration data and
the implementation of comparative studies with neighboring countries would provide a broader
regional perspective, allowing for contextualization of demographic ageing trends within the wider
post-socialist and post-conflict spatial development of this part of Balkans.
The findings of the study are significant for future planning of demographic development in
Bosnia and Herzegovina since they point to the need to create and implement regional demographic
development strategies. In the coming period, it is necessary to define and implement measures of
population policy by applying various financial and social measures. This includes developing
programs for the revitalization of rural areas through investments in infrastructure, agriculture, and
other local economic activities that could reduce emigration. Improving working conditions and
reducing unemployment among young population, should be addressed through the creation of new
jobs and the implementation of programs supporting self-employment and entrepreneurship.
A strategy for return of Bosnian emigrants should be developed, incorporating tax incentives,
subsidies for starting new business and recognition of qualifications acquired abroad. Furthermore,
immigration should be promoted by creating policy that would attract immigrants to Bosnia and
Herzegovina through temporary or permanent residence. Bosnia and Herzegovina needs balanced
regional development strategies that consider the geographic and demographic specificities of each
region and municipality.
Therefore, in the forthcoming period it is necessary to conduct case studies and research spatial
demographic disparities through the systematic collection and analysis of data on demographic data
at regional and municipal level. These insights will serve as a foundation for formulating effective and
regionally responsive demographic development strategies.
6. CONCLUSIONS
This study emphasizes the use of k-means clustering in determining spatial disparities in
population ageing in Bosnia and Herzegovina providing valuable insights into demographic challenges
facing this country. The results of k-means clustering revealed three distinct clusters with different
levels of ageing, which correlate with underlying demographic, socioeconomic, and political factors.
Key socioeconomic factors, affecting this demographic process, include unemployment rate, the
unemployment rate among highly educated population, net salaries, etc. These are accompanied by
political and social factors such as political instability, social injustice, lack of trust in institutions, and
general feeling of insecurity. Psychological factors also play a significant role, especially the attitudes
of young generations towards marriage and family, personal and professional ambitions. These factors
aggravate rural-urban polarization contributing significantly to the uneven demographic development
of Bosnia and Herzegovina, causing disparities in population ageing. Uneven socio-economic
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
16
development in Bosnia and Herzegovina causes emigration of young population from underdeveloped
rural to developed urban areas, and the emigration of young people abroad.
Cluster analysis identified that municipalities in rural and less economically developed areas of
Bosnia and Herzegovina experience higher levels of ageing. These regions are characterized by
significant emigration and lower fertility rates, which aggravates the challenges of the pension and
healthcare system, putting pressure on the economic stability of the country. On the other hand,
municipalities with lower levels of ageing are mostly located in urban and economically more
developed areas, with larger share of young population and higher fertility rates.
Therefore, the k-means clustering approach proved to be an effective tool for identifying spatial
disparities in population ageing in Bosnia and Herzegovina. The classification of municipalities based
on ageing indicators enabled a better understanding of spatial differences in the ageing process.
Moreover, cluster analysis of ageing indicators provided a visualization of ageing disparities,
facilitating data interpretation for future strategists and spatial planners. Future research could
incorporate additional socioeconomic variables to gain a more comprehensive understanding of
factors that contribute to spatial disparities in demographic development and population ageing.
Future studies could also explore interdependencies between demographic and socioeconomic
development using advanced spatial statistical methods, such as geographically weighted clustering.
This approach would contribute to more precise and effective policy recommendations aimed at
addressing the challenges of population ageing in Bosnia and Herzegovina.
ACKNOWLEDGEMENTS
Preliminary research results of the study are presented at the 6th Congress of Geographers of Bosnia and
Herzegovina, Department of Geography, Faculty of Science, University of Sarajevo, Geographical Society of
FBiH, Sarajevo, 19th to 21st September, 2024.
AUTHOR CONTRIBUTIONS
Alma Kadušić: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation,
Resources, Data curation, Writing – original draft preparation, Writing – review and editing, Visualization,
Supervision. Mariana Lukić Tanović: Conceptualization, Methodology, Validation, Resources, Writing –
review and editing, Visualization, Supervision. Nedima Smajić: Resources, Writing – review and editing,
Visualization, Supervision. Aida Avdić: Resources, Writing – review and editing, Visualization, Supervision.
