Portfolio trajectory
reported crash volume
Executive operating picture
Crash portfolio command center
Reported-event intelligence for territorial strategy, prevention, claims operations, and actuarial research.
CONFIRMED · CCRS-derived descriptive evidence
INFERRED · modeled signal, explicitly labeled
UNKNOWN · no policy exposure or paid-loss denominator
2026 year-end outlook
seasonally scaled · inferred
Spatial concentration
0.1° crash-density cells · all years
LOW ● DENSITY ● HIGH
Color = injury involvement
Color = injury involvement
Decision brief
highest-signal findings
Territorial intelligence
Risk geography
Credibility-adjusted injury involvement and crash concentration. Volume is not exposure-adjusted claim frequency.
California event surface
selected analysis window
Cell size = crash volume
Color = selected metric
Color = selected metric
Select a cell
Click to pin its metrics.
Click to pin its metrics.
Territorial severity × volume
top 100 cities · smoothed risk
Interpretation guardrail: risk index compares injury involvement among reported crashes. It does not measure per-policy or per-mile frequency.
Territory ledger
selected analysis window
Prevention + claims operations
Loss-driver observatory
When, how, and under what conditions reported crashes become injury-involved.
Weekly risk clock
volume intensity by day × hour · all years
Collision mechanism
share + injury involvement
Primary violation codes
unique crashes · canonical CVC section
Lighting severity
injury involvement rate
Occupant protection
injured-person records
At-fault composition by driver age
descriptive · not a pricing recommendation
Vehicle age signal
at-fault share among known vehicle years
Transparent predictive research
Scene severity prediction lab
Out-of-time validation for injury-or-fatal involvement. No protected attributes. Not an approved rating model.
Scenario lab
empirical Bayesian model
Holdout calibration
trained 2016–2023 · untouched 2024 test
Model card
intended use + limitations
Target: crash has ≥1 injured or killed person.
Features: city, hour, day, weather, lighting, collision mechanism.
Method: credibility-smoothed empirical rates combined with fixed, transparent weights.
Use: portfolio segmentation research and post-event triage.
Do not use: automated adverse action, individual pricing, causal attribution, or loss-cost projection.
Features: city, hour, day, weather, lighting, collision mechanism.
Method: credibility-smoothed empirical rates combined with fixed, transparent weights.
Use: portfolio segmentation research and post-event triage.
Do not use: automated adverse action, individual pricing, causal attribution, or loss-cost projection.
Commercial data gap
what converts signal into pricing value
Required next: geocoded policy exposure, vehicle-years, earned premium, claim counts, paid/incurred loss, limits/deductibles, and weather/road exposure. Join under privacy controls, validate chronologically, then test lift against the carrier baseline.
Ontology-backed aggregate exploration
Object explorer
Search public, browser-sized entities and traverse their analytical context. Individual crash, party, and person records remain local.
Objects
— aggregate entities
Inspector
properties · links · evidence
Reproducible aggregate workbench
Analysis studio
Compose a browser-side aggregate query over the published dimensions. The generated specification is shareable and never exposes raw CCRS records.
Result set
—
Reproduction specification
aggregate query contract
This is an equivalent aggregate specification, not access to a remote SQL engine. It can be reproduced locally against the verified CCRS snapshot.
Data products + health checks
Catalog and lineage
Trace the public artifact from verified CCRS resources through conformance, canonicalization, models, and aggregate publication.
Pipeline lineage
current local build → static public artifact
Published ontology
aggregate entity types and navigable relationships
Data-quality expectations
validated by the local build before publishing
Grounded aggregate tools
Ask Foundry
Ask supported CCRS questions. Answers are deterministic, cite their evidence class, and refuse insurance questions the dataset cannot answer.
Ready
Ask a bounded crash-intelligence question.
The tool works over the same aggregate artifact used by this site. It does not claim policy exposure, claim severity, reserves, premium, or causal effects.