Revenue Journal: Management and Entrepreneurship, vol. 4 (1), pp. 49-62, 2026
Received: 2025-11-15; Revised: 2026-01-10; Accepted: 2026-02-05; Published: 2026-02-28
https://doi.org/10.61650/rjme.v4i1.1012

Spatial Analysis of Food Inadequacy
Clusters: Managerial Implications for
Regional Food Security Policy in
Indonesia
Ana Novianti1*, Nely Aida2, and Asih Murwiati3
1,2,3Master of Economics, Faculty of Economics and Business, University of Lampung

Corresponding author: ananovianti6@gmail.com

KEYWORDS:
Food inadequacy,
spatial
management,
regional
development,
Moran’s I, food
security policy,
Indonesia

ABSTRACT Persistent regional disparities in food access and distribution pose significant
managerial and developmental challenges for Indonesian food security. This study aims to
analyze the spatial distribution patterns of food inadequacy prevalence across 514
regencies and municipalities in Indonesia in 2025 to inform more effective regional
management policies. Utilizing secondary data from official government institutions, this
research adopts a quantitative spatial approach using GeoDa software, performing
thematic mapping, Moran’s I spatial autocorrelation, and Local Indicators of Spatial
Association (LISA) analysis. The findings reveal a strong positive spatial autocorrelation
(Moran’s I = 0.804), indicating that food inadequacy is not randomly distributed but forms
distinct clusters. High-High clusters, or insecurity hotspots, are predominantly
concentrated in Eastern Indonesia—specifically in Papua, Maluku, and East Nusa
Tenggara—driven by geographical isolation and inadequate infrastructure. Conversely,
Low-Low clusters are concentrated in Java and Bali, benefitting from superior economic
development and market accessibility. These results conclude that food inadequacy is
strongly influenced by interregional development disparities, highlighting the need for
spatially integrated management strategies. Policymakers should transition toward a
region-based approach, prioritizing infrastructure connectivity, strengthening local food
distribution systems, and reducing socio-economic inequalities to ensure targeted and
sustainable food security interventions.
© The Author(s) 2026

1. INTRODUCTION
Food security is a foundational pillar of global sustainable development, directly impacting community
welfare, regional stability, and national economic resilience (FAO, 2023; Arifin, 2020). In an increasingly
globalized economy, the ability of a nation to ensure equitable access to adequate, nutritious, and safe food
remains a critical benchmark for effective governance and social development (Bappenas, 2023; Todaro &

Smith, 2015). Despite global efforts to mitigate hunger, persistent disparities in food distribution and access
continue to exacerbate regional inequalities, particularly within archipelagic and developing nations like
Indonesia (FAO, 2023; BPS, 2025). The significance of this issue lies in its direct correlation with human
capital development, where food inadequacy serves as a primary determinant of poverty traps and socioeconomic stagnation (Sen, 1981; Arifin, 2020).
The core problem and primary challenge in ensuring national food security revolve around deep-seated
regional disparities between Western and Eastern Indonesia, where infrastructural and geographical
barriers create persistent vulnerability (Bappenas, 2023; Rustiadi et al., 2021). While urban centers in Java
and Bali benefit from advanced logistics, market accessibility, and higher purchasing power, remote regions
such as Papua, Maluku, and Nusa Tenggara struggle with high food logistics costs and weak regional
connectivity (BPS, 2025; Tarigan, 2016). These regional imbalances are further aggravated by localized
poverty, susceptibility to environmental shocks, and a lack of integrated distribution networks, which
prevent the efficient flow of food resources (FAO, 2023; Nugroho & Dahuri, 2012). Addressing these
structural challenges is imperative, as they directly impede the achievement of equitable national welfare
goals (Arifin, 2020; Rustiadi et al., 2021).
Extensive research regarding food inadequacy has been conducted by various scholars, yet results remain
fragmented across different spatial scales. Studies related to spatial food insecurity distribution have been
conducted by Filzah Syakirah et al. (2025), Solana (2022), Rustiadi et al. (2021), Arifin (2020), and BPS
(2025). Furthermore, research focused on spatial autocorrelation and socioeconomic determinants has been
highlighted by Anselin (1995), Lee and Wong (2001), and Goodchild (2004). While these studies have
provided valuable insights into general food insecurity drivers, many fail to integrate real-time spatial cluster
identification with contemporary managerial policy frameworks tailored for the 2025 socio-economic
landscape. Most previous literature focuses heavily on agricultural production output rather than the spatial
distribution of inadequacy itself, leaving a gap in understanding how interregional connectivity and spatial
policy management can mitigate these disparities.
The novelty of this research lies in its adoption of a high-resolution, contemporary spatial analysis utilizing
2025 data, shifting the focus from mere food availability to a spatially explicit managerial perspective. Unlike
previous studies that often treat regions in isolation, this research employs advanced GeoDa-based spatial
modeling to map the exact clustering of food inadequacy, identifying critical hotspots and coldspots with
high statistical precision. By integrating Tobler’s First Law of Geography with current Indonesian
socioeconomic data, this study offers a new dimension of analysis that captures the interdependency
between neighboring regencies. The use of the 2025 dataset provides a timely evidence base that reflects
recent infrastructure developments, making this research a vital tool for policymakers navigating the postpandemic economic landscape.
A notable research gap exists in the limited literature that connects spatial autocorrelation patterns directly
with actionable regional management interventions in Indonesia. Previous works by Solana (2022) and
Filzah Syakirah et al. (2025) have identified clusters but often lacked the managerial depth required to
translate these findings into specific cross-regional collaborative policies. This study bridges the gap
between spatial econometrics and management science by explicitly proposing how spatial cluster data
(Hotspots) can inform specific managerial resource allocation. While prior research frequently emphasized
the causes of inequality, this research focuses on the managerial framework required to manage these
inequalities, providing a comprehensive strategy for targeted intervention.
The theoretical framework utilized in this study is anchored in the spatial econometrics approach,
fundamentally driven by Tobler’s First Law of Geography (1970) and the spatial autocorrelation principles
established by Moran (1950). To explain the socioeconomic disparities, the study incorporates Sen’s
Entitlement Theory (1981), which posits that food access is a function of economic resources and social
positioning rather than simple production. Additionally, the study utilizes the Growth Pole Theory (Perroux,
1955) to explain the concentration of food security in developed urban centers at the expense of peripheral
areas. By merging spatial theory with developmental economics, this study constructs a robust multitheoretical lens for analyzing regional food distribution (Anselin, 1995; Rustiadi et al., 2021).
The core concepts applied in this research revolve around spatial association, specifically using Local
Indicators of Spatial Association (LISA) and Moran’s I as the primary analytical drivers. The concept of
"Spatial Dependency" is central here, assuming that the food security status of a regency is not independent
but rather a result of the socio-economic interactions with its neighboring regions (Anselin, 1995; Goodchild,
2004). Furthermore, the concept of "Regional Vulnerability" is operationalized through the Prevalence of
50

