Journal of Natural Resources and Environmental Management
http://dx.doi.org/10.29244/jpsl.15.5.844

RESEARCH ARTICLE

Spatiotemporal Dynamics of Land Cover Changes and Local Sustainability Index
of Cianjur Regency, West Java Province, Indonesia
Muhamad Fiqri Rizqullaha, Ernan Rustiadia,b, Vely Brian Rosandia,b, Andrea Emma Pravitasaria,b
a

Division of Regional Development Planning, Department of Soil Science and Land Resources, Faculty of Agriculture, IPB University, IPB Dramaga
Campus, Bogor, 16680, Indonesia
b
Center for Regional System Analysis, Planning, and Development (CRESTPENT), IPB University, IPB Baranangsiang Campus, Bogor, 16144,
Indonesia

Article History
Received 7 January 2025
Revised 1 May 2025
Accepted 16 June 2025

ABSTRACT

Keywords
land cover change, local
sustainability index, spatial
autocorrelation, village
typology

Rapid land cover changes, primarily driven by urbanization and deforestation, present critical
sustainability challenges for rural landscapes, particularly in developing regions such as Cianjur
Regency, West Java. These transitions threaten ecological integrity, disrupt water resource systems,
and compromise biodiversity, ultimately impacting land productivity and the achievement of
sustainable development goals. This study addresses the need for localized, spatially detailed
assessments by integrating remote sensing and spatial analysis to evaluate land cover transitions
and their sustainability impacts. Landsat imagery (30 m resolution) was analyzed using GIS and
spatial statistical techniques, including overlay analysis, spatial autocorrelation (LISA), and
hierarchical clustering. A Local Sustainability Index (LSI) was constructed at the village level,
incorporating environmental, social, and economic dimensions. Findings reveal that between 2011
and 2021, rice fields decreased by approximately 29,212.6 ha, while dry agricultural and residential
lands expanded by 43,428.5 ha and 4,889.7 ha, respectively. The LSI analysis indicates a spatial
trade-off: environmental sustainability has declined particularly in the northern development area,
whereas social and economic dimensions have improved, especially in Cianjur, Cipanas, and Pacet
Sub-Districts. Spatial clustering classifies villages into four typologies: urban village (Cluster One),
developing village (Cluster Two), environmentally strong village (Cluster Three), and
underdeveloped village (Cluster Four). This study provides spatially disaggregated policy insights for
balancing land development with ecological integrity. The identification of village-level sustainability
typologies supports targeted land-use planning and resource allocation, enabling local governments
to formulate adaptive, place-based strategies that align with sustainable development objectives.

Introduction
Land cover changes, particularly the conversion of agricultural and forest lands to urban and industrial uses,
are accelerating in many developing regions, with profound environmental and socio-economic implications
[1–8]. In Monsoon Asia, for instance, rampant urban expansion often engulfs surrounding rural areas and
forms extended metropolitan regions [9]. Such unchecked sprawl commonly leads to land-use conflicts,
infrastructure challenges, and environmental degradation [4,10,11], including the loss of agricultural land
[8,11,12] and the emergence of fragmented land-use patterns that are inefficient and unsustainable [13].
Indonesia exemplifies these trends. Rapid population growth and economic development have driven
widespread land-use changes across the country [14]. However, development has frequently prioritized
short-term economic gains over environmental protection and social equity [12–14], resulting in ecological
harm and rising inequalities. This tension between development and sustainability is acutely visible in regions
undergoing rapid rural-urban transitions.

Corresponding Author: Vely Brian Rosandi
vrosandi@apps.ipb.ac.id
Division of Regional Development Planning, Department of
Soil Science and Land Resources, Faculty of Agriculture, IPB University, IPB Dramaga Campus, Bogor, Indonesia.
© 2025 Rizqullah et al. This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY) license, allowing
unrestricted use, distribution, and reproduction in any medium, provided proper credit is given to the original authors.

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Cianjur Regency in West Java, Indonesia, is a telling case of these dynamics. Land cover changes have become
an increasingly critical issue in Cianjur Regency, which has a population of approximately 2.5 million [15].
Positioned between the sprawling Jakarta and Bandung metropolitan areas, Cianjur has increasingly become
a peri-urban corridor influenced by both mega-cities [16]. In particular, the northern part of the regency has
experienced surging urban expansion [17–19]. Over the 2004–2019 period, North Cianjur experienced a more
than 100% increase in built-up area as new urban centers, such as the Puncak–Cipanas cluster, rapidly
developed [3]. The rapid urban expansion of the Jakarta Metropolitan Area (JMA) and Bandung Metropolitan
Area (BMA) has profound implications for the northern part of Cianjur, which serves as a buffer zone or
hinterland. Regions functioning as hinterlands for major cities, such as Jakarta and Bandung, typically
experience faster development compared to other sub-districts in Cianjur Regency [12–14,17–19]. This
growth has largely come at the expense of natural land covers, forests, mixed agro-forest gardens, and paddy
fields, which have been extensively converted into settlements and other built uses [20]. Contributing to this
trend, regional planning policies in the early 2010s enabled the promotion of new urban zones without
sufficient land-use controls [21]. The outcome is a sprawl pattern that local authorities have struggled to
manage, accompanied by mounting environmental issues.
Already, signs of ecological stress are evident, for example, long-term land degradation in parts of North
Cianjur has triggered landslides during the rainy season and water scarcity in the dry season, severely
affecting local communities [22,23]. Such incidents underscore the fragility of the region’s ecosystems under
rapid land cover change. Beyond the environmental effects, the socio-economic fabric of Cianjur is being
reshaped in tandem with these land changes. Historically an agrarian region, Cianjur’s rural communities
depend on agriculture and related livelihoods that are now under pressure as farmland shrinks [24]. Many
farmers face displacement or must adapt to new forms of employment when their land is sold or repurposed,
a process that can disrupt traditional livelihood strategies and social networks [25]. Experiences from other
Indonesian regions show that land loss often forces rural households into difficult transitions entailing career
changes, relocation, and challenges in managing sudden compensation incomes [26]. In Cianjur, the rapid,
urban-centric growth concentrated in the north has also accentuated spatial socio-economic disparities: the
northern districts benefit from new infrastructure and investment, while the predominantly rural southern
areas lag behind [27]. This imbalanced rural-urban transformation is widening the development divide, with
inequality rising between fast-urbanizing and lagging communities [17]. As resources and opportunities
become unevenly distributed across the regency, there is growing concern that social vulnerability will
increase among those left behind by the current development trajectory.
The convergence of environmental degradation and socio-economic upheaval in Cianjur raises urgent
sustainability challenges. At stake are both ecological integrity and the long-term viability of local livelihoods.
Continued loss of forest cover and agricultural land threatens biodiversity and key ecosystem services such
as water regulation and soil stability while also jeopardizing food security and income for rural households
[14,19]. The situation is particularly critical given projections of future land use: one analysis forecasts that,
if current trends persist, the proportion of land remaining available for new development in Cianjur will
plummet from roughly 85% in 2018 to a mere 2% by 2030 [13]. Moreover, uncoordinated planning between
different levels of government has exacerbated the problem. National authorities have designated much of
Cianjur as a strategic agricultural and water catchment area to buffer the mega-urban region [4], yet local
and provincial plans have simultaneously encouraged urbanization in the north [16]. This asynchronous policy
framework has led to uncontrolled expansion and governance gaps, meaning that without corrective
measures, the regency could face even more severe long-term consequences [21]. There is a pressing need
to better balance development with environmental stewardship and social well-being to ensure Cianjur’s
transformation does not irreversibly undermine its future resilience.
In light of these multidimensional challenges, existing studies have often examined environmental or socioeconomic impacts of land cover change in isolation, relying on descriptive or univariate spatial methods. This
study advances the analytical frontier by integrating local indicators of spatial autocorrelation (LISA) with
clustering techniques to assess spatial sustainability typologies. Unlike earlier applications of LISA focused on
single-variable distributions, this combined approach enables the identification of localized clusters that
reflect multidimensional disparities in economic, environmental, and social indicators). The integration
enhances spatial resolution, provides deeper insight into intra-regional inequalities, and supports the design
of geographically targeted policies for sustainable land management.

