A Study and Investigation on the Implementation of Service-Oriented ‎Architecture for Spatial Data Infrastructure in Urban Management

Bazalipour‎, H.; Fallahi‎, Gh.

doi:10.22061/jrsgr.2025.12263.1106

Articles in Press

Document Type : Original Research Paper

Authors

Department of Geoinformation and Geomatics Engineering, Facutly of Civil, Water, and Environment Engineering, Shahid ‎Beheshti ‏University‎, ‎Tehran‎, ‎Iran

https://doi.org/10.22061/jrsgr.2025.12263.1106

Abstract

Background and Objectives: Spatial data, as one of the fundamental components of urban information systems, plays a crucial role in analysis, planning, decision-making, and policy evaluation processes. In recent decades, the rapid growth of urbanization, the emergence of smart cities, and the expansion of sensor networks and the Internet of Things (IoT) have led to an exponential increase in the volume and diversity of spatial data. These data are collected from multiple sources such as Geographic Information Systems (GIS), satellite imagery, remote sensing, intelligent transportation systems, and citizen-generated data. Consequently, the effective management of these datasets has become one of the major challenges in contemporary urban management. The absence of standardized and integrated infrastructures often leads to inconsistency among executive organizations, data redundancy, and reduced accuracy in data-driven decision-making.
Methods: To address these challenges, this study proposes a novel framework based on Service-Oriented Architecture (SOA) for establishing an integrated spatial data infrastructure in urban management. SOA, with its core principles of service independence, reusability, composability, and interoperability, provides a flexible and scalable foundation for developing distributed spatial systems. Additionally, the research utilizes international OGC standards, including Web Map Service (WMS), Web Feature Service (WFS), and Web Processing Service (WPS), to establish a unified technical framework for the exchange, processing, and visualization of spatial data across heterogeneous environments. The use of these standards enables various urban subsystems to interact dynamically and seamlessly without dependency on specific technologies or programming languages.
Findings: The findings indicate that the proposed framework consists of three main layers: the spatial data service layer for storing, managing, and accessing distributed datasets; the processing service layer for analyzing, integrating, and extracting spatial patterns at different decision-making levels; and the interaction management layer for service orchestration, data flow control, and quality assurance in heterogeneous environments. This three-layered structure was designed to enhance scalability, minimize inter-component dependencies, and improve interoperability among diverse urban systems. A case study was implemented in a real urban management environment to empirically evaluate the performance, stability, and reliability of the proposed framework in terms of response time, processing volume, and coordination among services.
Conclusion: The results demonstrated that implementing the integrated SOA–OGC framework led to an average 30% reduction in response time, improved scalability in handling large spatial datasets, and simplified service maintenance and expansion. Moreover, interoperability among urban systems in various domains—such as transportation, environment, and public services—was significantly enhanced. However, challenges including data security assurance, user access control, system stability under high network load, and Quality of Service (QoS) remain critical issues requiring further investigation. In summary, the study concludes that adopting a service-oriented approach in conjunction with OGC standards provides an effective foundation for developing spatial data infrastructures in urban management. This framework not only strengthens data-driven decision-making but also paves the way toward smart city realization, sustainable resource management, and improved quality of urban life. Future research is recommended to integrate Cloud GIS, Big Spatial Data processing, and Artificial Intelligence (AI)-based spatial analytics within this architecture to further enhance the performance, scalability, and security of urban spatial systems.

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