Distribution Pattern Analysis… / Ana Novianti..

Undernourishment (PoU) indicator, capturing the intersection of geographical accessibility and economic
capability. These concepts are treated as integrated variables that define the spatial behavior of food
inadequacy in the Indonesian administrative context (BPS, 2025; FAO, 2023).
This study is highly compelling because it addresses the "spatial silence" in current national food security
strategies, where policies are often applied uniformly despite vastly different regional realities. It is essential
to conduct this study because food insecurity in Indonesia is not a random phenomenon but a systematic,
clustered issue that requires targeted regional management. By uncovering the high-high clusters in Eastern
Indonesia, this research provides the necessary granular intelligence for the government to move from
generic national policies to localized, region-specific distribution strategies (Bappenas, 2023; Arifin, 2020).
The research is significant because it shifts the paradigm of food security management from a top-down
approach to an evidence-based, spatially integrated system.
The primary objective of this study is to analyze the spatial distribution patterns of the prevalence of food
inadequacy across 514 regencies and municipalities in Indonesia in 2025. Specifically, the study aims to (1)
quantify the global and local spatial autocorrelation of food inadequacy; (2) identify significant food
insecurity hotspots (High-High clusters) and coldspots (Low-Low clusters); and (3) propose region-based
managerial recommendations for policy interventions. By achieving these objectives, this research provides
a strategic blueprint for reducing interregional development disparities, thereby fostering a more equitable
and resilient national food security system that responds directly to the geographical and socioeconomic
realities of 2025 Indonesia (Bappenas, 2023; Rustiadi et al., 2021).

2. LITERATURE REVIEW
The global landscape of food security has undergone significant transformations over the past three years,
shifting from a focus on aggregate caloric availability to a nuanced understanding of spatial distribution and
accessibility. Recent studies have highlighted that despite technological advancements in agriculture, the
persistent challenge of food inadequacy remains a critical issue in developing nations, where socio-economic
disparities and geographical isolation create complex bottlenecks in food supply chains. A significant
research gap has emerged in the literature: while broad macroeconomic factors of food insecurity are
documented, there is a lack of high-resolution, empirical studies that synthesize spatial econometrics with
regional management frameworks to explain localized clusters of vulnerability. This research addresses
these gaps by exploring three pivotal questions: (1) How do spatial clustering patterns manifest across
Indonesian regencies in the current socio-economic landscape? (2) Which managerial interventions are
effective in addressing identified "hotspots"? and (3) How can regional connectivity be optimized to bridge
the spatial divide? To ensure the integrity of this review, a systematic protocol was adopted, drawing
inspiration from the PRISMA framework to identify and synthesize peer-reviewed literature published
between 2023 and 2026. The search strategy utilized Boolean operators—("spatial analysis" OR "spatial
autocorrelation") AND ("food security" OR "food inadequacy") AND ("Indonesia" OR "regional
development")—across prominent academic databases including Scopus, Google Scholar, and Sinta. The
inclusion criteria were strictly limited to peer-reviewed journals utilizing quantitative spatial modeling,
while non-empirical reports and outdated regional datasets (pre-2023) were excluded to maintain the
currency of the evidence base. This systematic approach ensures that the synthesis is representative of the
latest academic debates, providing a reliable foundation for mapping the conceptual domain of regional food
management and identifying specific areas where current policy interventions remain insufficient.The
theoretical foundation of this study is grounded in modernized spatial interaction frameworks, synthesized
with current development economics theories that prioritize spatial equity. These are integrated with
contemporary views on "spatial dependency," which posit that the food security status of a regency is not
independent but a result of socio-economic interactions with neighboring regions. Table 1 provides a
comparative analysis of key recent studies that have shaped this domain, focusing on the evolution of spatial
modeling in the 2023–2026 period:
AUTHOR & YEAR
SOLANA
ET
AL.
(2024)

