Design and Implementation of a GIS-Based Urban Green Space Evaluation System

Thesis Abstract

Urban green spaces are critical for enhancing environmental quality, promoting public health, and fostering social cohesion within rapidly urbanizing environments. However, the lack of systematic evaluation tools impedes effective planning and management of these spaces, often resulting in underutilization and inadequate allocation of green amenities in the urban fabric. This study aims to design and implement a Geographic Information System (GIS)-based evaluation system that facilitates comprehensive assessment of urban green spaces, thereby informing sustainable urban planning policies. The specific objectives include (1) developing a spatial database integrating land use, green space distribution, and ecological data; (2) designing a GIS-based framework for evaluating green space accessibility, quality, and distribution; and (3) validating the system through case study analysis of a metropolitan city with a sample size of 150 green space sites within a population of approximately 1.2 million residents. The methodology adopts a mixed methods approach, integrating quantitative spatial analysis with qualitative assessments. The research design is a descriptive case study. The population comprises all urban green spaces in the selected city, with stratified random sampling used to select the 150 sites for detailed evaluation. Data collection instruments include high-resolution satellite imagery, existing land use maps, and field surveys employing GPS-enabled devices for data accuracy. Primary data encompass green space attributes such as size, vegetation cover, accessibility, and user satisfaction levels, collected through structured questionnaires distributed to residents and field observations. Secondary data include city planning records and ecological data obtained through government agencies. Validity and reliability of data are ensured via pilot testing, calibration of instruments, and inter-rater reliability checks. Data analysis employs spatial statistical techniques including network analysis for accessibility, density mapping for distribution patterns, and regression analysis to examine relationships between green space characteristics and socio-environmental factors. A GIS-based evaluation model is developed integrating these analyses, and validated through cross-validation with field survey data. Ethical considerations address informed consent, confidentiality, and data security. Expected findings indicate significant spatial disparities in green space accessibility, with underserved neighborhoods exhibiting lower quality and availability of green amenities. The evaluation system is anticipated to identify priority areas for green infrastructure development, reveal correlations between green space distribution and socio-economic variables, and provide policy-relevant insights. Additionally, the platform's capacity to visualize green space quality metrics and accessibility levels is expected to enhance stakeholder engagement and participatory planning processes. This research contributes to knowledge by bridging technological gaps in urban green space assessment, demonstrating the utility of GIS tools in sustainability planning, and providing an adaptable framework for cities with varying data capacities. It advances theoretical understanding of spatial equity and environmental justice concerning green infrastructure, supported by the application of the Environmental Justice Theory and the Urban Ecosystem Theory. The main conclusion underscores the importance of integrating GIS-based evaluation systems into urban planning to promote equitable and sustainable green space development. Recommendations include adopting the system for routine city assessments, expanding the database to incorporate ecological and social dimensions, and fostering stakeholder participation in green space planning. Future studies are suggested to explore the integration of remotely sensed data with socio-economic indicators and to expand the system's application in multi-city comparative analyses, thereby enhancing generalized models for urban green space management.