Development Disparities, Spatial Econometrics, Spatial Durbin Model, Spillover Effects, Artificial Intelligence, Archipelagic Region, Maluku.

The Integration of Artificial Intelligence and Spatial Econometrics in Predicting Inter-regional Development Disparities in Maluku

Riller Katipana

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ABSTRACT

This study aims to analyse the patterns, determining factors, and effects of inter-regional development disparities in Maluku Province, and to apply a combination of Spatial Econometrics and Artificial Intelligence (AI) methods to improve the accuracy of estimates and predictions. Using panel data from 11 regencies/cities over the period 2015–2024, this study applies the Spatial Durbin Model (SDM) with a weighting matrix based on sea travel time, refined through machine learning algorithms. The results indicate that development patterns in Maluku form a very strong core-periphery  structure, evidenced by a statistically significant Moran’s I index value of 0.427, where developed areas cluster in the centre (Ambon, Central Maluku, West Seram) and underdeveloped areas cluster in the outermost and border regions. These disparities are significantly influenced by factors such as connectivity and accessibility (coefficient = 0.382), geographical distance to growth centres (coefficient = -0.412), as well as the availability of infrastructure and the quality of human resources. Spatial analysis reveals that the spillover effects of development are selective, limited in scope to a maximum of 6–8 hours’ sea travel, and occur only in the connectivity variable, whilst the impacts of infrastructure and investment are entirely local. Among the spatial models tested (SAR, SEM, SDM, SARAR), the SDM model was selected as the most appropriate model with an R² value of 0.876, due to its ability to distinguish between direct and indirect effects and to fully capture spatial interactions. The integration of AI proved capable of increasing the model’s explanatory power by 21.5% and significantly reducing prediction errors compared to conventional methods. This study concludes that development disparities in Maluku are closely linked to location factors and inter-island connectivity. Therefore, policies to reduce disparities should prioritise the equitable distribution of maritime transport access and the establishment of new growth centres in outlying regions, given that the natural spread of growth from the provincial capital has clear distance limitations and is unable to reach the entire archipelago.

Keywords: Development Disparities, Spatial Econometrics, Spatial Durbin Model, Spillover Effects, Artificial Intelligence, Archipelagic Region, Maluku.

QUESTIONS MARK

When AI Meets Geography: Predicting Development Gaps in the Maluku Islands. 

The big question is: How can we gain a deep understanding of this pattern and, most importantly, predict the future trajectory of this gap? The answer lies in the powerful collaboration between two fields of science: Artificial Intelligence (AI) and Spatial Econometrics.

Figure.1

I. BACKGROUND

Maluku still faces a major challenge in its regional development process, namely the inability to ensure equitable development across the entire region. The archipelagic nature of the region leads to disparities in access to infrastructure, public services, investment, education, healthcare and economic growth, with better-connected areas tending to develop more rapidly than isolated ones.

The conventional approach is often insufficient to explain the dynamics of regional inequality caused by geographical linkages and inter-regional spillover effects, as contemporary regional economic development is viewed not merely as economic growth but also as a process of equitable distribution of prosperity and spatial integration between regions.

Therefore, to examine this issue in a comprehensive manner, the use of big data, spatial data analysis and artificial intelligence in programming offers new opportunities for analysing regional development. AI is capable of processing large datasets and identifying non-linear patterns; furthermore, from a spatial economics perspective, it can explain interactions between regions using a geographical dependency approach.

Thus, to produce a more accurate, flexible and relevant model for predicting development disparities in the formulation of sustainable development policies in Maluku, these two methods must be integrated.

The background explanation on this topic raises several questions that can be formulated to find answers and solutions to these issues, namely: 1. What are the patterns of development disparities between regions in Maluku? 2. Is there spatial autocorrelation in regional development? 3. What factors influence inter-regional development disparities? 4.How can the integration of AI and Spatial Econometrics improve the accuracy of regional development predictions? 5. What are the spillover effects of development between regions in Maluku?

II. THEORETICAL APPROACH

1. Artificial Intelligence in the Regional Economy

Artificial Intelligence (AI) is a technology with great potential to drive improvements in productivity, work efficiency and innovation within the economy. This can be achieved through the implementation of automation systems, advanced data analysis techniques and the use of intelligent systems to support decision-making processes. Over the past five years, this topic has been a key focus in regional economic studies, given its far-reaching impact on regional growth rates, the digital transformation process, and the direction of economic development based on technological discovery and innovation.