METHODOLOGY
Spatial Regression

VARIABLES/FOCUS
Regional Inequality

KEY FINDINGS
Significant link between
poverty and clusters

FILZAH SYAKIRAH
ET AL. (2025)
RUSTIADI ET AL.
(2023)
ARIFIN (2024)

Moran’s I

Food Inadequacy

Regional Planning

Infrastructure

Econometric
Modeling
Statistical Mapping

Food Security Policy

Positive
spatial
autocorrelation
Infrastructure connectivity
reduces food risk
Distribution efficiency is the
primary driver
Eastern Indonesia remains

BPS (2025)

Prevalence Trends

REVENUE JOURNAL: MANAGEMENT AND ENTREPRENEURSHIP 4 (1) Hal 49-62

GAP ADDRESSED
Actionable
management
framework
Multi-regional scope
Spatially
targeted
planning
Modern supply chain
management
Cluster
significance
51

vulnerable

mapping

The synthesis of this literature suggests that while the drivers of food inadequacy—poverty, poor
infrastructure, and weak market connectivity—are widely understood, the interregional nature of these
drivers remains the most significant point of academic debate. Contradictions exist in how scholars perceive
the effectiveness of top-down versus bottom-up regional management; some argue that centralized supply
chain control is necessary, while others suggest that localized, spatial-cluster-based interventions are more
effective for the unique archipelagic topography of Indonesia. Within the scope of the Revenue Journal:
Management and Entrepreneurship, this debate is particularly salient because it transforms food security
from an agricultural problem into a management and logistics challenge. By viewing food insecurity as a
"managerial failure" of distribution and resource allocation across administrative boundaries, this research
provides the necessary novelty to bridge the gap between academic spatial theory and practical
entrepreneurship-led regional development.
In light of the identified gaps, future research agendas must prioritize mixed-method approaches that
combine quantitative spatial autocorrelation analysis with qualitative field studies to capture the "human
element" of food accessibility. There is a pressing need to move beyond static mapping toward dynamic,
temporal modeling that accounts for climate-related shocks and evolving logistical infrastructure in remote
areas. Future scholars should focus on "Managerial Spatial Economics," testing how entrepreneurship and
local supply chain innovation within High-High clusters can disrupt the cycle of food insecurity. By
integrating the results of this study with predictive machine learning models, subsequent research can move
toward prescriptive policy tools, allowing for real-time allocation of food resources based on emerging
cluster trends. Ultimately, this study asserts that the path forward for Indonesian food security lies in
spatially integrated management, where policy decisions are guided by the empirical reality of geographical
proximity and economic interdependency.

3. METHODS
This research employs a robust quantitative spatial approach to examine the prevalence of food inadequacy,
ensuring transparency and reproducibility in its analytical framework. The methodology is designed to
bridge spatial econometrics with regional management policy, adhering to established academic protocols
to ensure validity and reliability. The research process systematically flows from secondary data integration
to spatial statistical testing and policy-oriented interpretation. By integrating structured data from national
administrative records, this research provides a comprehensive assessment of the spatial interdependency
of food insecurity. The following methodological workflow (Figure 2.1) illustrates the systematic stages,
starting from data normalization to the identification of statistically significant spatial clusters that dictate
the scope of managerial recommendations in this study.

Figure 2.1. Methodological Workflow of Spatial Analysis

The study utilizes a deductive research design, systematically translating the research questions into
actionable analytical types to ensure clear alignment between the investigation and the resulting
management implications.
52

Distribution Pattern Analysis… / Ana Novianti..

Table 2.1. Research Questions and Type of Analysis
RESEARCH QUESTION
HOW IS THE FOOD INADEQUACY PREVALENCE DISTRIBUTED SPATIALLY
ACROSS INDONESIA?
DOES THE PREVALENCE OF FOOD INADEQUACY EXHIBIT A SIGNIFICANT
CLUSTERED PATTERN?
WHERE ARE THE SPECIFIC HOTSPOTS AND COLDSPOTS OF FOOD
INSECURITY LOCATED?

TYPE OF ANALYSIS
Descriptive & Thematic Mapping
Global
Spatial
Autocorrelation
(Moran’s I)
Local Indicators of Spatial Association
(LISA)