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Given the complexity of these interlinked issues, a more integrative analytical approach is required. This study
responds by employing two complementary frameworks, sustainable livelihoods and ecological
modernization (EM), to connect micro-level community experiences with macro-level development
processes. The sustainable livelihoods framework [2,3] provides a lens to assess how households and
communities draw on various assets and capital (natural, human, social, physical, and financial) to sustain
their well-being in the face of change. It emphasizes how external stresses or shocks, such as land cover
change and declining access to natural resources, affect livelihood security, and how people adapt (or fail to
adapt) as a result [3]. Meanwhile, EM [2–5,28] provides a theoretical framework for examining the broader
development trajectory and its environmental implications. EM theory posits that economic development
and environmental sustainability need not be opposing forces; with enlightened policies and innovative
technologies, modern societies can reconfigure their institutions to decouple development from
environmental degradation. Through this lens, we can critically analyze whether Cianjur’s policies and growth
patterns align with sustainable development principles or perpetuate trade-offs.
Moreover, uncontrolled land conversion is often undertaken without sufficient planning, exacerbating social
inequalities. Rapidly developing urban areas tend to attract investment and infrastructure development,
while less developed rural areas lag behind in access to basic services, including education, healthcare, and
transportation. This disparity creates a dependency cycle, where rural areas supply cheap labor to more
advanced regions without benefiting from balanced development. This phenomenon also carries long-term
implications for social structures. Land use changes that shift lifestyles from agrarian to industrial or
urbanization can alter the social fabric, erode traditional community cohesion, and accelerate the
marginalization of groups unable to adapt swiftly to economic changes. Therefore, managing land use change
in Cianjur requires a comprehensive approach that not only addresses economic aspects but also considers
social impacts and long-term sustainability. Integrated spatial analyses, coupled with socio-economic
indicators, can offer valuable insights to identify vulnerable regions and inform policies that are more
inclusive, equitable, and sustainable. However, land cover changes can present significant challenges,
particularly in areas with conservation functions [29]. A lack of synchronization between government
regulations and spatial planning (RTRW/Rencana Tata Ruang Wilayah) can lead to issues and threats to
sustainable development. In this context, sustainability has become a crucial framework in regional
development [30]. Sustainable development relies on three pillars: economic, social, and environmental
sustainability, all of which must progress.

Materials and Methods
Location Study
This research was conducted in Cianjur Regency, West Java Province, which spans an area of approximately
361,434.98 hectares (ha). Geographically, Cianjur Regency is situated between the coordinates of 106°42'–
107° 25' East Longitude and 6°21'–7°25' South Latitude. The regency is divided into 32 sub-districts and 360
villages, with the governmental center in Cianjur Sub-District (Figure 1).

Figure 1. Research location map of the development area in Cianjur Regency, West Java, Indonesia.

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Based on Spatial Planning of Cianjur Regency No.12/2012 [31], the region is strategically organized into three
main development area wilayah pengembangan (WP), each with distinct characteristics and priorities. Each
of these development zones plays a crucial role in the overall planning and implementation of regional
policies, reflecting the regency's diverse geographical, economic, and social conditions. The study focuses on
these distinct zones to assess land cover changes and variations in the sustainability index across different
regions within Cianjur Regency, thereby providing a comprehensive understanding of the regional
development dynamics.
Identify Land Cover Changes and Local Sustainability Index (LSI)
The methodology employed in this study was meticulously designed to capture the spatial and temporal
dynamics of land cover changes and local sustainability in Cianjur Regency through an integrated and precise
approach incorporating a methodological innovation that combines spatial autocorrelation analysis with
cluster detection using an integrated LISA-clustering framework. The identification of land cover types in the
region was conducted by overlaying land cover data from 2011 and 2021 with administrative boundaries
using ArcGIS Pro 10.8 Software. To ensure the reliability of the classification, an accuracy assessment was
conducted using confusion matrix analysis, yielding an overall accuracy of 89.6% and a Kappa coefficient of
0.85. This process resulted in detailed maps that visualize the extent of land cover changes over the past
decade. Land cover change calculations were performed using the field calculator in ArcGIS Pro 10.8
Software, and the results were tabulated and systematically analyzed with Microsoft Excel to provide both
quantitative and interpretive insights. This methodological approach goes beyond previous studies, which
often employed spatial or temporal analysis in isolation, by offering a more comprehensive view of land
change patterns and trends over time. The integration of spatial and temporal dimensions ensures a deeper
understanding of land use dynamics, which is crucial for data-driven decision-making.
The assessment of the Local Sustainability Index (LSI) in this study was conducted using factor analysis, a
robust statistical method that simplifies complex datasets by identifying latent variables underlying the
relationships between correlated indicators. Factor analysis assumes that the variance of observed indicators
can be explained by a smaller number of unobserved latent factors that capture shared information among
them. Specifically, each observed indicator is modeled as a linear combination of common factors plus a
unique error term. Mathematically, the factor score for the k-th dimension (social, economic, or
environmental), m-th factor, in the i-th village is expressed as Equation (1).
𝑛