ORCID
Alma Kadušić
https://orchid.org/0000-0002-8363-6172
Mariana Lukić Tanović https://orchid.org/0009-0004-6202-1891
Nedima Smajić https://orchid.org/0009-0004-6618-1040
Aida Avdić https://orchid.org/0000-0001-6973-7201
REFERENCES
Abbas, S. A., Aslam, A., Rehman, A. U., Abbasi, W. A., Arif, S., & Kazmi, S. Z. H. (2020). K-means and k-medoids: Cluster
analysis on birth data collected in city Muzaffarabad, Kashmir. IEEE Access, 8, 151847-151855.
https://doi.org/10.1109/ACCESS.2020.3014021
Agency for Statistics of Bosnia and Herzegovina (2024). Demography 2023. Demographic Bulletin TB 02. Sarajevo:
Agency for statistics of Bosnia and Herzegovina.
https://bhas.gov.ba/data/Publikacije/Bilteni/2024/DEM_00_2023_TB_1_BS.pdf
Agency for statistics of Bosnia and Herzegovina (2023). Gross Domestic Product by Production, Income, and
Expenditure Approach. Thematic Bulletin TB 01. Sarajevo: Agency for statistics of Bosnia and Herzegovina.
https://bhas.gov.ba/data/Publikacije/Bilteni/2025/NAC_00_2023_TB_1_BS.pdf
Ahmadov, E.Y. (2023). Comparative analysis of k-means and fuzzy c-means algorithms on demographic data using the
PCA method. Problems of Information Technology, 14(1), 15-22. http://doi.org/10.25045/jpit.v14.i1.03
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
17
Ahmed, M., Seraj, R., & Islam, S.M.S. (2020). The k-means algorithm: A comprehensive survey and performance
evaluation. Electronics, 9(8), 1295.
https://doi.org/10.3390/electronics9081295
Al-Mekhlafi, A., Klawitter, S., & Klawonn, F. (2024). Standardization with zlog values improves exploratory data
analysis and machine learning for laboratory data. Journal of Laboratory Medicine, 48, 215-222.
https://doi.org/10.1515/labmed-2024-0051
Anselin, L. (2020). GeoDa Workbook: Spatial Clustering (Lab 9d). GeoDa Center for Geospatial Analysis and
Computation.
https://geodacenter.github.io/workbook/9d_spatial4/lab9d.html
Anselin, L. (2005). Exploring Spatial Data with GeoDaTM: A Workbook. Center for Spatially Integrated Social Science.
Ashari, I.F., Nugroho, E.D., Baraku, R., Yanda, I.N., & Liwardana, R. (2023). Analysis of elbow, silhouette, Davies-
Bouldin, Calinski-Harabasz, and rand-index evaluation on k-means algorithm for classifying flood-affected
areas in Jakarta. Journal of Applied Informatics and Computing, 7(1), 95-103.
https://doi.org/10.30871/jaic.v7i1.4947
Avdic, A., & Avdic, B. (2023). Center and Periphery in Bosnia and Herzegovina-Social and Spatial Indicators of
Regional Disparities. Folia Geographica, 65(2), 103-126.
https://www.foliageographica.sk/unipo/journals/2023-65-2/693/
Avdić, A., Avdić, B., & Zupanc, I. (2022). Socio-demographic analysis of border regions of Bosnia and Herzegovina. Acta
Geographica Slovenica, 62(3), 7-19.
https://doi.org/10.3986/AGS.10859
Basile, R., Basso, S., Miccoli, S., Reynaud, C. (2023). Ageing population in Italy: a space-time analysis. Rivista Italiana di
Economia Demografia e Statistica, LXXVI(2), 77-88.
https://www.rieds-journal.org/rieds/article/view/99/91
Begović, S., Lazović-Pita, L., Pijalović, V., & Baskot, B. (2020). An investigation of determinants of youth propensity to
emigrate from Bosnia and Herzegovina. Economic research-Ekonomska istraživanja, 33(1), 2574-2590.
https://doi.org/10.1080/1331677X.2020.1754267
Bošnjak, N. (2016). The Pension System of Bosnia and Herzegovina: Problems and Perspectives. European Journal of
Multidisciplinary studies, 1(2), 186-194.
https://doi.org/10.26417/ejms.v1i2.p186-194
Bucher, S. (2014). Selected indicators of population ageing in the world: Trends, impacts and consequences.