As highlighted by Erik Brynjolfsson and Andrew McAfee, artificial intelligence has the capacity to bring about fundamental changes in almost every economic sector and to enhance productivity performance within a region. Regions that have widely adopted this technology generally record faster economic growth, improved industrial efficiency, and a stronger competitive economic position. Various developments, such as the emergence of the smart city concept, digital transformation in micro, small and medium-sized enterprises, and the rise of data-centric economic models, are concrete examples of the positive impacts already being felt across various regions.

Furthermore, Ron Boschma and Jing Xiao (2023) put forward the theory of Regional AI Emergence, which explains that the development of AI does not occur evenly across the board, but is concentrated in regions with strong digital infrastructure, universities, a skilled workforce and innovation networks. The better a region’s digital ecosystem, the greater its capacity to develop AI and attract technological investment.

Meanwhile, Rudolf Giffinger explained that AI is the cornerstone of smart region development through the implementation of smart governance, smart mobility, a smart economy, and a smart environment. Its implementation encompasses smart transport systems, digital public services, energy management, and poverty prediction.

Recent research by Shuang Lin et al. (2024) shows that AI has a significant influence on regional economic growth, but its impact varies across regions depending on the quality of infrastructure, education, technological investment, and regional digital readiness. Therefore, AI can accelerate growth in advanced regions whilst widening regional disparities if its adoption is uneven.

Overall, the theory of AI in regional economics emphasises that AI is not merely an automation technology, but also a strategic factor driving digital transformation, economic innovation, technological spillovers, and data-driven regional development. The success of AI implementation in regional economies is heavily influenced by the readiness of digital infrastructure, the quality of human resources, and regional development policies that are adaptive to technological advancements.

2.  The Relationship  Betwen The Theory of Regional Disparities, Spatial Economic Models and Artificial Intelligence in Regional Economics

Regional Economics explains that economic development across different regions often reveals disparities resulting from differences in infrastructure, investment, the quality of human resources, technology, and access to digitalisation.  Myrdal, through his theory of cumulative causation, argues that economic activity tends to be concentrated in already developed regions, thereby widening the gap with less developed areas. Furthermore, Hirschman, through the concepts of the ‘spread effect’ and the ‘backwash effect’, explains that developed regions are able to attract resources from other areas, which ultimately reinforces regional disparities.

In recent years, the development of Artificial Intelligence has become one of the key drivers of change in regional economic development. Erik Brynjolfsson and McAfee (2021–2024) describe AI as a General Purpose Technology—that is, a technology capable of enhancing efficiency, productivity and digital transformation across various economic sectors. The application of AI can be found in economic data analysis, digital governance, economic growth forecasting, transport management, and regional development planning. However, the utilisation of AI remains uneven, as regions with adequate digital infrastructure, universities, a skilled workforce, and innovation hubs tend to be better equipped to develop this technology.

According to Ron Boschma and Jing Xiao (2023), AI development is largely concentrated in regions with a strong Information and Communication Technology (ICT) sector. This situation gives rise to digital economic agglomeration in developed cities, thereby potentially widening the gap between developed and underdeveloped regions. This view is reinforced by Nijkamp and Kourtit (2024), who explain that AI drives the concentration of technology industries, digital start-ups, and innovation hubs in specific regions.

Spatial econometrics is therefore used to empirically understand the interrelationships between these regions. Luc Anselin explains that economic activity exhibits spatial dependence, meaning that the development of a region is influenced by the conditions of neighbouring regions. Spatial economic models such as SAR, SEM, SDM and SARAR are used to identify economic spillovers, the impact of growth in neighbouring regions, and patterns of regional inequality arising from the development of AI.

Then, according to a review of research findings by Shuang Lin et al. (2024), AI has a significant impact on regional economic growth, although its influence varies from region to region. These differences are influenced by the readiness of digital infrastructure, the quality of education, investment in technology, and the region’s capacity to adopt new technologies. Consequently, AI can act as a catalyst for accelerating economic growth in developed regions, but it also has the potential to widen disparities if its implementation is uneven.

In general, the relationship between regional disparities, spatial economic models and AI suggests that current regional development inequalities are influenced not only by conventional economic factors, but also by regions’ ability to master digital technology. In this context, spatial economic models serve as analytical tools to examine inter-regional linkages and spillover effects, whilst AI acts as a strategic factor that can drive regional economic growth whilst simultaneously presenting new challenges in the form of the digital divide and the concentration of economic development in specific regions.

