B
Urban Planning AI Public Opinion Analyzer
3.05
Derivation Chain
Step 1
Incheon 2045 Master Urban Plan adopts AI
→
Step 2
Expansion of AI-driven urban planning across local governments
→
Step 3
AI analysis tool for public opinion data in civic engagement
Problem
When local governments draft master urban plans, extracting key requirements from public hearings, online surveys, and civil complaint data requires 2–3 urban planning researchers working for 1–2 months. Over 70% of the input is unstructured text (open-ended comments, complaint posts), making manual classification unavoidable — and recurring issues include minority opinions being buried and biased summaries.
Solution
(1) Upload public opinion texts (surveys, complaints, hearing transcripts) for automatic issue-based clustering (2) Sentiment analysis with priority scoring (3) Heatmap visualization of resident demands by neighborhood district.
NUMR-V Scores
NUMR-V Scoring System
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
SaaS N=.15 U=.20 M=.15 R=.30 V=.20
Senior N=.25 U=.25 M=.05 R=.30 V=.15
Feasibility (65%)
Data Availability
20.6/25
Feasibility Breakdown
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
Market Validation (55/100)
Validation Breakdown
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |
Technical Requirements
AI/ML [medium]
Backend [medium]
Frontend [medium]