𝑆𝑘𝑚𝑖 = ∑𝑗=1 𝜆𝑘𝑗𝑚 𝑋𝑗𝑖

(1)

where λkjm is the loading of the j-th indicator on the m-th factor for dimension k, and Xij is the standardized
value of the j-th indicator for village i [32]. The strength of each factor’s contribution to explaining the
variance in its corresponding dimension is represented by its eigenvalue Ekm, and factors with eigenvalues
greater than 1 were retained for interpretation following the Kaiser criterion.
This method reduces data complexity by grouping indicators into a smaller, manageable number of latent
components that represent the core dimensions of sustainability namely, social, economic, and
environmental aspects. By applying factor analysis to the data collected from these three dimensions, this
study successfully identified key factors that capture the interconnections and underlying patterns of
sustainability. The LSI score for each village was then calculated as a weighted aggregation of normalized
factor scores Skmi, where the weights were proportional to the corresponding eigenvalues Ekm of each
retained factor. This ensures that factors explaining more variances have a stronger influence on the overall
sustainability score. In contrast to previous studies that often-combined indicators using simple arithmetic
means or subjective weights, this approach offers a multidimensional and empirically grounded perspective,
enabling a deeper understanding of how sustainability dimensions interact. Furthermore, the LSI scores were
normalized using the minimum–maximum method to ensure consistent comparisons across villages,
enhancing the robustness and comparability of the results.
To ensure a comprehensive sustainability evaluation, this study utilized a variety of indicators across the three
sustainability dimensions. In the environmental dimension, variables such as the frequency of disasters
(inverted), the percentage of rice paddy and forest land area, and the number of slum settlements were used
to represent ecological resilience. The social dimension included indicators such as access to educational and
healthcare facilities, medical staff ratios, and malnutrition rates to reflect community well-being. The
economic dimension encompassed indicators such as the number of industries, markets, and access to
essential infrastructure like electricity and telecommunications. This broad selection of variables allowed the

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study to capture the multifaceted nature of sustainability in Cianjur Regency while addressing the limitations
of previous research, which often focused on a single dimension or a small set of indicators.
The mathematical rigor underlying the factor analysis model further strengthens the research. This model
expresses each observed variable as a linear combination of factors and an error term, enabling the
estimation of factor loadings that measure the relationship between variables and sustainability dimensions.
These factor loadings are crucial for interpreting the contribution of each factor to sustainability, providing a
solid foundation for evidence-based policy recommendations. By integrating advanced spatial analysis with
in-depth statistical modeling, this study bridges the gap between academic research and the practical needs
of regional planning. The findings not only offer a detailed understanding of land cover changes and
sustainability dynamics but also facilitate the development of targeted policy interventions to address
specific challenges in Cianjur Regency. Therefore, this research not only contributes to the sustainability
literature but also provides strategic insights that can be applied to foster sustainable regional development.
The land cover types in Cianjur Regency were identified by overlaying the region's land cover data with its
administrative boundaries. This resulted in a detailed map of the various land cover types across the regency.
Land cover data from 2011 and 2021 were overlaid and compared to analyze changes over time. This
comparison involved calculating the extent of land cover changes, which was accomplished using a field
calculator in ArcGIS Pro 10.8 Software [33–34]. The results were then systematically tabulated and analyzed
using Microsoft Excel to quantify and interpret the land cover transformations over the ten years. This
methodological approach provided a clear and comprehensive understanding of the spatial and temporal
dynamics of land cover in Cianjur Regency. The local sustainability index in this research was assessed using
factor analysis, a powerful statistical method designed to identify and describe the underlying relationships
among observed, correlated variables. Factor analysis, a significant tool, reduces the complexity of data by
grouping these correlated variables into a smaller set of unobserved variables, known as factors, which
capture the essential dimensions of sustainability. This technique is particularly effective in studies where
multiple indicators are used to measure complex constructs, such as sustainability, as it allows for the
distillation of these indicators into a more manageable number of factors, thereby clarifying the process for
the audience.
In this study, factor analysis was applied to the data collected from various indicators of economic, social,
and environmental sustainability within Cianjur Regency. By examining the correlations among these
indicators, the analysis identified key factors that represent the major dimensions of local sustainability in
the region [35]. These factors, by providing a more nuanced understanding of how different aspects of
sustainability are interrelated, make the audience feel knowledgeable and insightful about the topic. The
factor analysis model used in this research is mathematically formulated to describe how the underlying
factors influence the observed variables. Each observed variable is expressed as a linear combination of the
factors plus an error term.
The model enables the estimation of factor loadings, which indicate the strength of the relationship between
each observed variable and the underlying factors. These loadings are crucial for interpreting the meaning of
each factor and understanding the contribution of each variable to the sustainability index. Through this
methodological approach, the study provides a robust framework for evaluating the sustainability of local
development in Cianjur Regency. The factor analysis not only simplifies the complex data set into key factors
but also offers insights into the multidimensional nature of sustainability in the region. This, in turn, facilitates
the formulation of targeted policy recommendations that address the specific sustainability challenges
identified by the analysis. The formula for calculating the LSI is expressed in Equation (2) and (3), while the
variables for each dimension shown in Table 1.
𝐿𝑆𝐼 = ∑𝑛𝑘
𝑚=1 𝐸𝑘𝑚 . 𝑆𝑘𝑚𝑖

(2)

Where:
LSI : LSI for k-th dimension on i-th village
k

: Dimension

Ekm : Eigenvalue for k-th dimension on m-th factor
Skmi : Factor score for k-th dimension, m-th factor on i-th village
i

: 1,2,3, …. ,360

To standardize LSI scores using the minimum maximum standardization method to obtain scoring scores from
each village:

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𝐿𝑆𝐼 𝑘𝑖 − 𝐿𝑆𝐼 𝑘𝑖 (𝑚𝑖𝑛)

𝐿𝑆𝐼 (𝑠𝑡𝑑) = 𝐿𝑆𝐼 𝑘𝑖 (𝑚𝑎𝑥) − 𝐿𝑆𝐼 𝑘𝑖 (𝑚𝑖𝑛) × 100

(3)

Where:
LSI (std) : LSI standardization for k-th dimensian in the n-th village i
LSI ki

: LSI for the k-th dimension in the n-th village i

LSI (max) : Maximum LSI value for the n-th k dimension in the n-th village i
LSI (min) : Maximum LSI value for the n-th k dimension in the n-th village i
Table 1. LSI variables for each dimension.
Code
V1
V2
V3
V4
V5
V6
V7
V8
V9
V10
V11
V12
V13
V14
V15
V16
V17
V18
V19
V20
V21
V22
V23
V24
V25
V26
V27

Variables
ENVIRONMENT
Invers the number of landslide events
Invers the number of flood events
Invers the number of earthquake events
Invers the number of settlements on the riverbanks
Invers the number of slum settlements
Rice fields area
Forest area
SOCIAL
Invers average distance to formal education
Invers average distance to health facilities
Number of formal education facilities
Number of informal education facilities
Number of health facilities
Ratio of medical personnel
Invers sufferers of malnutrition
Invers patients suffering from plague
Invers the number of poor letters that come out
ECONOMY
Invers average distance to the nearest bank
Invers average distance to the nearest shop
Number of banks
Households using landline telephones
Household electricity users
Number of industries
Number of hotels and inns
Number of restaurants and shops
Number of stores
Number of markets
Residential area

Unit

Source

Number
Number
Number
Unit
Unit
Percentage (%)
Percentage (%)

Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Land cover map [37]

Kilometers (km)
Kilometers (km)
Unit
Unit
Unit
Number
Number
Number
Household

Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]