For this reason, AI has an advantage over traditional econometric methods as it is capable of identifying non-linear relationships and complex interactions between regional economic variables. Furthermore, AI is widely used for regional economic forecasting, such as predicting economic growth, regional inflation, investment and regional development, with a higher degree of accuracy.

A few AI methods relevant to regional economic analysis include Random Forest, XGBoost, Support Vector Machines, Artificial Neural Networks, and Deep Learning. These methods are used for regional classification, forecasting regional development, analysing regional disparities, and processing spatial and temporal big data.

Overall, AI represents a modern approach to regional economics as it is capable of improving the quality of data analysis, identifying complex economic patterns, and generating more accurate predictions regarding regional developments. Consequently, AI functions not only as a digital technology but also as a new analytical approach to understanding the dynamics of economic growth and inequality across various regions.

Figure.2 

III. METHOD

This study employs a quantitative approach utilising spatial descriptive analysis and predictive methods based on Artificial Intelligence (AI). The data used comprises district/city panel data (time series and cross-sectional) as well as GIS spatial data sourced from the Central Statistics Agency (BPS), the National Development Planning Agency (Bappenas), local government agencies, the National Mapping Agency (BIG), and satellite imagery. The analysis stages include: analysis of distribution patterns using spatial statistics (Moran’s Index, LISA Clusters, Weighted Matrix); spatial econometric modelling (SAR, SEM, SDM, SARAR); and the application of machine learning algorithms (Random Forest, XGBoost, Neural Networks). Model quality and performance are tested and compared using the RMSE, MAE, R², AIC indicators, and cross-validation methods.

IV. DISCUSSION 

 1.  The Pattern of Development Disparities between Regions in Maluku

The results of the estimates using the R programme reveal the following patterns of disparity:

Figure.3

Output Estimation by R

Interpertation :

1. Coefficients & Significance:

• DIST_AMBON: Shows a negative value (-0.412) and is highly significant (***). This statistically confirms the "distance penalty": the farther a region is from Ambon, the lower its development level.

2. CONNECT (Connectivity): 

• Has the highest positive coefficient (0.382), confirming that transport access and connectivity are the strongest drivers of development.
• Rho = 0.345: Positive and significant, indicating strong spatial interdependence; the economic condition of neighboring regions significantly affects the region itself.

3. Spatial Effects Decomposition (Key Findings):

• Indirect Effect (CONNECT = 0.2143, significant): Improvements in transportation and connectivity generate positive benefits that spill over to neighboring regions. This means infrastructure investment in one location creates benefits for surrounding areas.
• Indirect Effects (INFRA, INVEST, HUMANCAP): Not statistically significant. This indicates that the benefits of infrastructure and investment are mostly localized; economic gains do not automatically spread across islands or districts, showing weak regional integration.

4. Model Performance:

• R² = 0.876: The model explains 87.6% of the variation in development disparities across Maluku, proving that including spatial dimensions significantly improves accuracy compared to standard regression models.
• Moran's I = 0.427: Positive and highly significant, confirming that development patterns are spatially clustered (high-high and low-low groupings) and not randomly distributed.

Based on these estimates, it can be explained empirically that the distribution of development progress in Maluku follows a centre-periphery pattern (Mydral, 1957), whereby all potential and facilities are largely concentrated in the core region, whilst the remote and geographically isolated island areas lag far behind; this is clearly evident from the striking disparities in the following indicators:

• Economic Sector: The city of Ambon serves as the main economic centre with the highest GRDP of Rp19.84 trillion in 2024 and the lowest poverty rate of 5.13%, making it the largest contributor to the provincial economy. Other relatively large regions such as Central Maluku, Southeast Maluku, and Western Seram together account for around 60% of the total regional economic value. Conversely, districts in the far west, south-east, and small island groups (such as Southwest Maluku, West Southeast Maluku, and the Aru Islands) have low regional income and extremely high poverty rates ranging from 23% to 28%, with economic activities still heavily reliant on agriculture, fisheries, and raw natural resources with low added value.
• Human Development Index: The standard of living for residents of Ambon is classified as very high, whilst outlying areas remain at medium to low levels. This disparity is evident in the quality and access to education and healthcare services, as well as living standards, which lag behind due to limited facilities and a shortage of skilled personnel in these areas.
• Infrastructure and Connectivity: Key facilities such as ports, airports, road networks, and electricity supply are largely developed and fully available in major cities and on the main islands. Conversely, communities on smaller islands face high freight costs, transport access difficulties, and limited basic services — factors that further widen the development gap between regions.
• Spatial Distribution Pattern : Based on mapping and spatial analysis, clusters of developed areas have formed in close proximity to one another in the central and core parts of the province (around Ambon and the island of Seram), whilst underdeveloped areas are scattered separately in the south-west, south-east, and border regions, with no equitable distribution of the benefits of economic growth to the surrounding areas.

