Navigation: [Home]] ](/GuitarAlchemist/ix/wiki/[ML-Pipelines) | [Architecture]]

Use Case: PSAP / 911 Dispatch

ML pipelines for Public Safety Answering Points — caller location, unit dispatch, incident prediction, mass casualty detection, and response time compliance.

Pipelines

1. Caller Location Fusion (Kalman Filter)

Fuse noisy GPS + cell tower readings. High measurement noise for indoor callers. Standard: FCC E911 ≤ 50m for 80% of calls.

2. Nearest Unit Dispatch (A*)

Find unit with shortest time-to-scene using road speed, traffic, and emergency preemption. Standard: NFPA 1710 — 4 minutes for 90%.

3. Incident Hotspot Prediction (DBSCAN + Gradient Boosting)

Cluster historical incidents, predict when hotspots activate by time/weather features.

4. Mass Casualty Event Detection (DBSCAN space-time)

Detect spatial-temporal clusters of 911 calls within a sliding window. Trigger multi-unit dispatch.

5. Response Time Compliance (Time Series)

Monitor with rolling_mean, ewma, expanding_mean. Alert when 7-day average exceeds NFPA target.

6. Predict Response Time Failures (Decision Tree + CV)

Train to predict which calls miss the 4-min target. Cross-validate with cross_val_score, evaluate with ConfusionMatrix.

Algorithms Used

Algorithm	ix Crate	Role	Standard
Kalman Filter	ix-signal	Caller location	FCC E911
A* Search	ix-search	Unit dispatch	NFPA 1710
DBSCAN	ix-unsupervised	Hotspots, MCI	NIMS/ICS
Gradient Boosting	ix-ensemble	Activation prediction	—
Rolling Mean/EWMA	ix-signal	Compliance monitoring	NFPA 1221
Decision Tree + CV	ix-supervised	Failure prediction	NFPA 1710

Related

• Full GIS + PSAP tutorial
• Time series tutorial