Kilometers (km)
Kilometers (km)
Unit
Household
Household
Unit
Unit
Unit
Unit
Unit
Area percentage (%)

Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Village potential data [36]
Land cover map [37]

Local Indicator of Spatial Autocorrelations (LISA) and Cluster Typology
In this study, Local Moran’s Index, a LISA, was used to examine the spatial distribution of the LSI across Cianjur
Regency. The LSI was constructed from economic, social, and environmental indicators selected for their
relevance and data availability. As introduced by Anselin [38] and further developed in his earlier spatial
econometric work [39], LISA provides localized measures of spatial autocorrelation, enabling researchers to
assess the degree of spatial clustering at the individual unit level, rather than relying solely on global statistics
such as Global Moran’s Index. This makes LISA especially valuable for regional analysis where spatial
heterogeneity is expected and local patterns matter for targeted intervention. The analysis employed a firstorder queen contiguity spatial weights matrix to model spatial dependence, assuming spatial stationarity
under the null hypothesis of no autocorrelation; the statistical significance of local Moran’s I values was
assessed using 999 random permutations to generate pseudo p-values, and local clusters were deemed
significant at the 0.05 level.
This method was specifically chosen to address the need for identifying spatial patterns in sustainability, a
crucial component of regional planning and policy formulation, as outlined by Pravitasari et al. [40]. Unlike
traditional statistical methods that may treat observations as independent, LISA recognizes the inherent
spatial dependency between neighboring areas. This enables a more accurate understanding of how
sustainability indicators are interrelated across different regions, a factor often overlooked in non-spatial

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analyses. One of the key advantages of using the Local Moran’s Index is its ability to detect spatial clustering,
which is essential in understanding how areas with similar sustainability characteristics (either high or low)
tend to group. This capability is particularly important in the context of Cianjur Regency, where socioeconomic and environmental factors vary significantly across the region. By analyzing the spatial
autocorrelation of LSI values, the LISA method helps reveal regions where high sustainability (high-high) or
low sustainability (low-low) values are concentrated, providing valuable insights into regional disparities and
the distribution of sustainability performance. This method also helps identify spatial outliers, which are
regions where a particular area’s LSI value significantly differs from those of its neighbours. This is especially
useful for pinpointing areas that may require targeted interventions, either due to their isolated high
sustainability or their starkly low sustainability despite proximity to more sustainable areas. Detecting such
outliers enables policy-makers to tailor interventions more effectively and efficiently, addressing areas that
might otherwise be overlooked in traditional analyses.
Furthermore, the choice of the Local Moran’s Index represents an advancement over previous studies that
often relied on global or aggregate measures of sustainability without addressing the localized nature of the
data. While global measures such as Global Moran’s Index, Geary’s C Index, or regression-based spatial lags
are informative, they assume spatial homogeneity and may mask significant local. LISA, by contrast, is capable
of decomposing global autocorrelation into location-specific values, enabling finer spatial insight into
sustainability dynamics [38,39]. Prior research typically used broader methods like simple regression analysis
or global spatial autocorrelation, which assume that spatial data is homogeneous across regions or do not
account for local variations in sustainability performance [39]. In contrast, LISA's localized approach enables
a finer-grained analysis, highlighting regional disparities and specific clusters of sustainability values that may
not be captured through global indices or other less spatially sensitive techniques.
By incorporating LISA, this study addresses the limitations of earlier research, which often failed to consider
the intricate spatial dependencies between neighboring regions [39]. The inclusion of spatial lag and the local
interaction of sustainability values improves the precision of the findings, as it allows for a more nuanced
interpretation of the data. This approach, therefore, not only strengthens the validity of the study but also
enhances its policy relevance by offering actionable insights into regional sustainability dynamics. The
formula used to calculate the Local Moran’s Index in this study was specifically adapted to account for both
the magnitude and spatial distribution of LSI values across Cianjur Regency. This ensures that the results
reflect not only the intensity of sustainability indicators but also how they are spread geographically, offering
a more comprehensive examination of local spatial patterns. The ability to measure clustering and dispersion
provides a holistic view of the regional sustainability landscape, making the methodology particularly useful
for planning and decision-making processes aimed at addressing both socio-economic and environmental
challenges within the region.
The application of the Local Moran’s Index in this study provides a more robust, spatially-aware approach to
analyzing sustainability (Equation 4). It improves upon previous research by addressing the spatial
interdependence of sustainability indicators, offering more localized insights, and identifying both clusters of
similar values and spatial outliers. This methodology thus ensures a deeper understanding of the regional
dynamics of sustainability, making it an invaluable tool for policy formulation and regional planning in Cianjur
Regency.
𝐼𝑖 =

∑𝑛
𝑗=1 𝑊𝑖𝑗 (𝐿𝑆𝐼𝑘𝑖 − 𝐿𝑆𝐼𝑘) (𝐿𝑆𝐼𝑘𝑗 − 𝐿𝑆𝐼𝑘)
∑𝑛
𝑗=1 𝑊𝑖𝑗 (𝐿𝑆𝐼𝑘 − 𝐿𝑆𝐼 𝑘)

2

(4)

Where:
Ii

: Moran index

N

: Number of incident locations

LSIki : k-th LSI value at location i
LSIkj : k-th LSI value at location j
LSIk : Average of the number of variables
Wij : Element in standardized weighting between regions i and j
Cluster analysis is employed in this study to classify various areas within Cianjur Regency into distinct groups
based on similar or nearly identical characteristics [41–43]. The core objective of this approach is to identify
clusters groups of regions that share comparable features allowing for more precise and targeted regional
planning and development. The clustering process is grounded in a metric that measures the proximity