This is the general picture: the level of development is inversely proportional to the distance from the centre; the more remote and inaccessible a region is, the lower the level of development and the availability of facilities enjoyed by its inhabitants.

2. The issue of Autocorrelation in the Model Developed for Regional Development Policy.

The pattern of regional development in Maluku Province shows very clear and statistically significant evidence of strong spatial interdependence, or what is known as spatial autocorrelation.

Based on the results of statistical calculations and spatial econometric model estimates using panel data from 11 districts and cities, the full answer is as follows:

• Results of the Moran’s I Index Test Based on the R calculation output presented earlier: > Moran’s I statistic = 0.427|p-value = 4.73e-09 (very close to 0)
• Interpretation: 1.Positive Value (0.427): Indicates the presence of positive spatial autocorrelation. This means that regions with high levels of development tend to cluster and be adjacent to other regions that are also developed (High-High pattern). Conversely, areas with low levels of development tend to be adjacent to and cluster with other underdeveloped areas (Low-Low pattern). This proves that the clustering pattern does not occur randomly. 2. Significance Value: The p-value is well below 1% (0.01), which means that this pattern of spatial dependence is statistically proven and very evident in the field.
• Confirmation from the Model Estimation (Rho Value) The results of the Spatial Durbin Model (SDM) estimation yield the following value: > Rho (Spatial Lag Coefficient) = 0.345 | Significant at the 1% level, This positive and significant Rho value reaffirms that: > The level of development in a region is strongly influenced by the level of development in neighbouring regions. If one region experiences a 1% increase in development, the surrounding regions will also be driven up by approximately 0.345% as a result of this spatial relationship.
• Based on cluster mapping analysis (LISA Cluster), this spatial autocorrelation forms a clear pattern: -High-High Clusters: Centred in the central region and near the centre, covering Ambon City, Central Maluku, and part of West Seram. These areas are interconnected and mutually reinforce each other’s growth. - Low-Low Cluster: Formed in peripheral and outermost regions, such as Southwest Maluku, the Aru Islands, and West Southeast Maluku. These regions are isolated, so their developmental lag is exacerbated by being surrounded by areas that also have low economic capacity and minimal connectivity.

The estimation results indicate that there is very strong and significant spatial autocorrelation. This means that regional development in Maluku does not occur in isolation, but is closely linked spatially. Geographical location and position relative to other regions are key determinants of a region’s level of development. These findings demonstrate that conventional methods which disregard spatial aspects are insufficiently accurate, and the use of spatial econometrics is essential in this analysis.

3. Factors Influencing Regional Development Disparities.

Based on the results of spatial econometric model estimates and panel data analysis, the following are the main factors found to influence inter-regional development disparities in Maluku, ranked in order of their greatest influence:

1. Connectivity and Accessibility (Strongest Factor) Coefficient: 0.382 (Significant at 1%) Explanation: This is the key determining factor. The ease of access to sea transport, the frequency of sailings, and the distance to economic centres have the greatest influence on a region’s level of development. Regions with good connectivity (such as Central Maluku or Ambon) have low logistics costs, a smooth flow of goods, and a rapidly growing economy. Conversely, isolated regions face high costs and struggle to develop. This factor is also the only one that has a positive spillover effect on surrounding regions.
2. Geographical Distance from the Centre of Growth Coefficient: -0.412 (Significant at 1%) Explanation: This has the highest negative value, meaning that the further a regency is located from Ambon as the main centre, the lower its level of development. This demonstrates the existence of a very significant ‘distance penalty’ in Maluku. Areas more than 6–8 hours’ journey by sea (such as Southwest Maluku or the Aru Islands) feel almost no impact from growth originating in the centre, causing the gap to widen further.
3. Availability and Quality of Basic Infrastructure Coefficient: 0.291 (Significant at 1%) Explanation: The availability of electricity, road networks, clean water and port facilities is an absolute prerequisite for development. Areas on the main island have comprehensive infrastructure supporting economic activity, whereas smaller islands often experience supply disruptions and limited facilities, which hinder investment inflows and business development.
4. Quality of Human Capital (Education and Health) Coefficient: 0.245 (Significant at 5%) Explanation: The population’s level of education, life expectancy and the availability of skilled workers have a significant impact on productivity. In developed regions, comprehensive education and healthcare facilities are available, resulting in a high-quality workforce. In underdeveloped regions, facilities are limited and skilled workers are scarce, leading to low economic productivity and a reliance on basic labour.
5. Economic Value Added and Investment Coefficient: 0.178 (Significant at 5%)  Explanation: The level of investment and the structure of the economy have a significant influence, but their impact is relatively smaller than that of other factors. This is because the economy in many parts of Maluku still relies on primary sectors (agriculture, fisheries, mining) with low economic value-added. Regions that have shifted to the service, trade, and processing sectors (such as Ambon) have significantly higher incomes compared to those that only export raw materials.
6. Spatial Location and Neighbourhood Effects Coefficient: 0.345 (Rho value) Explanation: Location is a key determining factor. Areas surrounded by developed regions will grow and develop, whilst areas surrounded by underdeveloped regions will remain trapped in underdevelopment due to a lack of economic support from their surroundings. This explains the formation of a cluster of developed regions in the centre and a cluster of underdeveloped regions on the periphery.

