Improving Regional Budgeting: An Ontology-Based SDSS Framework for Fair Resource Allocation

Document Type : Original Article

Authors

1 PhD Student in Remote Sensing and GIS, RS and GIS group, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 GIS and SDI Specialist in NCC of Iran

10.22059/eoge.2025.388553.1165

Abstract

To ensure fair budget distribution across the country, equitable resource allocation is crucial. This involves using budget planning informed by land use data to tailor fund allocation to the unique characteristics of each region. Spatial data has recently become vital in decision-making for various projects, including land use planning and disaster management, enhancing implementation across management domains. Resource allocation is inherently complex due to its dependence on diverse factors, often viewed as semi-structured problems. Addressing these decision-making challenges requires integrating the spatial dimension into the processes. Consequently, technological solutions like Spatial Decision Support Systems (SDSS) have been developed to improve decision-making in budget and resource allocation, which is a sensitive issue across different levels of society. In this study, decision-making techniques based on qualitative analysis have been applied in the field of resource allocation and budgeting. Therefore, using spatial decision support systems (SDSS), the climatic, industrial, natural, political, and other characteristics of various regions in the country have been categorized based on specific qualitative parameters. The resource distribution functions for different regions have been established in the system’s inference engine, taking into account various characteristics and using decision-making logic. The results indicate that the use of this system leads to appropriate resource allocation while considering regional features. In particular, it provides recommendations for budget allocation in underdeveloped or border areas to enhance security and, in general, addresses the deficiencies of traditional resource allocation methods that do not take these factors into account.

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