Geographica Pannonica, 18(2), 26-33.
https://scindeks-clanci.ceon.rs/data/pdf/0354-8724/2014/0354-
87241402026B.pdf
Chen, Y., Bouferguene, A., Shen, Y., & Al-Hussein, M. (2019). Difference analysis of regional population ageing from
temporal and spatial perspectives: a case study in China. Regional Studies, 53, 849-860.
https://doi.org/10.1080/00343404.2018.1492110
Chong, B. (2021). K-means clustering algorithm: a brief review. Academic Journal of Computing & Information Science,
4(5), 37-40.
https://doi.org/10.25236/AJCIS.2021.040506
Cristea, M., Noja, G.G., & Thalassinos, Y. (2021). Population ageing and welfare dissimilarities within the European
Union: a new approach based on cluster analysis. Facta Universitatis, Series: Economics and Organization, 18,
17-27. https://doi.org/10.22190/FUEO201226003C
Efendic, A. (2016). Emigration intentions in a post-conflict environment: evidence from Bosnia and Herzegovina. Post-
Communist Economies, 28, 335-352.
https://doi.org/10.1080/14631377.2016.1166800
Efendic, A., Husić-Mehmedović, M., & Turulja, L. (2023). Emigration in Bosnia and Herzegovina: Empirical Evidence
from the Last Two Decades. Central and Eastern European Migration Review, 12(2), 53-72.
https://doi.org/10.54667/ceemr.2023.18
Fahmiyah, I., Ningrum, R.A. (2023). Human Development Clustering in Indonesia: Using K-Means Method and Based
on Human Development Index Categories. Journal of Advanced Technology and Multidiscipline, 2(1), 27-33.
https://e-journal.unair.ac.id/JATM/article/view/45070
Federation of Bosnia and Herzegovina (2024). Population migration 2023. Statistical Bulletin 375. Institute for
statistics of FBiH.
https://fzs.ba/wp-content/uploads/2024/02/Migracije-2023.pdf
Gabor, V.R., Iftimoaei, C., & Baciu, I.C. (2022). Population Ageing. Romania in a European Context. Romanian Journal of
Population Studies, 16(2), 81-110.
https://doi.org/10.24193/RJPS.2022.2.04
Gekić, H., Bidžan-Gekić, A., Mirić, R., & Drešković, N. (2019). Spatial differentiation of the age structure of a population
in Bosnia and Herzegovina. In Macedonian Geographical Society (Ed.), Proceedings of International Scientific
Symposium New Trends Geography (pp. 215-226). October 3-4, 2019, Ohrid, Republic of North Macedonia.
https://doi.org/10.37658/procgeo19215g
Gekic, H., Bidzan-Gekic, A., Miric, R., & Reményi, P. (2020). Hidden geographies of population implosion in Bosnia and
Herzegovina. European Journal of Geography, 11, 47-64. https://doi.org/10.48088/ejg.h.gek.11.2.47.64
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
18
Gnjatovic, J.S., Devedzic, M. (2016). Certain characteristics of population ageing using a prospective approach: Serbia
as a case study. Vienna Yearbook of Population Research, 14, 89-106.
http://www.jstor.org/stable/26506084
Guerin, B., Hoorens, S., Khodyakov, D., & Yaqub, O. (2015). A growing and ageing population: Global societal trends to
2030. Thematic report 1. RAND Corporation.
https://www.rand.org/pubs/research_reports/RR920z1.html
Hamadani, A., Ganai, N.A., Raja, T., Alam, S., Andrabi, S.M., Hussain, I., & Ahmad, H.A. (2021). Outlier removal in sheep
farm datasets using winsorization. Bhartiya Krishi Anusandhan Patrika, 36(4), 334-337.
https://doi.org/10.18805/BKAP397
Harasty, C., & Ostermeier, M. (2020). Population ageing: Alternative measures of dependency and implications for the
future of work. ILO Working Paper No. 5. International Labour Organization.
