Service & Field Operations · Open-data demonstration
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.
Source, licence and limits
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. The public technique is reusable; the customer’s rules, costs, constraints, and outcomes must be validated.
What this runs on
- The data is public or fully synthetic.
- Impact figures are modelled from that dataset, with the model shown.
- The interface is a working demonstration of the method.
- Consequential decisions retain an accountable human owner.
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.