3.1 Research Design
The research design is structured to provide a comprehensive spatial overview, transitioning from global
diagnostics to local cluster identification. This design ensures that the analysis captures both national-level
trends and specific localized disparities, which are crucial for tailoring regional management policies. By
utilizing a cross-sectional design based on the 2025 data, this study provides a high-resolution snapshot of
food vulnerability that is vital for targeted managerial interventions. The deductive nature of this research
allows for the systematic testing of spatial theories against real-world administrative data. This approach is
instrumental in identifying systemic patterns rather than isolated occurrences, thereby ensuring that the
resulting management framework is grounded in empirical reality. Furthermore, the design incorporates a
recursive process where the identification of "hotspots" directly informs the subsequent discussion on policy
efficacy. By anchoring the design in spatial econometrics, the study creates a bridge between theoretical
geographical laws and the practical demands of Indonesian regional governance, providing a rigorous
analytical baseline for future comparative studies.
3.2 Data Collection Technique
Data collection focuses on secondary sources, specifically the 2025 Prevalence of Undernourishment (PoU)
data obtained from the Central Statistics Agency (BPS) and relevant regional institutions. The data
acquisition process involves a rigorous verification stage to ensure that all 514 regencies and municipalities
are represented, maintaining statistical significance for national-level analysis. All raw data were
systematically integrated into a unified digital database and matched with official 2025 administrative
shapefiles to facilitate accurate spatial mapping. This verification ensures that geographical boundaries align
perfectly with socioeconomic indicators, minimizing spatial mismatch errors. Furthermore, the selection of
the 2025 dataset ensures that the analysis reflects current post-pandemic infrastructure conditions and
recent regional development trajectories. The consistency of this data, audited by national statistical bodies,
provides a high degree of confidence in the empirical foundations of this research. By focusing on a
standardized national indicator, the study avoids the biases associated with disparate regional survey
methods, resulting in a cohesive dataset that is uniquely positioned for cross-regional comparative spatial
analysis.
3.3 Subject and Research Location
The research subjects are the 514 regencies and municipalities across the Indonesian archipelago,
encompassing diverse geographic, economic, and infrastructure conditions in both Western and Eastern
Indonesia. This extensive subject base allows for a comprehensive analysis of national disparities that might
otherwise be obscured in smaller, sub-national studies. The geographical scope is restricted to Indonesian
territorial boundaries as defined by official 2025 administrative maps, ensuring that the findings are directly
applicable to the national regional development framework and local government management mandates.
The location encompasses a wide range of topographies—from mountainous interior regions to archipelagic
zones—providing the necessary variation to evaluate how different environmental and developmental
contexts influence food security clusters. This extensive coverage ensures the research is not limited to a
single development model, but instead captures the complexity of the Indonesian landscape. By covering the
entirety of the national administrative structure, the study serves as a diagnostic tool for central and regional
governments, enabling a nuanced understanding of how developmental disparities manifest across the
entire national territory.

REVENUE JOURNAL: MANAGEMENT AND ENTREPRENEURSHIP 4 (1) Hal 49-62

53

Figure 2.2. Procedural Process of Spatial Cluster Identification

The procedural identification of clusters (Figure 2.2) involves calculating the local spatial lag for each
regency, which is then compared against its own value to identify whether it contributes to a significant
spatial cluster. This rigorous process is essential for separating statistically significant spatial patterns from
random distribution. By employing this method, the research successfully differentiates between regencies
that face systematic, clustered food insecurity and those where the issue is intermittent or localized,
providing a clear roadmap for prioritized managerial resource allocation and infrastructure investment. This
procedural step ensures that the policy recommendations are not based on generic assumptions but on the
statistically significant interdependence of neighboring administrative units.
3.4 Research Instrument Design
The primary instrument for this study is the GeoDa analytical software, which serves as the standardized
tool for processing complex spatial socioeconomic data. The instrument parameters are defined by the queen
contiguity matrix (first-order), which ensures that each regency's analysis is directly influenced by its
immediate neighbors, reflecting real-world socioeconomic spillover effects. This instrument was selected for
its capacity to handle large-scale spatial datasets (N=514) and its precision in generating statistically
significant cluster outputs. The reliability of the instrument is supported by its ability to perform high-speed
permutations, which are vital for calculating the p-values of the spatial autocorrelation coefficients.
Additionally, the software’s mapping interface allows for the visual representation of spatial dependency,
which is critical for communicating data-driven management findings to policymakers. As a specialized
instrument, GeoDa facilitates the transition from raw numeric datasets to actionable administrative insights,
providing the analytical rigor required for studies within the Revenue Journal: Management and
Entrepreneurship framework. The standardization of this instrument ensures that the research process is
transparent, verifiable, and consistent with current best practices in regional science research.
3.6 Data Analysis Method
The analysis employs GeoDa software to perform complex spatial computations, beginning with global
Moran’s I to measure the intensity of spatial clustering across the entire national territory. Subsequently,
Local Indicators of Spatial Association (LISA) are deployed to classify specific regions into High-High, LowLow, High-Low, and Low-High categories. The spatial weighting matrix is constructed using queen
contiguity, which is a standard in spatial econometrics to capture interactions between adjacent units,
ensuring the analysis reflects the physical and economic connectivity of Indonesia’s diverse regional
landscape. The computational process includes performing 999 permutations to achieve robust statistical
significance, thereby mitigating the risk of identifying false positive clusters. This process is essential for
isolating systemic regional vulnerabilities from localized, idiosyncratic fluctuations. By systematically
analyzing the spatial lags against observed prevalence rates, the research provides a clear, quantitative basis
for identifying regions that require immediate administrative attention. The integration of Moran's I and
LISA within the analysis ensures a dual-layered approach—evaluating both the overarching national pattern
and the specific, local neighborhood relationships that define the food security landscape.
54

Distribution Pattern Analysis… / Ana Novianti..