Hassan, I.H., Abdullahi, M., & Ali, Y.S. (2021). Analysis of Techniques for Selecting Appropriate Number of Clusters in
K-means Clustering Algorithm. In Proceedings of the International Conference on Computing and Advances in
Information Technology (ICCAIT 2021) (pp. 90-96), November 15-17, 2021, Ahmadu Bello University, Zaria,
Nigeria.
https://iccait.com.ng/2021/
Inoue, T., & Inoue, N. (2024). The Future Process of Japan’s Population Aging: A Cluster Analysis Using Small Area
Population Projection Data. Population Research and Policy Review, 43, 58.
https://doi.org/10.1007/s11113-
024-09903-5
Ismail, K., Nayan, N., & Ibrahim, S.N. (2016). Improving the tool for analyzing Malaysia’s demographic change: Data
standardization analysis to form geo-demographics classification profiles using k-means algorithms. Malaysian
Journal of Society and Space, 12(6), 34-42. https://core.ac.uk/download/pdf/84306815.pdf
Jahić, H., Hadžiahmetović-Milišić, N., Dedović, L., & Kuloglija, E. (2024). Diagnosing the Issue: Understanding and
Combating Medical Brain Drain in Bosnia and Herzegovina. Naše gospodarstvo/Our economy, 70, 12-
https://doi.org/23. 10.2478/ngoe-2024-0008
Jakovljevic, M., Cerda, A.A., Liu, Y., García, L., Timofeyev, Y., Krstic, K., Fontanesi, J. (2021). Sustainability challenge of
Eastern Europe-historical legacy, belt and road initiative, population aging and migration. Sustainability,
13(19), 11038. https://doi.org/10.3390/su131911038
Jakovljevic, M., & Laaser, U. (2015). Population aging from 1950 to 2010 in seventeen transitional countries in the
wider region of South Eastern Europe. South Eastern European Journal of Public Health, 3(1), 1-12.
https://doi.org/10.12908/SEEJPH-2014-42
Jemna, D.-V., & David, M. (2021). Determinants of ageing in Romania. Evidence from regional-level panel analysis. CES
Working Papers, 13(1), 17-32.
https://ideas.repec.org/a/jes/wpaper/y2021v13i1p17-32.html
Jukic, M., & Khan, H.T. (2015). Spatial pattern of structural ageing in eastern Croatia: evolution and explanations.
International Journal of Ageing and Later Life (IJAL), 9(2), 53-78.
https://doi.org/10.3384/ijal.1652-
8670.2015_159253
Jurun, E., Ratković, N., & Ujević, I. (2017). A cluster analysis of Croatian counties as the base for an active demographic
policy. Croatian Operational Research Review, 8(1), 221-236.
https://doi.org/10.17535/crorr.2017.0014
Káčerová, M., Ondačková, J., & Mládek, J. (2014). Time-space differences of population ageing in Europe. Hungarian
Geographical Bulletin, 63(2), 177-199.
https://doi.org/10.15201/hungeobull.63.2.4
Kadušić, A., & Smajić, N. (2019). Primary-school-age population change in the Federation of Bosnia and Herzegovina:
The effects of demographic factors. Stanovnistvo, 57(2), 49-70.
https://doi.org/10.2298/STNV1902049K
Kadušić, A., Smajić, S., Kunosić, S., & Smajić, N. (2023a). Geospatial analysis of population ageing in Bosnia and
Herzegovina. Geographica Pannonica, 27(1), 38-49.
https://doi.org/10.5937/gp27-41960
Kadušić, A., Smajić, S., & Smajić, N. (2023b). The patterns of depopulation in Bosnia and Herzegovina: Spatial
autocorrelation analysis. Geografski Pregled, 48, 85-100.
https://doi.org/10.35666/23038950.2023.48.85
Kadušić, A., Suljić, A., & Mešanović, D. (2015). Fertility rates in Tešanj Municipality (Bosnia and Herzegovina) in the
second half of the 20th and the beginning of 21st century. Geographica Pannonica, 19, 50-57.
https://doi.org/10.5937/GeoPan1502050K
Kadušić, A., Suljić, A., & Smajić, N. (2016). The demographic ageing of population in Bosnia and Herzegovina: causes
and consequences. Revija za geografijo - Journal for Geography, 11(1), 41-52.
https://doi.org/10.18690/rg.11.1.3951
Kastreva, P., & Patarchanova, E. (2021). Creating Spatial Models of Demographic Processes Using Cluster Analysis for
Demographic Policy Planning in Bulgaria. Journal of Settlements & Spatial Planning, 12(2), 119-130.
https://doi.org/10.24193/JSSP.2021.2.05
Kočanová, D., Kováč, V., Serzhanov, V., & Buleca, J. (2023). Differences and similarities in patterns of ageing society in
the European Union. Economics and Management, 26(1), 45-64.
https://doi.org/10.15240/tul/001/2023-1-
003
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
19
Kodinariya, T.M., & Makwana, P.R. (2013). Review on determining number of Cluster in K-Means Clustering.