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between each region, commonly referred to as distance. In this study, the analysis utilizes the Euclidean
Distance metric, a well-established and widely used method for calculating the straight-line distance between
two points in multi-dimensional space.
The Euclidean distance metric is particularly valuable in spatial analysis, as it provides a measurable and
quantifiable gauge of similarity between regions. By evaluating the "closeness" of attributes across various
dimensions such as geographical, economic, social, and environmental characteristics it effectively captures
the degree of similarity or difference between regions. For example, if two regions share similar socioeconomic conditions, the Euclidean Distance between them would be small, indicating that they are close in
terms of these attributes. Conversely, regions with differing characteristics would have a larger Euclidean
Distance, reflecting their greater dissimilarity. This ability to calculate the distance between individual regions
or between groups of regions allows the analysis to identify clusters of areas that are more similar to each
other than to regions outside of their cluster. These clusters are defined by minimizing the within-group
variance (i.e., the differences between regions within the same group) while maximizing the between-group
variance (i.e., the differences between clusters), thus ensuring that the clusters are as distinct and meaningful
as possible.
The formula used to calculate the Euclidean Distance in this study follows the method outlined by Rustiadi et
al. [19], which has become a foundational technique for spatial clustering in regional studies. The equation
considers the differences between the values of selected variables (such as population density, land use, and
infrastructure availability) across the various regions. This allows for the precise identification of regions that
share similar characteristics based on a range of socio-economic and environmental variables. The flexibility
of the Euclidean Distance metric in this context is particularly beneficial because it enables the incorporation
of multiple dimensions, allowing for a holistic view of regional characteristics in Cianjur Regency. By using
this method, the study is able to group regions into clusters that reveal patterns in how these regions are
spatially distributed. The clustering process minimizes the variance within each group and maximizes the
variance between groups, which enhances the clarity and usefulness of the analysis. For example, regions
with similar economic development, land use patterns, or infrastructure characteristics will be grouped
together, and this grouping provides valuable insights into the geographical spread of these characteristics
within the regency. The resulting clusters then serve as a starting point for further analysis, as they offer a
way to examine how and why certain areas share common attributes.
The advantage of this method lies in its ability to group regions objectively based on actual data, rather than
relying on subjective interpretations or arbitrary categorizations. By using a rigorous statistical foundation,
cluster analysis ensures that the regions are classified based on measurable, quantifiable characteristics,
offering a data-driven approach to understanding spatial patterns. This statistical rigor enhances the
reliability of the results and ensures that the identified clusters accurately reflect the underlying
characteristics of the regions within Cianjur Regency. Once the clusters are formed, the study can delve
deeper into understanding the factors that contribute to the similarities between the regions within each
cluster. For instance, one cluster might consist of regions with high agricultural productivity and strong rural
infrastructure, while another might include more urbanized areas with higher industrial activity.
Understanding these factors is crucial for regional development strategies, as it allows planners and decisionmakers to tailor policies and interventions to the specific needs of each cluster. For example, clusters that
exhibit high economic potential but lower environmental sustainability may require policies that balance
economic development with environmental conservation, while more rural clusters might benefit from
interventions focused on infrastructure improvement or agricultural support.
The methodical application of cluster analysis in this study ensures that the regional classification process is
both objective and statistically robust. This provides a reliable foundation for decision making in regional
planning, where the goal is to develop targeted, effective strategies that account for the unique
characteristics of different areas within Cianjur Regency. By allowing the identification of clusters with similar
characteristics, this approach helps to avoid one size fits all solutions and promotes a more nuanced, localized
approach to regional development. Ultimately, this clustering method provides valuable insights that can
guide policymakers in crafting strategies that are more aligned with the realities on the ground in Cianjur,
ensuring that resources are allocated efficiently and development efforts are optimized for each distinct
region. Cluster analysis is designed to classify various areas within Cianjur Regency into distinct groups with
similar or nearly identical characteristics [41–43]. The primary objective is to identify clusters of regions that
exhibit comparable features, thereby allowing for a more targeted approach to regional planning and
development. The grouping process is based on a metric of proximity between each region, commonly

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referred to as distance. Specifically, this analysis employs the Euclidean Distance metric, a widely used
measure that calculates the straight-line distance between two points in a multi-dimensional space (Equation
5).
𝑑𝑝𝑞 = √∑𝑛𝑖=1(𝑋𝑝𝑖 − 𝑋𝑞𝑖 )2

(5)

Where:
dpq : Euclidean distance
p

: (p1,p2…. Pn)

q

: (q1,q2…. Qn)

The variables utilized in the cluster analysis for this study include social LSI, economic LSI, environmental LSI,
and the percentage of residential area. Each variable contributes critical dimensions to assessing regional
sustainability and development within the Cianjur Regency. The social LSI Pravitasari et al. [22] represents
the social dimensions of sustainability, capturing factors such as community well-being, access to services,
and social equity. This variable provides insights into how social conditions and quality of life vary across
different regions, allowing for an understanding of how social sustainability influences and interacts with
other aspects of development. The economic LSI reflects the economic health and performance of the
regions, including indicators such as income levels, employment rates, and economic activity. This measure
is essential for understanding how economic factors contribute to regional sustainability and how economic
development correlates with social and environmental conditions.
The environmental LSI assesses the ecological sustainability of the regions, incorporating factors such as land
use, environmental quality, and resource management [44,45]. This variable is crucial for evaluating the
impact of human activities on the environment and for identifying regions that are performing well or poorly
in terms of environmental stewardship. The percentage of residential area provides a spatial measure of
urbanization and land use patterns within each region [46–48]. By analyzing this variable, the study can assess
how residential development influences and interacts with social, economic, and environmental
sustainability. Incorporating these variables into the cluster analysis allows for a comprehensive evaluation
of regional characteristics and their interplay. By grouping regions based on these multifaceted variables, the
analysis can identify patterns and clusters that reflect different sustainability profiles, offering valuable
insights for targeted regional planning and policy development [49–51].

Results
Land Cover Changes in Cianjur Regency and Its Implications
The land cover in Cianjur Regency consists of 8 types of land cover. The land cover was analyzed using data
from two time points, namely 2011 and 2021 (Table 2 and Figure 2). The analysis reveals that in 2011 and
2021, the land cover was predominantly dryland farming, which covered approximately 40.3% of the area in
2011 and increased to 52.3% in 2021. This significant increase in dryland farming indicates notable changes
in land use in the region over the decade.
Table 2. Land cover changes (LCC) from 2011 to 2021.
Total area (ha)

Percentages area (%)

Land cover

Forest
Shrubs
Water body
Open field
Dryland farming
Rice field
Plantation
Built-up area
Total

2011

2021

WP1

WP2

WP3

LCC from
2011–2021

95,388.9
6,203.7
4,535.6
1,127.8
146,053.6
77,270.4
25,888.6
5,505.5
361,974

85,611.9
1,601.2
4,224.9
347.5
189,482.1
48,057.8
22,253.5
10,395.2
361,974

–3839
–3,794.7
–20.7
–373.6
7,838.9
–137.9
–2,313.7
2,640.7

–2,432.1
–401.8
–48.9
–324.1
33,259.3
–25,468.1
–4,796.6
212.3

–3,505.9
–406
–241.1
–82.6
2,330.3
–3,606.6
3,475.2
2,036.7

–9,777
–4,602.5
–310.7
–780,4
43,428.5
–29,212.6
–3,635.1
4,889.7

2011

2021

WP1

WP2

WP3

LCC from
2011–2021

26.4
1.7
1.3
0.3
40.3
21.3
7.2
1.5
100

23.7
0.4
1.2
0.1
52.3
13.3
6.1
2.9
100

–2.7
–1.3
–0.1
–0.2
12
–8.1
–1
1.4

–2.7
–2.7
0.0
–0.3
5.5
–0.1
–1.6
1.9

–2.1
–0.3
0.0
–0.3
28.2
–21.6
–4.1
0.2

–8.20
–3.40
–0.29
–0.62
35.96
–25.21
–2.26
4.04

This shift might be driven by the rising demand for agricultural land to support population growth and local
economic development. However, the increase also points to ecological changes that warrant attention,
particularly the reduction of other land cover types. Several types of land cover have decreased in area,

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including forests, which declined from 26.4 to 23.7%, shrubs from 1.7 to 0.4%, plantations from 7.2 to 6.1%,
open fields from 0.3 to 0.1%, water bodies from 1.3 to 1.2%, and rice fields from 21.3 to 13.3%. The decrease
in forested areas and rice fields suggests that land might have been converted for other uses, such as dryland
farming or infrastructure development, posing potential risks to the region. Built-up land and dryland
agriculture experienced an increase in area, with built-up land expanding from 1.5 to 2.9% and dryland
agriculture from 40.3 to 52.3%. The increased built-up land is likely linked to urbanization and more intensive
infrastructure development in Cianjur Regency.

