Presentation
Cyberinfrastructure-Driven Spatial Decision-Making Support: Addressing Participatory Collaboration and Spatiotemporal Heterogeneity
DescriptionSpatial decision support systems (SDSS) are pivotal in resolving complex geospatial challenges but face critical limitations in harmonizing conflicting objectives, capturing behavioral heterogeneity, and enabling efficient large-scale data processing. Besides, a central challenge is that current Geospatial cyberinfrastructure (GeoCI) is inefficient in supporting real-time data access. Additionally, CI struggles to deliver scalable computations needed for addressing complex, multidimensional problems. This research addressed these limitations by presenting a unified GeoCI framework powered by geospatial artificial intelligence (GeoAI). Our framework provides a dual-capability platform, enabling both participatory, stakeholder-driven planning for scenarios like offshore wind siting, and high-fidelity, agent-based simulations for dynamic phenomena such as epidemic transmission. These sophisticated applications are underpinned by an intelligent service tier where a machine learning model reduces data retrieval times by over 80%, making the entire system more scalable and responsive. This study provides a validated template for building next-generation spatial data sharing systems (SDSS) that can balance the needs of all stakeholders, simulate complex real-world dynamics, and process massive geospatial datasets in real time, thereby achieving the goals of greater efficiency, inclusiveness, and adaptability to the complexities of the real world.
Event Type
Doctoral Showcase
TimeThursday, 20 November 20253:45pm - 4:00pm CST
Location230
Archive
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