International Journal, 1(6), 90-95.
http://ijarcsms.com/docs/paper/volume1/issue6/V1I6-0015.pdf
Kong, F. (2018). Aging Trend of the World. In T. Hoshi & S. Kodama (Eds.), The Structure of Healthy Life Determinants.
International Perspectives on Aging, vol. 18. Springer.
https://doi.org/10.1007/978-981-10-6629-0_2
Krisjane, Z., Berzins, M., Krumins, J., Apsite-Berina, E., & Balode, S. (2023). Uneven geographies: ageing and population
dynamics in Latvia. Regional Science Policy & Practice, 15(4), 893-909.
https://doi.org/10.1111/rsp3.12648
Li, J., Han, X., Zhang, X., & Wang, S. (2019). Spatiotemporal evolution of global population ageing from 1960 to 2017.
BMC Public Health, 19, 127.
https://doi.org/10.1186/s12889-019-6465-2
Lillova, K. (2021). Population Ageing and Longevity in Bulgaria (1947-2015). Население, 39(2), 205-240.
https://www.ceeol.com/search/article-detail?id=995325
Litra, A.V. (2014). Economic effects of demographic ageing. Bulletin of the Transilvania University of Brasov. Series V:
Economic Sciences, 7(56), 2, 229-234.
https://webbut.unitbv.ro/index.php/Series_V/article/view/5005/3881
Lo, V. I. (2023). The Criss-Cross of Ageing and Globalisation. In U. Ghori, M. Hiscock, L. Parsons & C. Watters. (Eds.),
Globalisation in Transition (pp. 67-79). Springer.
https://doi.org/10.1007/978-981-99-2439-4_6
Lukić-Tanović, M., & Marinković, D. (2024). Population census in Bosnia and Herzegovina: Between political interests
and unfavorable demographic processes. Zbornik Matice srpske za drustvene nauke, 189, 53-67.
https://doi.org/10.2298/ZMSDN2489053L
Marisa, F., Wardhani, A.R., Purnomowati, W., Vitianingsih, A.V., Maukar, A.L., & Puspitarini, E.W. (2023). Potential
customer analysis using k-means with elbow method. Jurnal Informatika dan Komputer, 7, 307-312.
http://dx.doi.org/10.26798/jiko.v7i2.911
Matsuga, O.M., & Sheremet, V. S. (2023). Оцінювання оптимальної кількості кластерів для методу k-середніх на
основі кусково-лінійної регресії з одним вузлом [Determining the optimal number of clusters for the k-
means method using piecewise-linear regression with one changepoint]. Актуальні проблеми
автоматизації та інформаційних технологій, 27, 17-24.
http://dx.doi.org/10.15421/432302
Milena, S. (2022). Population ageing in selected ex-Yugoslav states. Zbornik radova Departmana za geografiju, turizam
i hotelijerstvo, 51(1), 1-10.
https://doi.org/10.5937/ZbDght2201001S
Ministry for security of Bosnia and Herzegovina, Sector for migration (2024). Migration profile of Bosnia and
Herzegovina for 2023. Sarajevo.
https://www.msb.gov.ba/PDF/160820244.pdf
Morissette, L., & Chartier, S. (2013). The k-means clustering technique: General considerations and implementation in
Mathematica. Tutorials in Quantitative Methods for Psychology, 9, 15-24.
https://doi.org/10.20982/tqmp.09.1.p015
Nejašmić, I. (2005). Demogeografija: stanovništvo u prostornim odnosima i procesima [Demogeography: Population in
Spatial Relations and Processes]. Školska knjiga.
