<![CDATA[This study aims to address the analytical limitations of traditional Geographic Information Systems (GIS) in urban governance by proposing an integrated, cloud-based Spatial Data Mining Decision Support System (SDM-DSS) framework. Employing a systematic literature review methodology, recent peer-reviewed studies (2021–2026) from major scientific databases were extracted, compared, and thematically synthesized to identify architectural vulnerabilities in current smart city models. The results indicate that while advanced SDM algorithms exhibit high theoretical accuracy for modeling urban phenomena, their practical deployment is frequently hindered by fragmented architectures, localized computational bottlenecks, and a lack of real-time Internet of Things (IoT) integration. To resolve these operational deficiencies, this study formulates a three-layered conceptual architecture comprising a Data Management Layer, a Spatial Analytics Engine, and a Presentation Dashboard. By decoupling heavy computational workloads into a scalable cloud environment, the proposed framework seamlessly translates complex algorithmic outputs into intuitive, actionable policy directives, as demonstrated through dynamic public facility allocation and predictive disaster mitigation scenarios. In conclusion, the integrated SDM-DSS architecture fundamentally transforms reactive urban planning into a proactive, predictive paradigm. Future research should prioritize the empirical prototyping of this framework using real-time municipal data streams and the incorporation of privacy-preserving machine learning techniques to ensure data sovereignty.]]>
Copyrights © 2025