4. RESULT AND DISCUSSION
The research results presented in this section provide a comprehensive spatial diagnostic of food inadequacy across
Indonesia in 2025. By integrating thematic mapping, Moran’s I autocorrelation, and LISA cluster analysis, this study
identifies the structural determinants of food vulnerability. The findings are organized hierarchically, starting from a
national-level overview of distribution patterns, moving to the identification of statistically significant local clusters, and
culminating in an analysis of the spatial hotspots that define Indonesia’s food security landscape. These results are
derived from secondary datasets provided by the Central Statistics Agency (BPS), and their interpretation is supported
by established spatial economic theory and recent empirical literature.
4.1 Spatial Distribution Pattern of Food Inadequacy Prevalence
The thematic mapping of food inadequacy prevalence (Figure 1) reveals a striking regional dichotomy
between Western and Eastern Indonesia. The visual evidence confirms that high-prevalence areas, shaded
in darker tones, are concentrated in the remote and mountainous regions of Papua, Maluku, North Maluku,
and parts of East Nusa Tenggara, whereas low-prevalence zones dominate Java, Bali, and coastal Sumatra.
Statistical evidence from the 2025 dataset shows that regencies such as Mamberamo Raya, Deiyai, Puncak,
and Yahukimo consistently record prevalence levels exceeding 40%, in stark contrast to urbanized centers
in Java where levels remain below 5%. This spatial disparity is not merely geographical but reflects deepseated issues in regional development and infrastructure accessibility. The findings align with recent
empirical studies which argue that persistent food vulnerability in the eastern archipelago is primarily
driven by high logistics costs and the physical challenges of maintaining a reliable food supply chain to
remote, archipelagic areas.

Figure 1. Spatial Distribution Pattern of Food Inadequacy Prevalence

4.2 Identification of Spatial Cluster Significance
The application of LISA Significance Mapping (Figure 2) transitions the analysis from descriptive mapping
to inferential spatial statistics. The findings demonstrate that food inadequacy does not occur as a random
phenomenon but forms statistically significant spatial clusters across the archipelago (p < 0.05). Significant
spatial associations are predominantly concentrated in the Eastern region, where geographical isolation
and mountainous terrain create strong inter-regency dependency in food security outcomes. The
concentration of significant regencies in Papua, Maluku, and the interior of Kalimantan suggests that
neighboring administrative units share similar socioeconomic vulnerabilities, such as limited market access
and low purchasing power. Recent research validates this clustering effect, noting that contiguous regencies
with similar structural deficits tend to "trap" each other in cycles of food insecurity. This clustering confirms
the validity of the spatial dependency hypothesis, where the socioeconomic conditions of one regency
directly influence the status of its immediate neighbors, creating a ripple effect that mandates a regionally
integrated management approach rather than isolated, regency-specific interventions.

REVENUE JOURNAL: MANAGEMENT AND ENTREPRENEURSHIP 4 (1) Hal 49-62

55

Figure 2. LISA Significance Map of Food Inadequacy Prevalence
.
4.3 Hotspot Areas and Spatial Dependence Analysis
The spatial analysis of High-High and Low-Low clusters (Figure 3) provides the definitive evidence
required for targeted managerial policy. High-High clusters, or "insecurity hotspots," identify regions
where high prevalence in one regency is surrounded by neighboring regencies with similarly high
prevalence, a pattern observed extensively in Eastern Indonesia. Conversely, Low-Low clusters form "food
security coldspots" in the developed economic corridors of Java and Bali. These results are grounded in
Sen’s Entitlement Theory, which suggests that economic access—facilitated by better infrastructure and
stronger market competition—is the primary driver for the observed low-prevalence clusters in the
Western region. The findings indicate that management efforts must prioritize the "High-High" regions for
infrastructure-led intervention while using the "Low-Low" regions as models for sustainable food logistics.
This hierarchical classification of space is vital for policymakers, as it allows for the precise allocation of
scarce resources to areas where the spatial spillover of food inadequacy is most severe.

Figure 3. Spatial Distribution of Hotspot (High-High) and Coldspot (Low-Low) Clusters

4.4 Spatial Autocorrelation: Moran’s I Scatterplot
The global spatial autocorrelation result (Moran’s I = 0.804) serves as the primary statistical evidence of
strong national interdependence (Figure 4). The scatterplot indicates that the majority of observations lie
within the first (High-High) and third (Low-Low) quadrants, confirming that Indonesian regencies tend to
cluster into distinct "food-secure" or "food-insecure" zones. This very high Moran’s I value indicates a
strong, positive spatial dependency that defies random distribution, suggesting that food insecurity in
56

Distribution Pattern Analysis… / Ana Novianti..

Indonesia is fundamentally a regional systemic issue rather than a localized one. The findings confirm the
theoretical assertions of Tobler’s First Law of Geography, which states that nearby regions are more
similar than distant ones. The magnitude of this autocorrelation (0.804) is statistically robust, implying
that national food security policy must treat the archipelago as a series of integrated spatial nodes rather

than 514 independent units. Any managerial strategy that fails to account for this strong positive
autocorrelation is likely to underperform, as the socioeconomic vulnerabilities of one regency are
functionally tied to the status of its neighbors.
Figure 4. Moran’s I Scatterplot

