Service & Field Operations · Interactive demo
Which requests should limited crews handle first?
A geospatial decision lab that turns unresolved service requests into risk-ranked hotspots and tests how crew capacity changes priority coverage.
Data source and licence
Sources311 service requestsLocation · category · age · borough
NigatForecast and decision modelBaseline · uncertainty · monitoring
OutputCrew allocationSend crews where risk concentrates
Unresolved requestHotspot reachedBeyond capacity
| Cluster | Leading request type | Borough | Requests | Risk |
|---|
Business problem
Dispatch sees one undifferentiated backlog. Age, urgency, density, travel and crew availability are weighed separately or not at all, so the order of work changes with whoever is on shift and cannot be explained afterwards.
Techniques used
- Density-based geospatial clustering
- Explainable multi-criteria risk scoring
- Crew-capacity scenario allocation
- Source and limitation transparency
What the workflow can improve
- Reveal geographic backlog concentration
- Prioritize high-risk requests
- Test capacity before changing dispatch
- Create a path to skill-, SLA-, and travel-aware optimization
From demonstration to production
Replace the proxy with the customer’s operating evidence.
The production path begins with a bounded workflow scan, source and permission mapping, baseline measurement, user and administrator roles, and an explicit decision owner. In an engagement, your rules, costs and constraints replace the public ones, and the result is measured against your baseline.
More demonstrations
See the same approach in another sector.
Start with the operating problem
Show us where the work slows down.
You do not need a polished technical brief. Describe the decision, handoff, report, document workflow, or AI initiative that is harder than it should be.