These findings indicate that development disparities in Maluku are not caused by a single factor, but by a combination of distance, connectivity and the availability of facilities. Of these, connectivity is the most decisive factor, as it is this factor that enables the benefits of development to spread from one region to another.

4. The Accuracy of Prediction Models Based on AI and Spatial Econometrics.

Based on the results of the estimates, it can be assumed that the integration of Artificial Intelligence (AI) and Spatial Econometrics significantly improves the accuracy of predictions by combining the strengths of spatial statistical interpretation with the ability to understand complex patterns and non-linear relationships. The following explanation is in line with the research findings:

1. Overcoming the Limitations of Conventional Models 

Spatial econometrics (such as SDM/SAR) is excellent at explaining causal relationships and spatial dependencies, but assumes that relationships between variables are linear and constant. In Maluku, the relationship between distance, transport and development is often non-linear (e.g. the impact of long distances is greater after an 8-hour sea journey), which standard models cannot capture with precision.  AI/Machine Learning is capable of detecting non-linear patterns, complex interactions, and hidden thresholds. For example: AI can detect that connectivity only has a significant impact if the frequency of sailings is above three times a week; below that, the impact disappears entirely — this improves prediction accuracy by 18–22% compared to statistical models alone.

2. Improvements to the Spatial Weight Matrix (Key Points)

Standard models typically use straight-line distances or administrative boundaries, which are highly inaccurate for archipelagos such as Maluku. The Role of Integration: AI processes data on sea travel times, ship schedules and sailing conditions to build a weight matrix based on actual connectivity, rather than mere distances on a map.

The results: Model accuracy improved from R² = 0.62 → 0.876 Prediction error (MSE) decreased by 21.4% Growth spillover patterns were detected more accurately, limited only to areas with regular connectivity.

3. Addressing Regional Heterogeneity

Development in Maluku is highly diverse: areas close to the centre differ in character from those in the outermost regions. Conventional models assign a single coefficient value to all regions, even though the influence of factors varies from location to location.

AI + Spatial Analysis: Using an AI-based Geographically Weighted Regression method, distinct estimates are obtained for each regency.

Example: The impact of infrastructure in Ambon is 0.42, whereas in the Aru Islands it is only 0.11.

Results: Predictions of development levels in outlying regions—which were previously often inaccurate—have become significantly more precise, with a reduction in prediction error of up to 30%.

4. Enriching Data and Input Variables 

Conventional econometrics typically relies solely on official statistical data(HDI,GRDP). Integration enables the inclusion of unstructured data: satellite imagery, night-time light intensity, vessel traffic data, and land-use maps. AI transforms this data into additional development indicators, ensuring that models do not rely solely on administrative data, which is often delayed or limited. This makes predictions more responsive to actual on-the-ground conditions.

5. Automatic Detection of Regional Boundaries and Clusters

AI automatically identifies the regional boundaries where the effects of growth cease to spread. In Maluku, the system found that the economic reach extends only as far as a 6–8-hour boat journey, beyond which self-sufficient or isolated regional clusters form. This insight means that predictive models no longer impose spatial relationships that do not actually exist.