Nejašmić, I., & Toskić, A. (2013). Population Ageing in Croatian Border Regions. Acta Geographica Croatica, 40, 1-13.
https://hrcak.srce.hr/file/216609
Nogueira, A.L., & Munita, C.S. (2021). The effect of data standardization in cluster analysis. Brazilian Journal of
Radiation Sciences, 9(1A), ENAN / 2019.
https://doi.org/10.15392/bjrs.v9i1A.1324
Nogueira, A.L., & Munita, C.S. (2020). Quantitative methods of standardization in cluster analysis: finding groups in
data. Journal of Radioanalytical and Nuclear Chemistry, 325, 719-724.
https://doi.org/10.1007/s10967-020-
07186-6
Pekarek, S. (2018). Population aging and economic dependency ratio: Comparative study of the Czech Republic and
Slovakia. Ecoforum, 7(1), 69-78.
https://ecoforumjournal.ro/index.php/eco/article/view/2084
Pijalović, V., Lazović-Pita, L., Peštek, A., & Martinović, D. (2018). The analysis of perceptions and attitudes related to
ageing in the Federation of Bosnia and Herzegovina. Public Sector Economics, 42, 255-278.
https://doi.org/10.3326/pse.42.3.2
Rašić-Bakarić, I. (2007). Uncovering regional disparities-the use of factor and cluster analysis. Croatian Economic
Survey, 9, 11-34.
https://hrcak.srce.hr/en/file/17906
Reynaud, C., Miccoli, S., & Lagona, F. (2018). Population Ageing in Italy: An Empirical Analysis of Change in the Ageing
Index Across Space and Time. Spatial Demography, 6, 235-251.
https://doi.org/10.1007/s40980-018-0043-6
Roszkowska, E. (2018). Comprehensive analysis of the process of population ageing in Poland in the years 1950-2016.
Optimum. Economic Studies, 92(2), 206-226.
https://doi.org/10.15290/oes.2018.02.92.16
Republic of Srpska Institute of Statistics (2024). Demographic statistics. Statistical Bulletin 2024. Republic of Srpska
Institute of statistics.
https://www.rzs.rs.ba/static/uploads/bilteni/stanovnistvo/BiltenDemografskaStatistika_2024_WEB.pdf
Kadušić, A., Tanović, M. L., Smajić, N., Avdić, A. Finisterra, LX(130), 2025, e39994
20
Sabri, S. M., Annuar, N., Rahman, N. L. A., Musairah, S. K., Mutalib, H. A., & Subagja, I. K. (2022). Major trends in ageing
population research: a bibliometric analysis from 2001 to 2021. Proceedings, 82(1), 19.
https://doi.org/10.3390/proceedings2022082019
Wan, Q., Tang, Z., Pan, J., Xie, M., Wang, S., Yin, H. … Song, C. (2022). Spatiotemporal heterogeneity in associations of
national population ageing with socioeconomic and environmental factors at the global scale. Journal of
Cleaner Production, 373, 133781. https://doi.org/10.1016/j.jclepro.2022.133781
Wang, X., & Xu, Y. (2019). An improved index for clustering validation based on Silhouette index and Calinski-
Harabasz index. IOP Conference Series: Materials Science and Engineering, 569, 052024.
https://doi.org/10.1088/1757-899X/569/5/052024
Wang, Y. (2024). Exploring the Application of Early Childhood Psychological Education Based on K-means Cluster.
Analysis. Journal of Electrical Systems, 20, 64-71.
https://journal.esrgroups.org/jes/article/view/4290/3136
Wu, L., Huang, Z., & Pan, Z. (2021). The spatiality and driving forces of population ageing in China. PLOS One, 16,
e0243559.
https://doi.org/10.1371/journal.pone.0243559
Yakar, M., & Özgür, E.M. (2024). Revealing ageing regions through spatial patterns: the evidence from Türkiye.
Finisterra – Revista Portuguesa de Geografia, LIX(125), 105-123.
https://doi.org/10.18055/Finis32411
Zhang, K., Sun, H., & Li, X. (2022). Aging population spatial distribution discrepancy and impacting factor.
Sustainability, 14(15), 9528.
https://doi.org/10.3390/su14159528
Zhang, Y., & Li, D. (2013). Cluster analysis by variance ratio criterion and firefly algorithm. International Journal of
Digital Content Technology and its Applications, 7(3), 689-697.
https://doi.org/10.4156/jdcta.vol7.issue3.84
Zlotnik, H. (2016). Population Ageing in the Context of Globalization. In D. Sánchez-González & Rodríguez-V.
Rodríguez (Eds.), Environmental Gerontology in Europe and Latin America. International Perspectives on
Aging, vol. 13. Springer. https://doi.org/10.1007/978-3-319-21419-1_3
