Haight Ashbury Crime Rate Trends — San Francisco
Haight Ashbury — known simply as 'the Haight' — is the neighborhood synonymous with the 1960s counterculture and the 1967 Summer of Love. Today its colorful Victorians, vintage clothing shops, music stores like Amoeba Records, and the green slope of Buena Vista Park retain a bohemian, slightly faded character.
Two categories moved in Haight Ashbury this September, against a backdrop of broad, sustained decline across nearly every tracked crime type. The stronger signal is a single-month below-trend reading in motor vehicle theft. The second is structural: vandalism has shifted downward over multiple months, not just a quiet September but a change in the underlying pattern.
Motor vehicle theft sits at 48 incidents over the trailing 12 months, down from 73 in the prior 12-month period, a 34.2% year-over-year reduction. Vandalism shows a similar trajectory, 96 incidents against 141 the year before, down 31.9%. Robbery is down 51.7% over the same window (14 incidents vs. 29), and burglary has fallen 26.7%. Everything else in the neighborhood ran within range this month, with no spikes and no rare-event signals.
Notable signals 1
Motor Vehicle Theft
The past 12 months saw 48 incidents — about 61% below the 122 average from prior years.
All categories, last 24 months
Each panel: recent monthly count vs. trailing 12-month context. MoM is the most recent month vs. the one before; 12mo YoY compares the trailing year to the year before that.
What's been quietly true for a year
Spikes get attention. Sustained shifts shape policy. These are multi-quarter patterns where the past 12-month total differs meaningfully from the year before — they often precede the baseline resetting.
- Vandalism has reset to a lower baseline.
The trailing 12-month count is 96, down 32% from 141 the year before. If the trend holds another quarter, it will pull the multi-year baseline down.
What next month likely looks like
Forecasts trained through September 2026, with a likely range we're 95% confident the actual count will fall inside. Categories with too little recent volume — or violent categories at the neighborhood level — show no forecast and are surfaced through signals above instead. See the methodology page for the gating rules.
Aggravated Assault
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Arson
Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.
Burglary
Homicide
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Motor Vehicle Theft
Other Larceny
Robbery
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Sexual Assault
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Theft from Vehicle
Vandalism
How Haight Ashbury compares
Peer neighborhoods picked by closest 12-month motor vehicle theft volume — a pragmatic v1 of peer matching. Demographic / housing-stock peer matching isn't built yet (we deliberately don't ingest income or race data alongside crime). Volume similarity has the right intuition: “neighborhoods experiencing comparable motor vehicle theft levels.”
Inner Richmond
46 incidents over the past 12 months — 2 below Haight Ashbury's 48.
Open page →Outer Mission
44 incidents over the past 12 months — 4 below Haight Ashbury's 48.
Open page →Lone Mountain/USF
43 incidents over the past 12 months — 5 below Haight Ashbury's 48.
Open page →Recurring local terms (last 12 months)
Top terms in incident descriptions for Haight Ashbury, excluding generic crime taxonomy. Useful as texture — what kinds of specifics show up here that don't show up elsewhere.
Hour-of-day, day-of-week, and seasonality
Distribution of bucketed incidents in this neighborhood across the full analysis window. Useful for routine context — shopping-strip thefts vs. late-night assaults read very differently when you can see when each typically happens.
How we built this page
Data → Anomalies → Forecast → Page
Incident data is pulled from SFPD's open dataset on DataSF, mapped to 10 NIBRS-aligned categories, and aggregated to neighborhood × category × month.Anomalies are surfaced using strict thresholds (~p < 0.01). Forecasts are Prophet with low-count gating; violent categories at the neighborhood level skip the forecast and show rare-event / streak signals instead.
Spike rule: 12-mo total > baseline mean + 2.5σ AND ≥ 20 incidents AND 6-mo confirms. Drop rule: 12-mo total < baseline mean − 2.5σ AND baseline mean ≥ 20. Rare event: any incident in the last 90 days, no prior comparable in ≥ 5 years.