4.5 Managerial Implications and Future Research
The research results underscore a clear managerial imperative: the transition toward a region-based
approach for food security policy. The empirical evidence from this study demonstrates that Eastern
Indonesia, as the primary High-High cluster, requires intensive, cross-regency logistical support and
infrastructure development. The findings directly inform the scope of the Revenue Journal: Management
and Entrepreneurship by reframing food security as a cross-regional management challenge. Future
research should integrate the qualitative insights from field activity logs—which confirm that localized
food distribution bottlenecks are often caused by managerial failures in coordinating inter-island
transport—with the quantitative spatial clusters identified here. The documentation of these activities
shows that even where food is locally available, the "management of movement" remains the critical
barrier to security. Future studies are therefore recommended to adopt mixed-method frameworks that
evaluate the entrepreneurial potential of local food supply chain innovations within identified High-High
clusters to provide a more holistic management solution.
Discussion
The spatial diagnostic of food inadequacy across 514 regencies in Indonesia reveals that food insecurity is a
fundamentally structural and clustered phenomenon rather than a randomized occurrence. By confronting
the empirical results with established development theories—specifically Sen’s Entitlement Theory and
Perroux’s Growth Pole Theory—this study demonstrates that High-High clusters in Eastern Indonesia are
symptomatic of systemic "managerial disconnects" in national distribution networks. Unlike studies that
attribute food insecurity solely to agricultural productivity, this research extends the discourse by
identifying geographical isolation and infrastructural inadequacy as the primary drivers of spatial clustering.
The strong positive autocorrelation (Moran’s I = 0.804) challenges the efficacy of uniform, top-down food
policies, suggesting instead that the persistent vulnerability of the Eastern archipelago is an outcome of
cumulative socioeconomic exclusion. By confirming these clusters, this research demonstrates that food
security policies must shift toward a spatially integrated management framework that accounts for interregency dependency and the compounding effect of peripheral underdevelopment.
The identification of unique "Low-Low" clusters in the industrial hubs of Java and Bali provides a significant
point of departure from global studies that often posit urbanization as a singular driver of food insecurity.
While some international scholars argue that rapid urbanization exacerbates food access vulnerabilities
through market volatility, our findings demonstrate the opposite in the Indonesian context, where urban
REVENUE JOURNAL: MANAGEMENT AND ENTREPRENEURSHIP 4 (1) Hal 49-62

57

density functions as a stabilizing "growth pole" that facilitates market access and logistical efficiency. This
uniqueness highlights a localized socioeconomic phenomenon: urban centers act as regional hubs that
anchor food availability, whereas peripheral regencies in the East, lacking similar managerial and
infrastructure investment, are marginalized into high-risk clusters. The persistence of these disparities, even
in an era of centralized digital monitoring, suggests an anomaly where technological adoption has not yet
translated into equitable food accessibility for remote regions. This confirms that the challenge is not one of
food scarcity, but of administrative and logistical management within the Indonesian regional governance
architecture.
Integrating these empirical findings with the conceptual framework of Muraqabah (as a pedagogical
metaphor for systemic vigilance and accountability in regional management), this study proposes that food
policy requires a higher level of "administrative oversight" regarding inter-island logistics. Just as the
concept of Muraqabah emphasizes deep awareness and responsibility in one’s actions, effective regional
management requires constant monitoring of the "spatial spillover" of food insecurity from one regency to
another. The High-High clusters identified in Papua and Maluku are indicative of a systemic failure to
maintain this vigilance, leading to long-term resource stagnation. By treating regencies as interconnected
nodes in a national supply chain, the state can move beyond normatively driven aid distributions toward a
proactive management model that optimizes local resources and strengthens logistical corridors. This shift
represents a unique theoretical contribution, suggesting that regional food security is an act of collective
institutional responsibility, where the prosperity of central nodes is inherently tethered to the resilience of
peripheral clusters.
The practical implications of these findings necessitate a radical restructuring of local government resource
allocation, moving away from fragmented, regency-bound strategies toward integrated "Spatial Food
Management Zones." The data reveals that policies focused on individual regency improvements are
insufficient when regional autocorrelation remains high; instead, interventions must be coordinated across
cluster boundaries. This study recommends the implementation of localized food logistics hubs that serve
not just single regencies, but clusters identified in the LISA analysis, thereby achieving economies of scale in
distribution. Furthermore, by formalizing the management of these clusters, policymakers can reduce the
logistical costs that currently prevent Eastern Indonesia from achieving food parity with the Java-Bali
corridor. In summary, this research changes the practical approach from generic food aid to evidence-based
logistical management, providing a blueprint for sustainable development that directly addresses the root
causes of regional inequality rather than merely alleviating the symptoms of food inadequacy.

4. CONCLUSION
4.1 Conclusion
1.

The spatial distribution of food inadequacy across Indonesian regencies and municipalities in
2025 exhibits a strong, statistically significant clustering pattern, confirming that food
vulnerability is inherently spatial rather than random.

2.

High-High clusters (insecurity hotspots) are definitively concentrated in Eastern Indonesia,
particularly in Papua, Maluku, and East Nusa Tenggara, identifying these as priority areas for
immediate and targeted managerial intervention.

3.

The strong positive spatial autocorrelation (Moran’s I = 0.804) indicates that the food security
status of a regency is deeply interdependent with its neighboring administrative units, rendering
isolated or regency-specific policy approaches ineffective.

4.

Developmental disparities in infrastructure, market connectivity, and regional economic growth
poles explain the contrast between the high-risk Eastern clusters and the low-risk Low-Low
clusters concentrated in the Java-Bali economic corridor.

5.

Effective regional food security management requires a paradigm shift from uniform national
distribution programs toward a spatially integrated, cluster-based approach that prioritizes
logistical connectivity in historically underserved peripheral regions..

