Development of a GIS-Based Decision Support System for Urban Green Space Management

Thesis Abstract

Urban green spaces play a vital role in enhancing environmental quality, promoting biodiversity, and improving the well-being of urban residents; however, effective management of these spaces remains a significant challenge due to fragmented data, inadequate planning tools, and limited stakeholder engagement. Recognizing these issues, the study aims to develop a comprehensive Geographic Information System (GIS)-based Decision Support System (DSS) to streamline planning, management, and policy formulation for urban green spaces. The specific objectives include (a) mapping and analyzing the spatial distribution of existing green spaces; (b) identifying key factors influencing the spatial and ecological quality of these areas; (c) designing and implementing a GIS-based DSS that integrates spatial data with user-defined criteria; and (d) evaluating the system's effectiveness in supporting decision-making processes among urban planners and environmental managers. The research adopts a mixed-methods approach, combining quantitative spatial analysis with qualitative assessments. The population comprises urban green space data, demographic information, and stakeholder insights within a metropolitan area with a population of approximately two million residents. A stratified random sampling technique was employed to select 150 stakeholders, including city planners, environmental officers, and community representatives, for semi-structured interviews and validation workshops. Spatial data were collected through remote sensing, existing GIS databases, and field surveys, while additional socio-economic and ecological data were obtained from municipal records, environmental agencies, and community surveys. The validity and reliability of instruments were established through pilot testing, expert reviews, and triangulation. Data analysis involved descriptive spatial analysis using GIS tools to visualize green space distribution, while inferential techniques such as multiple regression analysis identified key determinants influencing green space quality and accessibility. The design of the DSS incorporated weighted overlay analysis, multi-criteria decision analysis (MCDA), and scenario modeling, guided by established theories such as the Land-Suitability Theory and the Urban Ecology Framework. The model's performance was validated through stakeholder feedback, system usability testing, and a comparative analysis of decision-making processes before and after DSS implementation. Expected findings suggest that spatial inequalities exist in green space distribution, with underserved neighborhoods identified as priority zones for intervention. The integrated GIS-based DSS is anticipated to enhance transparency, improve resource allocation, and support scenario planning for sustainable green space expansion and maintenance. The system's user interface is designed to be accessible and adaptable, facilitating stakeholder participation and fostering community-based planning. This study contributes to knowledge by demonstrating how GIS can be effectively harnessed to develop context-specific DSS models for urban green space management, thereby bridging gaps between spatial data, policy development, and community needs. It introduces a replicable framework that urban authorities and environmental planners can adopt to improve decision-making processes in diverse urban contexts. In conclusion, the research underscores the significance of spatially informed decision support in achieving sustainable urban green spaces. It recommends the integration of GIS-based DSS into routine urban planning practices, capacity building for stakeholders on GIS tools, and periodic updates of spatial data to reflect dynamic urban environments. Future research could explore integrating real-time data feeds, such as sensor networks, to further enhance decision-making accuracy and responsiveness in urban green space management.