Table. 1

Numerical Evidence (Based on R Output Results)

Accuracy Indicator	Standard Spatial Model	Integrated AI Model	Improvement
R² (Explanatory Power)	0.721	0.876	+21.5%
Error Value (RMSE)	1.147	0.654	-42.9%
Significance of Spatial Variables	Partial only	Fully significant	Higher reliability

The results show that this integration ensures that predictions are not merely based on statistical formulas, but rather simulate the actual functioning of an archipelagic system. AI refines how we measure spatial relationships and process complex data, whilst spatial econometrics guarantees that results remain economically interpretable and scientifically valid. Consequently, predictions regarding development disparities become significantly more accurate, well-targeted, and highly useful for designing differentiated policies for central versus outer regions.

5. The Spillover Effects of Development between Regions in Maluku

 

The effects of development spillovers in Maluku are generally selective, limited in scope, and heavily dependent on the quality of inter-regional connectivity. The impact of economic growth does not spread evenly in all directions, but is confined to areas that are well-connected and situated close to growth centres. Here are the full details:

1. Magnitude and General Characteristics of Spillover Effects

Based on the results of the spatial effect decomposition in the Spatial Durbin Model (SDM), the coefficient for the indirect effect was found to be 0.214 (significant at the 1% level).

• This means that every 1-unit increase in the development index or investment in a region will have an additional positive impact of 0.214 units on its connected neighbouring regions. 
• However, this value is significantly smaller than the direct effect (0.382). This demonstrates that the benefits of development are largely enjoyed by the region that receives the policy/investment directly, whilst the spillover effects beyond the region are slow and limited.
• The Rho value of 0.345 also confirms that if a neighbouring region grows by 1%, that region itself will grow by approximately 0.345%. There is a correlation, but it is neither strong nor comprehensive.

2.  Variables with Spillover Effects (Single Factor)

The research findings indicate that only the Connectivity/Accessibility variable has a positive and significant spillover effect on other regions. The other variables do not have a spillover effect:

• The results of the estimation indicate that Connectivity & Transport received a score of (0.214): this means that policies such as port improvements, the addition of shipping routes, or road improvements in one region provide economic benefits that extend to neighbouring islands or districts. Example: Port improvements in Ambon or Masohi reduce logistics costs in the surrounding area and facilitate the smoother flow of goods. This factor is the sole bridge for the dissemination of growth.
• Basic Infrastructure received a score of (0.072 – Not Significant): This indicates that policies regarding the development of electricity, clean water or public facilities only have an impact within the specific area that serves as the main focal point. The benefits do not extend to other districts because this infrastructure is local in nature and not integrated across islands.
• Investment and Human Resources: The impact is entirely local. Investment in a factory or improvements to schools in one area does not affect productivity in other areas. This indicates that the inter-regional economy in Maluku is not yet strongly integrated.

3. The Extent and Limits of Spill Effects

This is the most significant finding of the integration of AI and spatial analysis:

• The spillover effects of development in Maluku have a clear geographical limit, namely a maximum of 6 to 8 hours’ journey by sea.
• Areas within reach (< 8 hours): Ambon, Central Maluku, West Seram, and parts of Southeast Maluku. Here, economic growth is mutually reinforcing. Progress in Ambon is felt as far as these buffer zones. A ‘High-High’ cluster has formed.
• Areas Beyond Reach (> 8 hours): Southwest Maluku, the Aru Islands, and West Southeast Maluku. These areas feel absolutely no spillover effects from the centre. Economically, these areas are isolated and operate independently, meaning that their underdevelopment cannot be addressed by Ambon’s growth. A ‘Low-Low’ cluster has formed, trapped in a cycle of underdevelopment.

4. Direction of Distribution: Centripetal Pattern (Towards the Centre)

A unique pattern has been observed: the flow of benefits tends to move from the periphery to the centre, rather than the other way round.

• Peripheral regions supply raw materials to the centre, leading to rapid economic growth in the centre. However, the centre does not return these benefits to the periphery due to an unbalanced exchange rate and high transport costs. Consequently, the spillover effect tends to draw economic power towards the centre, widening the gap rather than reducing it.

So, It can therefore be concluded from these findings that spillover effects from development in Maluku do occur, but they are weak, limited in scope, and occur only via transport links. Economic growth in Ambon has not yet been able to act as a driving force capable of stimulating the entire Maluku region simultaneously. Development policy cannot rely solely on natural spillover effects from the centre; the government must undertake specific interventions in the form of developing inter-regional connectivity and establishing new growth centres in outlying areas so that spillover effects can be distributed evenly.

6. Comparison of Spatial Model Estimation Results.

This study utilises the following software: R | Package: splm with the following variables: Dependent variable: Human Development Index (HDI); Independent variables: CONNECT, INFRA, HUMANCAP, INVEST, DIST_AMBON; Weight matrix: Travel time by sea.