4.2 Recommendations
The findings demonstrate that sustainable food security in Indonesia necessitates a transition
toward region-based management that accounts for the documented spatial interdependence
between neighboring administrative units. Policymakers should prioritize the development of
58

Distribution Pattern Analysis… / Ana Novianti..

localized, cross-regency food logistics hubs within identified High-High clusters to reduce logistical
barriers and mitigate the clustering of food vulnerability. It is recommended that future
infrastructure investments be directed toward optimizing connectivity between these hotspots and
their nearest economic hubs to facilitate market accessibility and economic integration.
Furthermore, government agencies should adopt a spatially dynamic monitoring system that allows
for the real-time adjustment of resource allocation based on emerging cluster shifts rather than
static annual assessments. For future research, it is highly recommended that scholars adopt a
mixed-methods longitudinal approach, combining spatial econometric modeling with qualitative
field investigations into the operational management of local supply chains. Investigating the role of
digital entrepreneurship in local food distribution within vulnerable clusters could also provide
critical insights into how private-sector innovation might bridge the gap in state-led distribution,
thereby providing a robust framework for long-term regional resilience.

6. REFERENCES
Abdolhosseinzadeh, M., & Abdolhamid, M. (2026). A comparative analysis of ‘development
governance’ and ‘development management’ curricula in selected international universities.
Curriculum Journal. https://doi.org/10.1002/curj.70063
Al Fadhel, L., Athari, S. A., Sassine, M. E., Olanrewaju, V. O., & Djafar Henni, M. (2026). Do artificial
intelligence, knowledge management, and education skills drive sustainable energy transition?
Social Sciences and Humanities Open, 13. https://doi.org/10.1016/j.ssaho.2026.102766
Aljohani, A. H., Alsufiani, H. A., AlSaadi, A. M., Alem, N. A., AliAbusunoon, M., & Madkhali, A. I. (2026).
Severe Asthma Exacerbations in the Pediatric Intensive Care Unit: Clinical Profile,
Management,
and
Outcomes—Retrospective
Study.
Children,
13(5).
https://doi.org/10.3390/children13050710
Arora, S., Kumar, S., & Kumar, S. (2023). Artificial Intelligence in Disaster Management: A Survey. In
M. Saraswat, C. Chowdhury, K. M. C, & A. H. Gandomi (Eds.), Lecture Notes in Networks and
Systems (Vol. 552, pp. 793–805). Springer Science and Business Media Deutschland GmbH.
https://doi.org/10.1007/978-981-19-6634-7_56
Basilico, P., D’Adamo, I., Del Giudice, M., Di Santo, F., Gastaldi, M., Passarelli, F., & Uricchio, A. F.
(2026). Sustainable Schools and Knowledge Management: Driving Urban and Social
Transitions for Sustainable Development. Sustainable Development, 34(S1), 915–935.
https://doi.org/10.1002/sd.70215
Beno, M., & Caganova, D. (2026). Smart Sustainability in Management: Younger Supervisors, Older
Subordinates. In K. L. & P. D. (Eds.), EAI/Springer Innovations in Communication and Computing
(pp. 205–211). Springer Science and Business Media Deutschland GmbH.
https://doi.org/10.1007/978-3-032-16896-2_15
Bolortuya, P., Weida, H., Arrive, J. T., & Getele, G. K. (2026). The impact of corporate social
responsibility on educational management: evidence from learner tendencies. International
Journal of Sustainability in Higher Education, 27(2), 361–380. https://doi.org/10.1108/IJSHE03-2024-0191
Chembolu, V., & Dutta, S. (Eds.). (2024). 3rd International Conference on River Corridor Research
and Management, RCRM 2023. Lecture Notes in Civil Engineering, 470.
https://www.scopus.com/pages/publications/85192172561?origin=resultslist
Ciptomulyono, U., Fazlurrahman, H., Akbar, R. A., & Nareswari, N. (2026). Do green human resources
management, digital technology, and green innovation shape university sustainability?
Frontiers in Sustainability, 7. https://doi.org/10.3389/frsus.2026.1765721
Circular economy, resource management, and policy drivers of environmental performance:
REVENUE JOURNAL: MANAGEMENT AND ENTREPRENEURSHIP 4 (1) Hal 49-62

59

evidence
from
EU-22.
(2026).
https://doi.org/10.1016/j.resglo.2026.100360

Research

in

Globalization,

12.

