Data to Action: AI and Social Media

Course Details

This presentation begins with social media analysis, which highlights how methods driven by Artificial Intelligence (AI) uncover patterns of sense of place, accessibility, and community activity in Huntsville, Alabama. This people-centered perspective shows planners how unconventional data sources can capture local narratives in ways traditional datasets often miss.

The focus shifts to the streetscape and built environment. Using Google Street View imagery, environmental data, and AI-based feature extraction, presenters demonstrate practical methods for measuring walkability at social locations across the city. They emphasize how planners can evaluate accessibility at a fine-grained scale and identify opportunities for strengthening pedestrian-friendly design.

The presentation concludes with automated address attribution, which grounds the discussion in foundational spatial data. By seeing how open-source, reverse-geocoding tools can expand and clean parcel-level address records, you'll be equipped with a cost-effective and transferable method to strengthen your data infrastructure. Together, these case studies illustrate how innovative, practical tools can turn limited or fragmented data into actionable insights for planning practice.
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Learning Outcomes

• Understand how AI, social media, and geospatial tools can reveal patterns of accessibility, walkability, and community dynamics.
• Explore practical methods to enhance limited data sources in small and mid-sized cities.
• Apply lessons from Huntsville, Alabama, case studies to develop actionable strategies for local planning practice.