Smart GIS-Driven Zoning for Compact Urban Redevelopment

Thesis Abstract

This study addresses the spatial inefficiencies and policy fragmentation that hinder compact urban redevelopment in rapidly growing metropolitan regions, where traditional zoning often fails to align land use with transportation access, housing affordability, and ecological sustainability. The aim is to develop and validate a Smart GIS-Driven Zoning (SGDZ) framework that integrates high-resolution geospatial data, transport network analytics, and scenario-based planning to guide compact, transit-oriented development. Specific objectives include (i) evaluating current zoning inefficiencies using a GIS-aided diagnostic tool, (ii) designing a dynamic zoning model that incorporates land value, population density, and multimodal accessibility, (iii) integrating a participatory stakeholder layer to capture community preferences, and (iv) testing the framework through empirical application in the Central Metropolitan Region to generate policy-ready zoning recommendations. The methodological approach combines a mixed-methods research design anchored in urban planning and geography theory. The population consists of municipal planners, property developers, and residents within the Central Metropolitan Region (n ? 2.5 million inhabitants). A stratified random sample of 150 planners and 200 residents will be surveyed, complemented by 25 semi-structured interviews with senior planners and 12 focus groups with community representatives. GIS-based data will cover parcels, land use, building footprints, road networks, transit stops, housing affordability, and environmental constraints, sourced from municipal GIS portals, national census data, and open-access remote sensing products. Data collection instruments include a standardized planner survey, resident perception questionnaire, interview guides, and a GIS data-collection template. Validity and reliability will be ensured through pilot testing (n=20 planners), triangulation of survey results with interview insights, and test-retest reliability assessments for the resident questionnaire (Cronbach’s alpha ? 0.78). Analytical techniques include spatial econometric modeling, logistic but of sprawl propensity, and a GIS-driven multi-criteria decision analysis (MCDA) to rank zoning scenarios. Regression analysis (OLS and spatial lag models) will examine relationships among zoning attributes, accessibility, and housing affordability. A structural equation model will test theoretical linkages among perceived fairness, accessibility, and willingness to adopt proposed zoning changes. The MCDA component will integrate criteria such as floor-area ratio potential, parking requirements, transit-oriented incentives, green space, and flood risk, weighted through stakeholder preference elicitation via Delphi method. A scenario analysis will compare baseline zoning with three SGDZ configurations under different population growth projections and transit expansion plans. The expected analytical workflow will deploy Python-based scripting for data preprocessing, ArcGIS Pro for spatial analysis, and R for econometric and MCDA calculations. Ethical considerations include informed consent, anonymization of interview and survey data, and data-sharing agreements with the municipality. Key findings are anticipated to demonstrate that the SGDZ framework improves land-use efficiency by enabling higher-density, transit-accessible developments without compromising livability or green space, as evidenced by a predicted average 12–18% improvement in access to high-quality transit within 600 meters and a 9–14% reduction in travel time variability under peak conditions. The model is expected to identify zoning configurations that simultaneously reduce commute distances for lower-income households and preserve critical flood-prone areas through form-based codes and buffer provisions. The study should reveal that incorporating resident preferences strengthens policy legitimacy and accelerates adoption, with MCDA results showing stakeholder consensus on at least two of the three proposed land-use scenarios. The contribution to knowledge lies in operationalizing a transferable SGDZ framework that couples GIS-driven diagnostics with participatory planning and multivariate optimization to support compact, transit-oriented redevelopment. The research will extend theory by integrating spatial econometrics with MCDA under a participatory governance lens, offering a replicable methodology for other mid- to large-sized cities facing similar constraints. The main conclusion anticipates that technology-enabled zoning, when combined with community engagement and robust data governance, can produce actionable land-use transformations that reconcile density, accessibility, housing, and resilience. Recommendations include institutionalization of ongoing GIS data pipelines, establishment of a statutory mechanism for scenario-based zoning approvals, and capacity-building programs for planners and community leaders to utilize the SGDZ framework in routine planning practice.