Figure 2. Land cover changes in 2011–2021.
Significant changes were observed in the conversion of rice fields to dryland farming, covering an area of
approximately 37,401.1 ha. This transformation predominantly occurred in the central and southern WP
regions of Cianjur Regency. The findings, which are based on a comprehensive analysis of satellite imagery
and field surveys, align with the previous research [52], which identified several key factors influencing this
land cover change. One of the main factors is the area's topography, which is less accessible to water supplies
from mountainous regions. This, coupled with lower rainfall levels compared to the northern parts of Cianjur
Regency, made these areas more suitable for dryland farming rather than rice cultivation. The shift from rice
fields to dryland farming significantly indicates how environmental conditions, such as water availability and
rainfall patterns, can drive changes in agricultural practices. In regions where water supply is limited and
inconsistent, farmers may find it more sustainable and economically viable to switch to crops that require
less water, hence the increase in dryland farming.
Additionally, converting various dominant land covers, including agricultural lands, into built-up areas is
another critical trend observed in the region. This phenomenon is particularly pronounced in the northern
WP of Cianjur Regency, where urbanization and the demand for space are more intense [53–56]. The research
by Rustiadi et al. [19] supports this observation, stating that land conversion tends to occur from activities
with lower land rent to those with higher land rent. In this context, agricultural lands, which typically have
lower land rent values, are increasingly converted into non-agricultural uses, such as residential or
commercial developments, offering higher economic returns. Prabowo et al. [57] further emphasize that the
land rent value for agricultural activities is generally lower than non-agricultural activities. This economic
pressure accelerates the conversion of agricultural lands into built-up areas, especially in regions where urban
expansion is prevalent. The need for space to accommodate growing populations, infrastructure, and
economic activities drives this land use change, often at the expense of agricultural land and natural
ecosystems.

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Trends in The Local Sustainability Index (LSI) in Cianjur Regency
The LSI for each village in Cianjur Regency is evaluated across three key dimensions: environmental, social,
and economic. This assessment uses a total of 27 variables and compares data from the years 2011 and 2021.
Over the period from 2011 to 2021, the environmental LSI values in the southern WP of Cianjur Regency have
shown a tendency to decline (Figure 3). This decline, primarily attributed to the conversion of forest land into
other land uses, such as rice fields and dryland farming, has significantly impacted the area's environmental
sustainability. The reduction in forest area in the southern WP region, closely linked to the expansion of
agricultural activities, has led to worsening environmental conditions. One critical impact is the reduced
capacity of the soil to absorb water, which increases the risk of natural disasters.

Figure 3. Local sustainability index of each dimension in 2011 and 2021.
On the other hand, the social and economic LSI values have improved, particularly in the northern WP of
Cianjur Regency during the same period. This improvement reflects better accessibility and an increase in the
number of health and educational facilities in the northern region. Education and healthcare are essential
pillars for developing robust human capital, as Todaro and Smith [58] noted. Human resources are
fundamental to a region's competitiveness and drive social development [59]. The growth of the Cianjur subdistrict, as the central urban area, has positively impacted the development of accessibility and social

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infrastructure in surrounding sub-districts. According to Jatayu et al. [60], Cianjur's city center is becoming
denser, a trend that aligns with the city planning strategy outlined in the RTRW. This zone serves as a hub for
the transportation network that connects various parts of Cianjur Regency, indicating the potential for further
development. The urbanization process, initiated by the Jabodetabek Punjur urban agglomeration, is another
factor driving the transformation of rural areas into suburban regions [61]. The road network in this area has
been continuously upgraded due to its strategic importance, particularly the Puncak arterial road, which links
the JMA with the BMA [61]. This region has developed in response to market demand driven by the needs of
urban residents. Additionally, the emergence of a new activity center in Puncak-Cipanas, which has become
a focal point for trade, services, and tourism, contributes to the high concentration of development in this
zone [62].
Spatial Association of Sustainability Dimensions in Cianjur Regency
In both 2011 and 2021, the southern WP of the Cianjur Regency was characterized by a dominant
environmental LSI HH (High-High) cluster. This means that the villages in this region not only had high
environmental sustainability index values, but other villages with similarly high values also surrounded them.
This clustering indicates that the environmental sustainability of the southern WP is more robust and
consistent than other areas in the Cianjur Regency. The differences in hotspot locations over time are closely
related to the specific land cover characteristics within each spatial distribution pattern. Generally, when
forest land cover is converted to other land use types, these hotspot locations show a noticeable shift.
Conversely, the LL (Low-Low) environmental LSI cluster, which indicates areas of environmental degradation,
was notably concentrated in the northern and central WP of Cianjur Regency during both years. Villages
within this cluster are those experiencing significant environmental degradation, often due to excessive
human activity disrupting the natural balance, as noted by Suleman et al. [63].
For the social dimension, the LSI HH (High-High) cluster type in 2011 and 2021 remained unchanged,
concentrated in the northern WP of the Cianjur Regency. This suggests that villages in this area consistently
demonstrated high social sustainability, with solid social development indices mirrored by neighboring
villages. The presence of this cluster indicates a region with high levels of social welfare. Similarly, the
economic LSI HH (High-High) cluster type in 2011 and 2021 also showed an intense concentration in the
northern WP (Figure 4). Villages in this area consistently exhibited high economic sustainability, reflecting
robust economic growth supported by surrounding villages with similar economic indices. The northern WP’s
economic and social strength is partly due to its location within the southern JBMUR corridor [61], a strategic
area that connects major cities.
This corridor influences the transformation of rural areas, often blurring their rural character as urbanization
intensifies [61]. Urban expansion from the Jabodetabekpunjur region and Greater Bandung, particularly in
Cianjur Regency, has led to a spread of built-up areas from city centers to the outskirts. This expansion has
driven economic growth, social development, and regional urban activity [30]. However, the LL (Low-Low)
cluster type for both social and economic dimensions in 2011 and 2021 was predominantly found in the
southern WP of the Cianjur Regency. Villages within this cluster are marked by social inequality and are often
pockets of poverty. These areas face significant social and economic development challenges, contrasting
sharply with the more prosperous northern regions of the regency.
The results of the LISA analysis indicate that villages with high environmental sustainability in both 2011 and
2021 are predominantly located in the Southern WP area of Cianjur Regency. In contrast, villages
experiencing environmental degradation are more prevalent in the Northern and Central WP regions.
Regarding the social and economic dimensions during the same period, villages with high social welfare and
economic growth levels are primarily situated in the Northern WP, particularly in Cianjur, Pacet, and Cipanas
Sub-District. Conversely, villages identified as pockets of poverty are concentrated mainly in the Southern
WP.
These findings align with the research conducted by Pravitasari et al. [22], which suggests that regions with
high economic and social sustainability often exhibit low environmental sustainability. This pattern indicates
an imbalance in development, where the focus on economic and social aspects comes at the expense of
environmental considerations. The uneven distribution of development resources and the neglect of
environmental sustainability underscore the need for integrated development efforts across the Cianjur
Regency. Consequently, insufficient effort has been made to achieve balanced and equitable development in
the region, leading to disparities between the environmental, social, and economic dimensions.

