Research · 2 min read
Crime Data Is a Process That Runs Late
Recent occurrence-date totals are structurally incomplete, offense-specific, and lack any published revision history.
Crime data looks like a census. It is a process, and it runs late. In three years of Seattle property records the median offense took 32.7 hours to surface, motor-vehicle theft appeared in under a day while other larceny took almost three, and the source keeps no usable revision history — so we built our own.
Our analysis of 116,754 valid intervals, reports published july 1, 2023–june 30, 2026.
How quickly do Seattle property-offense records actually appear?
Among 116,754 valid Seattle property-offense intervals from 2023 through 2026, the median offense-to-report lag was 32.7 hours, only 44.2% appeared within a day, and 16% took more than a week. Motor-vehicle theft surfaced in 16.7 hours; all other larceny took 65.8.
The numbers behind the answer
Selected measures only. Denominators and interpretation stay attached so the headline cannot stand alone.
32.7 hours
Median offense-to-report lag
Across 116,754 valid nonnegative intervals in 2023–2026.
44.2%
Published within 24 hours
Slightly fewer than half of records appeared within a day.
16.7h vs 65.8h
Fastest vs slowest offense
Motor-vehicle theft versus all other larceny median lag.
A median of a day and a half, with a long tail
Half of property-offense records surface within 32.7 hours of the recorded start; 44.2% within a day. But 16% take more than a week, the 90th percentile reaches 286 hours, and the 95th reaches 590. A weekly dashboard that treats recent days as final is reading an unfinished page.
The gaps are not uniform. Motor-vehicle theft has a median lag of 16.7 hours, while all-other-larceny runs to 65.8 hours — different events get discovered and reported on very different clocks.
Evidence visual
Median offense-to-report lag by offense type
Hours from recorded offense start to publication, 2023–2026.
The clock is offense-specific
Treating the interval as a survival process, offense type predicts how quickly a record appears: relative to the baseline category, motor-vehicle theft shows a hazard ratio near 1.9 and shoplifting near 1.6 — they surface sooner. The concordance of the model is modest (0.57), which is itself the point: lag is noisy, but offense type clearly matters.
Use it as
A freshness policy: label recent periods provisional and never compare them with settled history.
The source keeps no revision history, so we keep one
SPD's crime dataset exposes no row-level revision history — the revisions endpoint returns 404. That means the only way to know how a published number changed is to snapshot the dataset yourself and diff it. We began that log in this program; it is the kind of record an open dataset does not publish about itself.
One caution throughout: this interval is not emergency-call, dispatch, arrival, or police response time. It is the gap between when an offense was recorded as starting and when its report was published.
- Show a data-as-of label on every map.
- Mark recent periods provisional.
- Never compare immature current periods to settled history.
- Keep offense-specific expectations, not one buffer.
- Snapshot the source if you need revision history.
Questions property teams ask
Does a 32.7-hour median mean police took that long to respond?
No. It measures how long after the recorded offense start the report was published, not any response time.
Is a seven-day buffer enough?
It depends on the offense and the decision. 16% of records exceed seven days, and some exceed three weeks.
Why are some offense times exactly midnight?
They likely encode missing precision rather than a real midnight event; they are counted and handled as a sensitivity.
Why build a revision log?
The public dataset publishes no row-level revision history, so change over time is only visible if you snapshot it yourself.
Our methods, limits, and sources
How we calculated this
This is original 911 Sentinel research — we gathered the records, ran every calculation below, and published the aggregate dataset.
We computed the interval between SPD offense_date and report_date_time for accepted property-offense records and modeled it with Kaplan–Meier and Cox proportional hazards.
- We pulled 116,846 property-offense records (2023–2026) and deduplicated by offense id.
- We flagged sentinel dates, negative intervals, and exact-midnight timestamps.
- We computed the lag distribution and by-offense medians.
- We fit Kaplan–Meier and a Cox model with offense type, and recorded a dataset revision-log baseline.
What this analysis cannot establish
- offense_date is an offense start and can be a broad or estimated window.
- Exact-midnight timestamps may be imprecise.
- Only finalized reports appear; recent weeks undercount.
- This is not emergency, dispatch, arrival, or police response time.
- Socrata exposes no row revision history, so our log is a snapshot diff, not an official record.
Sources
The raw records come from the sources below; the study design, analysis, charts, and conclusions are our own.
Related questions and practical guides
New coverage — intake open
Treat freshness as part of the decision
A property review should use data with a visible as-of date and a provisional window for recent periods, not last week treated as final.