Community engagement in urban planning faces two persistent challenges: hearing from all community members (not just those who attend evening meetings) and systematically analyzing the feedback received. AI helps address both.

Analyzing Community Feedback at Scale

Public engagement generates enormous volumes of qualitative data — meeting transcripts, survey responses, written comments, and social media reactions. AI can process this systematically:

"Analyze these community meeting transcripts and survey responses [paste or describe data]. Identify: 1. The top 10 themes by frequency (what do people talk about most?) 2. Themes that differ significantly between demographics (do homeowners and renters have different priorities?) 3. Concerns that were raised but not addressed in the current proposal 4. Sentiment analysis: which aspects of the proposal generate positive responses vs. opposition? 5. Voices that appear underrepresented: are certain neighborhoods, age groups, or demographics absent from the feedback?"