The results of the comparison of spatial model estimates are as follows :

Figure.4

Estimation Output Comparassion Spatial Model

Estimation results for selecting the SPATIAL DURBIN MODEL (SDM)

Table.2

Goodness-of-Fit Criteria

Indicator	SAR	SEM	SARAR	SDM (Selected)	Selection Criterion
R²	0.784	0.742	0.792	0.876	SDM is the highest, meaning it explains 87.6% of the data variation.
AIC	412.45	445.12	387.22	312.90	SDM is the lowest, indicating the smallest prediction error and best fit.
Log-Likelihood	-182.23	-198.56	-171.61	-142.45	SDM is the largest, confirming the model fits the observed data best.

The reason why this  model was chosen is that it demonstrated the highest explanatory power and the lowest prediction error compared to the other three models:

• SAR: Only incorporates dependence on the dependent variable (i.e., the development level of neighboring regions affects the region itself). Limitation: It cannot explain how explanatory factors from neighboring regions (e.g., neighboring infrastructure) also have an impact.
• SEM: Only captures dependence through the error term or unobserved components. Limitation: It treats spatial correlation merely as a statistical nuisance and is difficult to interpret economically.
• SARAR: Combines SAR and SEM. Limitation: In the estimation results above, the values of Rho and Lambda in SARAR are smaller and show reduced significance. This indicates the model is over-parameterized and becomes unstable when applied to the specific context of Maluku.
• SDM: The Most Comprehensive Model. It incorporates: Influence of the dependent variable from neighbors (Rho = 0.345, highly significant).Influence of independent/explanatory variables from neighbors (W_CONNECT = 0.214, significant).

This is critical for this research, as it proves that development in a region is influenced not only by its own facilities but also by the facilities and connectivity of neighboring regions. This analysis cannot be performed by SAR, SEM, or SARAR.

Where ini the main objective of this study is to measure Spatial Spillover Effects. 

• SAR and SEM only produce a single coefficient value and cannot distinguish between the extent of the impact received directly by the region itself and the extent of the impact that spreads indirectly from other regions.
• SDM is the only model that allows for the decomposition of effects into Direct Effects and Indirect Effects. This distinction is a key finding of this study (for example, demonstrating that the impact of connectivity spreads, where as the impact of infrastructure does not).

Thus, based on the characteristics of the archipelagic region, the results of the human capital analysis show that the spatial lag variable W_CONNECT is statistically significant, whereas the other spatial lag variables are not. This confirms that spatial interactions in Maluku are specific in nature and limited solely to the connectivity variable. The SDM model successfully accounts for this unique condition, whilst other models assume that interactions occur uniformly across all variables—an assumption that does not reflect the geographical reality of an archipelago.

This study’s findings therefore confirm that the Human Resources Model is the most appropriate model because, statistically, it offers the highest level of accuracy; theoretically, it is capable of capturing all spatial relationships (including endogenous and exogenous interactions); and practically, it is highly relevant to the research objective of measuring how development in one region affects other regions across the Maluku Islands.

V. CONCLUSIONS

Based on the results of panel data analysis, spatial econometric estimates and the integration of Artificial Intelligence (AI) methods, the following overall conclusions can be drawn: 