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Figure 4. Spatial association distribution patterns in 2011 and 2021.
Regional Typology of Cianjur Regency Based on Local Sustainability Index (LSI)
The regional typology of Cianjur Regency is determined through an analysis based on the LSI, which evaluates
the environmental, social, and economic dimensions, alongside the percentage of land cover dedicated to
residential areas. Residential areas are treated as a distinct factor in this analysis due to their unique and
significant impact on all three dimensions of sustainability. Environmentally, the expansion of residential
areas directly influences land use patterns, potentially leading to environmental degradation, such as the loss
of agricultural land and the increased pressure on natural resources. Socially, areas with higher residential
development often experience challenges related to population density, access to essential services, and
socio-economic disparities. Economically, residential areas are a key indicator of urbanization, which affects
local economies through changes in infrastructure needs, service provision, and labor market dynamics. By
isolating residential areas as a separate cluster, this approach allows for a more focused and precise analysis
of areas requiring specific interventions in infrastructure development, social welfare, and sustainable land
management.
This differentiation helps policymakers allocate resources more effectively, ensuring that development
strategies are tailored to the unique needs of each region within Cianjur Regency, ultimately guiding
sustainable development efforts. This typological analysis employs the k-means clustering method,

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classifying Cianjur Regency into four distinct clusters (Figure 5 and Table 3). The k-means clustering method
is a statistical technique used to partition objects into clusters based on their characteristic similarity [19,41–
43,64]. The purpose of establishing this regional typology is to guide the formulation of policies, strategies,
plans, and development programs in a more targeted manner. By identifying clusters with similar
characteristics, the government can prioritize its budget and resources toward developing social, economic,
and environmental aspects in most needy areas. This approach ensures that efforts to achieve sustainable
development in Cianjur Regency are focused and effective, directing investments where they will significantly
improve overall sustainability across the region.

Figure 5. Result plot of means for each cluster.
Table 3. Cluster typology of Cianjur Regency based on environmental, social, economic, and residential area
dimensions.
Typology
Cluster 1
Cluster 2
Cluster 3
Cluster 4

Environment
Low
Low
High
Low

Social
High
Medium
low
Low

Economy
High
Medium
Low
Low

Residential area
High
low
Low
Low

Characteristic
Urban village
Developing village
Good environmental village
Underdeveloped village

Cluster 1 comprises villages with low environmental sustainability, high social and economic sustainability,
and a large residential area percentage. These villages exhibit the characteristics of an urban village or
economic activity center. This cluster's high social and economic sustainability is strongly influenced by
adequate public facilities such as access to education, healthcare, and social services and good accessibility
to economic centers and markets. This accessibility drives economic activities, subsequently enhancing the
residents' living standards. On the other hand, the low environmental sustainability observed in these villages
is attributed to extensive land conversion. Forests and agricultural lands (rice paddies or drylands) have
frequently been converted into built-up areas, including housing, offices, and infrastructure. This land
conversion contributes to environmental degradation, reduction of green spaces, and increased air and water
quality risks in the region. These findings align with Pravitasari et al. [22], which indicates that regions with
high social and economic sustainability often experience low environmental sustainability, primarily due to
the high rate of land conversion in developed areas.
Cluster 2 includes villages with low environmental sustainability, moderate social and economic
sustainability, and a relatively low percentage of residential areas. Villages in this category display the
characteristics of a developing region, marked by potential for social and economic growth. Although not as
intense as Cluster 1, these villages still experience land conversion pressure, especially in strategic areas or
locations near the village’s economic hubs. The unoptimized potential includes resources such as natural
(agriculture, forestry, or plantations), social (community institutions and local wisdom), and economic (small
and medium-sized enterprises or local market potential). As Pravitasari et al. [40] mentioned, these villages
possess substantial resources, yet resource management remains suboptimal, thereby not fully supporting
long-term sustainability. The low environmental sustainability in this cluster is primarily due to insufficient
resource management rather than extensive construction, as seen in Cluster 1.

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Cluster 3 consists of villages with very high environmental sustainability but lower social and economic
sustainability and a limited residential area. Villages in this cluster show the characteristics of areas
prioritizing environmental sustainability. Generally, these villages have relatively undisturbed ecosystems,
with well-maintained vegetation cover and green spaces, and they apply sustainable conservation practices.
These characteristics make this cluster an essential environmental support area, as it contributes to
ecosystem services that sustain water, soil, and biodiversity health. Nevertheless, these villages' low social
and economic sustainability suggests limited access to public facilities, education, healthcare, and economic
opportunities. Accessibility and infrastructure deficiencies hinder improvements in the quality of life for
residents in this cluster, leading them to depend primarily on natural ecosystems and environmentally
sustainable economic practices that yield limited economic margins.
Cluster 4 comprises villages with low environmental, social, and economic sustainability and a low percentage
of residential area. Villages in this cluster are characterized as the most underdeveloped regions. These areas
face challenges across nearly all development aspects, from basic infrastructure and public facilities to
economic opportunities. According to Liu et al. [65], disadvantaged villages are typically located in remote
areas with limited accessibility and constrained natural resources within protected zones. Several factors
contribute to the underdevelopment of these regions, including geographic isolation, a lack of basic
infrastructure such as roads and bridges, limited healthcare and educational services, and the absence of
economically viable natural resources. These areas are also often affected by natural disasters or social
conflicts, which exacerbate the social and economic conditions of the communities. Geographic challenges
remain a primary barrier to connecting these villages with economic and social activity centers, thus
restricting the mobility of residents and goods. As highlighted by Guo et al. [66], improvements in accessibility
and transportation can help reduce geographic barriers that have impeded development processes. Guo et
al. and Long et al. [67,68] also noted that infrastructure development encourages increased mobility,
positively impacting economic and social activities, which is crucial for accelerating the development of
disadvantaged villages. Figure 6 showed map of Cianjur Regency typology in 2011 and 2021.

Figure 6. Cianjur Regency typology in 2011 and 2021.

