San Francisco ยท Emergency Services ยท Dataset Report

Fire Department
Response Analysis

Call Date: January 11, 2002 109 Records 10 Battalions 6 Call Types
โ€”
Total Calls Logged
โ€”
Avg Response Delay (min)
โ€”
ALS-Dispatched Calls
โ€”
Unique Call Types
01 โ€” Full Dataset

All 109 Emergency Calls

Jan 11 2002
TypeCall DateWatch DateAddress BattalionOrig PriorityPriorityFinal ALSDelay (min)
02 โ€” Visual Analysis
CHART 01

Call Type Distribution

Breakdown of all emergency call categories

Medical Incidents dominate overwhelmingly, accounting for the majority of all logged calls. This confirms the modern reality: fire departments today function primarily as emergency medical responders, with structural fires representing a fraction of total workload. Structure Fires and Alarms together form the next largest segment, while Vehicle Fires, Outside Fires, and Smoke Investigations represent tail-end categories. This distribution has direct staffing implications โ€” ALS (Advanced Life Support) unit allocation should reflect this medical-centric demand curve.
โ€”Medical calls โ€”Structure fires
CHART 02

Avg Delay by Priority

Mean response delay per original priority level

Counterintuitively, Priority 1 calls โ€” the most urgent โ€” show the longest average delays. This is partly driven by extreme outliers: one Priority 3 Medical Incident logged a 77.33 min delay, and a Priority 1 call reached 18.07 min. While Priority 2 sits in the middle, the pattern suggests that dispatch triage may not reliably correlate with actual field response speed. Outliers skew means significantly โ€” resource bottlenecks, unit availability, and geographic clustering all likely contribute to this paradox.
โ€”P1 avg min โ€”P3 avg min
CHART 03

ALS vs Non-ALS Delays

Average response delay split by ALS dispatch status

ALS-dispatched calls consistently show higher average delays than non-ALS calls โ€” not because ALS units are slower, but because they're deployed to more complex, time-sensitive incidents that inherently involve more coordination overhead. ALS resources are finite: when multiple critical incidents coincide, unit availability becomes the binding constraint. This finding argues for strategic pre-positioning of ALS units in high-density call zones (B02, B03, B04) and closer monitoring of simultaneous ALS dispatch events.
โ€”ALS avg delay โ€”Non-ALS avg delay
CHART 04

Calls by Battalion

Volume of calls handled per battalion unit

Call volume is unevenly distributed across battalions, with B03, B04, and B10 handling the highest loads on this date. B03 covers the dense downtown/SoMa corridor โ€” its elevated count reflects high population density and concentrated medical demand. B08 stands out due to the 1500 Block of 25th Ave Structure Fire, which alone generated 12 dispatched units. This multi-unit clustering effect can temporarily overwhelm a single battalion's resources, creating response gaps for concurrent calls in adjacent zones.
โ€”Busiest battalion