1. Development Pattern: A Strong Core-Periphery Structure The distribution of development in Maluku Province forms a clear and uneven spatial pattern, following a core-periphery structure. The central regions (Ambon City, Central Maluku, West Seram) are developing rapidly and have high Human Development Index (HDI) and economic indicators, whilst the outermost regions and small islands (Southwest Maluku, Aru Islands, West Southeast Maluku) lag far behind with high poverty rates and minimal facilities. This pattern is statistically confirmed by a Moran’s I value of 0.427, which is highly significant, indicating the presence of positive spatial autocorrelation where developed areas cluster with other developed areas, and underdeveloped areas are adjacent to other underdeveloped areas. This disparity is directly proportional to geographical distance: the further and more difficult to reach an area is from the growth centre, the lower its level of development.
2. Key Determining Factors: Connectivity and the Distance Penalty These disparities are influenced by a combination of geographical and economic factors, with Connectivity/Accessibility being the most dominant factor and having the greatest positive influence (coefficient = 0.382). Ease of maritime access, frequency of sailings, and logistics costs are the main determinants of a region’s progress. On the other hand, Distance to Ambon is the greatest obstacle with the highest negative coefficient (-0.412), indicating a very significant “distance penalty”. Other factors such as basic infrastructure, human resource quality, and investment have a positive influence, but their impact is smaller and more local in nature.
3. Limited and Selective Spillover Effects Economic interactions between regions in Maluku are weak, limited in scope, and occur solely via transport routes. Based on the results of the Spatial Durbin Model (SDM) estimation, the spillover effect of economic growth only occurs in the connectivity variable (indirect effect = 0.214) and has a real range of up to 6–8 hours’ sea journey. Beyond this distance, regions become economically isolated and feel no impact whatsoever from the progress of the centre. Infrastructure, investment and human capital factors have no spillover effect, meaning that the benefits of development in one area do not automatically improve conditions in neighbouring areas. Furthermore, a pattern of benefit flow was found that tends to move from the periphery to the centre, so that growth in Ambon actually widens the gap rather than reducing it.
4. Best Model: Spatial Durbin Model (SDM) Among the other spatial models (SAR, SEM, SARAR), the SDM proved to be the most appropriate and accurate model for use in this study. This is based on statistical criteria, whereby the SDM has the highest R² value (0.876), the lowest AIC value, and a unique ability to separate impacts into direct and indirect effects. This model is able to capture the characteristics of an archipelago well because it considers not only the influence of variables within the region itself, but also the influence of variables from neighbouring regions.
5. The Strategic Role of Integrating AI and Spatial Econometrics The combination of Artificial Intelligence (AI) and Spatial Econometrics has proven capable of significantly improving prediction accuracy by 21.5% (from R² 0.721 to 0.876) compared to conventional methods. AI plays a crucial role in refining the spatial weighting matrix based on actual travel time (rather than straight-line distance), detecting non-linear patterns and distance thresholds, and processing complex data such as satellite imagery. Meanwhile, spatial econometrics ensures that the results remain economically and scientifically interpretable. This integration demonstrates that spatial aspects are of paramount importance and must not be overlooked in the planning of development in archipelagic regions such as Maluku.

VI. POLICY IMPLICATIONS

Based on the above conclusions, development policies cannot be standardised. To reduce disparities, top priority must be given to equitable maritime connectivity and the reduction of logistics costs, particularly for regions located more than an 8-hour sea journey from Ambon. The government also needs to establish new growth centres in outlying regions so that economic spillover effects can develop independently and evenly, ensuring that the underdevelopment of peripheral regions no longer depends solely on the spillover of growth from the provincial capital.

VII. RESEARCH LIMITATIONS 

Although this study has integrated spatial econometric methods and Artificial Intelligence (AI) and produced significant findings, there are several limitations that constrain this research and which need to be taken into account when interpreting the results and in the development of future research. These are detailed below:

1. Data: The study uses aggregated data at the district level, and therefore does not capture inequalities at lower levels (sub-districts/villages); some detailed variables relating to the blue economy and logistics costs are not fully available.
2. Spatial Measurement: The weighting matrix is based on average sea travel time and does not take into account natural dynamics such as weather, waves, and shipping schedule constraints, which significantly affect actual access.
3. Methodology: Focuses on quantitative relationships; does not yet incorporate qualitative factors such as social, cultural, historical, and governance aspects, which also have an impact.
4. Scope: The research findings are highly specific to the characteristics of the Maluku region, so they cannot necessarily be directly generalised to other regions.

VIII. RECOMMENDATIONS

Based on the findings and limitations of this study, the following recommendations are made for future research:

1. Expansion of Data Scope and Scale

It is recommended to use sub-district or village-level data to reveal more detailed disparities within a regency, as well as to include specific marine economy variables, real logistics costs, and seasonal shipping dynamics that have not been accommodated in this study.

2. Development of Spatial Methods and Measurements

Develop a more dynamic spatial weighting matrix by considering weather, wave conditions, and the regularity of transport schedules. Furthermore, future research could incorporate qualitative approaches to understand the role of social, cultural, historical, and governance factors influencing development.

3. Analysis of Growth Dynamics and Structure

Using dynamic spatial econometric methods to examine shifts in growth patterns over time, as well as to examine the formation of new economic clusters and the impact of specific development policies in greater depth.

4. Application and Regional Comparison

Applying this framework integrating Artificial Intelligence and Spatial Econometrics to other island provinces in Indonesia to identify similarities and differences in characteristics, thereby enabling the development of a more general and comprehensive model for the development of island regions.

5. Focus on New Growth Centres

Conducting specialised research to determine strategic locations for the formation of new growth centres in outermost regions, in order to design the most effective intervention policies to break the cycle of underdevelopment in peripheral areas.

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