<![CDATA[This study examines the influence of socio-economic indicators on the distribution of small and micro industries (SMI) in Indonesia using spatial modelling. The research employs a Bayesian approach with the Integrated Nested Laplace Approximation (INLA) method to analyse spatial dependencies and heterogeneity across regions. Data were obtained from the Central Bureau of Statistics (BPS), focusing on variables such as Gross Domestic Product (GDP), unemployment rate, Human Development Index (HDI), and poverty rate. The results indicate that HDI has a significant positive impact on the distribution of SMI, suggesting that improvements in education, health, and living standards foster the growth of small and micro enterprises. Spatial analysis reveals regional variations in SMI potential, with Yogyakarta, Gorontalo, and Maluku showing the highest relative potential. Conversely, regions like Papua and West Papua face significant challenges due to infrastructure and socio-economic limitations. The findings provide valuable insights for policymakers to design targeted interventions to support SMI development in different regions.]]>
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