Discussion
Balancing Growth and Sustainability?
This research highlights the significant land cover changes in Cianjur Regency, providing insights into the
complexities of urbanization and its far-reaching implications for environmental sustainability. Between 2011
and 2021, a substantial reduction of 29,212.6 ha of paddy fields was observed, coinciding with the expansion
of dry farmland and residential areas. This trend indicates a clear shift from agricultural to urban uses, a
change that is emblematic of broader urbanization patterns in the region. The conversion of agricultural land
to urban uses, as evidenced by this reduction, is a critical issue, especially in the context of food security.

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The loss of productive agricultural land, often exacerbated by unregulated urban expansion, significantly
increases the risk of food insecurity, a concern that mirrors trends seen in other urbanizing regions globally
[11,12]. The theoretical concept of urban sprawl provides a useful framework for understanding these
changes. Urban sprawl, as a phenomenon, often leads to the consumption of agricultural land and natural
resources, resulting in environmental degradation. The findings of this study confirm that the rapid
urbanization occurring in northern Cianjur is driven largely by unplanned growth, which, as previous studies
have shown, can result in negative externalities such as increased pollution, loss of biodiversity, and
disruption of local ecosystems. This uncontrolled urban expansion aligns with broader discussions in urban
theory, which suggest that without comprehensive spatial planning, urban growth can threaten both the
environment and public health [22,60].
The decline in the LSI reinforces the notion of sustainability trade-offs, particularly the delicate balance
between economic growth and environmental health. As urbanization and agricultural intensification
outpace the region's capacity for environmental management, the findings of this research resonate with the
sustainability challenges identified in earlier studies [51,69]. The decline in LSI suggests that while economic
development continues to progress in Cianjur, it is not being matched by improvements in environmental
governance. This imbalance aligns with the theory of EM, which argues that economic growth can be
compatible with environmental sustainability, but only if policies are implemented to encourage sustainable
practices in land use, resource management, and industrial development [44].
Further, the LISA analysis revealed significant spatial disparities within Cianjur, with the southern region,
which remains relatively less developed, exhibiting higher levels of environmental sustainability. In contrast,
the northern and central regions, which have experienced more intense urbanization, face greater ecological
challenges [60,69]. These spatial differences reinforce the need for integrated regional planning that balances
socio-economic growth with environmental preservation. This study suggests that regional planning must
address these disparities by incorporating both socio-economic and environmental factors, a sentiment
echoed in prior research emphasizing the importance of holistic, coordinated development strategies for
sustainable regional growth [54,62].
The regional typology developed in this study further illuminates the necessity for targeted interventions in
urban clusters. These areas, characterized by high socio-economic sustainability but low environmental
sustainability, are at the forefront of development challenges. The application of the sustainable livelihoods
framework offers a potential solution, advocating for a multi-dimensional approach to addressing both
poverty reduction and environmental management in urban contexts [8,38]. Policies for urban areas must,
therefore, not only stimulate economic growth but also ensure the protection of ecological integrity through
sustainable land use practices. For underdeveloped rural areas, the study calls for comprehensive strategies
that integrate economic, social, and environmental considerations. The concept of spatial justice could
inform such strategies, ensuring equitable distribution of development benefits and empowering vulnerable
communities to engage in decision-making processes [66–68].
The findings of this study highlight the critical importance of implementing integrated and sustainable
development strategies that address the complex dynamics of land cover change and socio-economic
disparities in Cianjur Regency. The observed spatial patterns underscore how rapid urbanization, shifts in
land use, and uneven access to resources can exacerbate regional inequality and environmental degradation.
In comparing the empirical results with existing literature, this study reaffirms the persistent tension between
economic growth and environmental sustainability, particularly in rapidly urbanizing peri-urban and rural
urban fringe regions. These findings underscore the necessity of policy interventions that promote
coordinated spatial planning across urban and rural areas, aimed at achieving more equitable and ecologically
balanced development outcomes.
However, several limitations of the current study must be acknowledged. First, the reliance on secondary
data for land cover analysis, while practical and widely used, may not capture the full range of land use
dynamics particularly informal or small scale changes that are common in transitional regions. The temporal
scope, limited to two time points (2011 and 2021), provides a useful snapshot of decadal change but may
overlook short-term fluctuations driven by policy shifts, socio-economic shocks, or environmental
disturbances. Furthermore, the methodological approach to constructing the LSI, although analytically
robust, may oversimplify the interdependent nature of environmental, social, and economic factors. Notably,
the social sustainability dimension lacks granularity with regard to socio-cultural dynamics, community
resilience, and local governance, which are critical for contextualizing sustainability at the local scale.

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To advance the analytical depth and policy relevance of future research, several avenues are proposed.
Expanding the temporal resolution of land cover data using high frequency remote sensing techniques could
offer more nuanced insights into land use transitions, especially in rapidly changing environments. Moreover,
integrating qualitative methods such as participatory mapping, stakeholder interviews, and community
surveys would enhance the understanding of local perceptions, institutional arrangements, and lived
experiences that shape sustainability outcomes. Refining the LSI framework by incorporating additional
indicators related to governance quality, cultural practices, and rural urban linkages could improve its
sensitivity to local variations and policy contexts. Additionally, future studies should explore the socioeconomic impacts of land use change on vulnerable populations to ensure that sustainability interventions
are inclusive and socially justice.

Conclusion
This study reveals the complex interplay between land cover change and sustainability dynamics in Cianjur
Regency from 2011 to 2021. The marked decline in rice fields and concurrent expansion of dryland farming
and residential areas reflect a broader transition driven by urbanization and shifting agricultural priorities.
These changes underscore a critical trade-off between economic growth and environmental sustainability,
particularly in rapidly urbanizing northern regions. While social and economic indicators have improved due
to better infrastructure and growing economic activities, these gains often come at the expense of ecological
integrity, as seen in declining environmental sustainability scores and increased land degradation. The
integration of LSI, LISA, and typological clustering offers a multidimensional view of regional disparities and
development trajectories. The findings demonstrate that environmental benefits are concentrated in the
less-developed southern areas, while socio-economic gains are centered in the north, resulting in spatial
asymmetries and sustainability trade-offs.
This typology highlights the need to resolve competing demands: enhancing socio-economic opportunities in
environmentally rich but underdeveloped areas, while mitigating ecological degradation in urbanized zones.
To address these tensions, the study recommends differentiated, cluster-specific policy interventions that
align with local conditions. Urban areas require green infrastructure and sustainable design to balance
development with ecological preservation. Environmentally sustainable yet economically lagging villages can
leverage eco-tourism and sustainable agriculture, while underdeveloped regions demand comprehensive
support to improve infrastructure and reduce poverty. Overall, this research underscores the importance of
integrated, place-based planning that reconciles development with conservation, ensuring that sustainability
is not sacrificed for growth, but achieved through deliberate, context-sensitive trade-offs.

Author Contributions
MFR: Analysis, Software, Writing – original draft; ERU: Conceptualization, Supervision, Validation, Writing –
Review; VBR: Supervision, Validation, Writing, and Editing; and AEP: Supervision, Analysis, Validation.

Conflicts of Interest
There are no conflicts to declare.

Acknowledgments
The authors declare that this research didn’t receive external funding.

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