Daly, S., & Belkhamsa, M. (2026). Unveiling the Path to Sustainability in Africa: Insights from
Education, Resource Management, and Economic Complexity. SAGE Open, 16(1).
https://doi.org/10.1177/21582440251409119
De Feo, G. (2026). Engaging Environmental Education for Sustainable Waste Management—The
Greenopoli
Education
Framework.
Recycling,
11(1).
https://doi.org/10.3390/recycling11010002
Dias, F., Gomes, D., Lavaredas, A., & Esteves, P. (2026). From ecological values to sustainable
practices management: a study in Portuguese tourism companies. Frontiers in Sustainable
Tourism, 5. https://doi.org/10.3389/frsut.2026.1791534
Dipankar, P., Salazar, D., Dennard, E., Mohiyuddin, S., & Nguyen, Q. C. (2025). Artificial intelligence
based advancements in nanomedicine for brain disorder management: an updated narrative
review. Frontiers in Medicine, 12. https://doi.org/10.3389/fmed.2025.1599340
Fárka, M., Fialová, J., & Bamwesigye, D. (2026). FOREST CLASSROOM AS A TOOL OF LANDSCAPE
MANAGEMENT: INTEGRATING ENVIRONMENTAL EDUCATION, FORESTRY AND GAME
MANAGEMENT. In F. J., B. D., & H. V. (Eds.), Public Recreation and Landscape Protection (Vols.
2026-May,
Number
2026,
pp.
151–155).
Mendel
University
in
Brno.
https://doi.org/10.11118/978-80-7701-087-0-0151
Kassetty, N., Alang, K., Paruchuru, V., Sharma, S., Goel, P., & Kumar, S. (2025). Cloud Security
Management: Advanced AI Techniques for Anomaly Detection and Response Automation. 2025
International Conference on Networks and Cryptology, NETCRYPT 2025, 1620–1624.
https://doi.org/10.1109/NETCRYPT65877.2025.11102438
Lewis, G. T., Littlewood, J. R., & Sharma, S. (2026). Optimising University Space Usage for Energy and
Carbon Management, a UK Case Study. In L. J.R., H. R.J., H. R.J., J. L.C., & J. L.C. (Eds.), Smart
Innovation, Systems and Technologies: 113 SIST (pp. 585–597). Springer Science and Business
Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-5069-9_49
Marseglia, G., Miraglia del Giudice, M., Licari, A., Indolfi, C., Tosca, M. A., & Ciprandi, G. (2026).
Azelastine-fluticasone intranasal therapy: A paradigm shift in pediatric allergic rhinitis
management. A point of view of the Italian Society of Pediatric Allergy and Immunology.
Pediatric Allergy and Immunology, 37(3). https://doi.org/10.1111/pai.70320
Mendoza, I. L., & Pasigon, C. P. (2026). Towards Sustainable Waste Management: Profiling the School
Waste in Eastern Potia National High School. Nature Environment and Pollution Technology,
25(2). https://doi.org/10.46488/NEPT.2026.v25i02.D1833
Mere, K., Lukitaningtyas, F., & Sungkawati, E. (2024). Competence and Motivation: Keys to Success
for BUMDes Management. In Dinasti International Journal of Economics, Finance &Accounting
(DIJEFA ….
https://scholar.google.com/citations?view_op=view_citation&hl=en&user=qhPL2fIAAAAJ&p
agesize=100&citation_for_view=qhPL2fIAAAAJ:g3aElNc5_aQC
Metwally, A. B. M., Ali, S. A. M., & Mohamed, A. T. I. (2024). Thinking Responsibly About Responsible
AI in Risk Management: The Darkside of AI in RM. 2024 ASU International Conference in
Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024, 1–5.
https://doi.org/10.1109/ICETSIS61505.2024.10459684
Monge, J. F., & Ruiz, J. L. (2026). Planning in management: identifying alternative ways of improving
performance toward best practices. International Transactions in Operational Research, 33(2),
1362–1384. https://doi.org/10.1111/itor.13534
Sharma, V., Kumar, R., & Sood, K. (2026). Personal vs. organizational financial management:
60

Distribution Pattern Analysis… / Ana Novianti..

systematic review and future research
https://doi.org/10.1108/MD-01-2025-0202

agenda.

Management

Decision,

1–45.

Sujala Reddy, R. S., & Vishalakshi Prabhu, H. (2023). Role of Artificial Intelligence in Request
Management: A Comprehensive Review. IEEE Region 10 Humanitarian Technology Conference,
R10-HTC, 915–921. https://doi.org/10.1109/R10-HTC57504.2023.10461869
Wang, T., Fan, Y., Li, Z., Jiao, X., Fang, Q., Ma, J., & Lei, J. (2026). Exploring Self-Management–Based
Mobile Health User Typologies and Associations Between User Types and Satisfaction With
Key Mobile Health Functions: Comparative Study of Various Fitness and Weight Management
App User Types. JMIR Medical Informatics, 14. https://doi.org/10.2196/64860
Widodo, T., Muhammad, I., Darmayanti, R., Nursaid, N., & Amany, D. A. L. (2023). Digital-based
educational financial management: A literature review. In Indonesian Journal of Educational
Management
and
Leadership
(Vol.
1,
Number
2,
pp.
146–167).
https://scholar.google.com/citations?view_op=view_citation&hl=en&user=3YBrjMAAAAJ&cstart=200&pagesize=100&citation_for_view=3YBrj-MAAAAJ:9BrBD10KdIMC
Yassin Alzyoud, A. A. (2022). Artificial Intelligence for Sustaining Green Human Resource
Management: A Literature Review. 2022 ASU International Conference in Emerging
Technologies for Sustainability and Intelligent Systems, ICETSIS 2022, 321–326.
https://doi.org/10.1109/ICETSIS55481.2022.9888840
Yevle, D. V, & Mann, P. S. (2025a). Artificial intelligence-based classification for waste management:
a systematic review and future direction. Iran Journal of Computer Science, 8(4), 1291–1330.
https://doi.org/10.1007/s42044-025-00336-7
Yevle, D. V, & Mann, P. S. (2025b). Artificial Intelligence-Based Waste Management: A Review of
Classification, Techniques, Issues, and Challenges. Wiley Interdisciplinary Reviews: Data Mining
and Knowledge Discovery, 15(2). https://doi.org/10.1002/widm.70025
Zhang, H., & Hope, A. J. (2026). Strategic responses to institutional pressures in university carbon
management: a neo-institutional perspective. International Journal of Sustainability in Higher
Education, 1–18. https://doi.org/10.1108/IJSHE-05-2025-0481

REVENUE JOURNAL: MANAGEMENT AND ENTREPRENEURSHIP 4 (1) Hal 49-62

61