Early Warning Dashboard

Demo Data

Real-time monitoring of neighborhood risk thresholds, ML model fairness, and community-defined alert triggers. The system shifts from reactive crisis response to predictive, proactive intervention.

4

Active Alerts

2

Critical

5

Thresholds Exceeded

3

Approaching Threshold

Threshold Proximity Monitor

Tracking how close each neighborhood metric is to its community-defined risk threshold. Black markers indicate the threshold line.

Gambling Risk — Brooklyn Park
EXCEEDED↑ Rising
Score: 72/100Threshold: 70
Elder Isolation — Minneapolis (North)
EXCEEDED↑ Rising
Score: 75/100Threshold: 65
Elder Isolation — St. Paul (East Side)
EXCEEDED→ Stable
Score: 70/100Threshold: 65
Mental Health — St. Paul (East Side)
EXCEEDED↑ Rising
Score: 68/100Threshold: 65
Dropout Risk — Brooklyn Center
EXCEEDED↑ Rising
Score: 62/100Threshold: 60
Dropout Risk — Brooklyn Park
APPROACHING→ Stable
Score: 55/100Threshold: 60
Gambling Risk — St. Paul (East Side)
APPROACHING→ Stable
Score: 65/100Threshold: 70
Mental Health — Brooklyn Park
APPROACHING→ Stable
Score: 60/100Threshold: 65

Neighborhood Risk Heatmap

Multi-domain risk assessment across Lao community neighborhoods

NeighborhoodGamblingDropoutMental HealthElder IsolationOverall
Brooklyn Park72556062high
St. Paul (East Side)65456870high
Brooklyn Center58624855medium
Minneapolis (North)52505575high
Woodbury35223038low
Risk scale:70+ Critical55-69 High40-54 Medium<40 Low

Reactive → Predictive Transformation

Before: Reactive Response

  • Community crises identified after they occur
  • One-size-fits-all interventions across all "Asian" communities
  • Services deployed based on aggregate data that hides Lao disparities
  • Months-long delay between data collection and policy response

After: Predictive & Proactive

  • ML models detect rising risk weeks before crisis thresholds
  • Culturally-specific interventions matched to Lao community needs
  • Disaggregated data reveals true disparities hidden by aggregation
  • Real-time alerts enable immediate, targeted intervention deployment

Source: ML Risk Models, Fairness Audit System, Community Data Pipeline · Lao Community Data Portal