4.3 KiB
Bolt dashboard specification
Product language
Use Utah Vehicle Health as the brand, but call the modeled quantity next-episode non-pass risk. The estimator is a cohort estimate, not a diagnosis, certification, or guarantee.
Four-page MVP
The sections below describe the full product target. The checked-in development preview intentionally implements a narrower safe subset: binary pass/non-pass aggregates, supported make/model scorecards, age bands, coverage quality, and pre-2025 model diagnostics. Four-class charts, uncertainty intervals, adjusted scorecards, and the prediction lookup remain disabled until their own reviewed aggregate assets exist.
Overview
- Eligible inspections, pass rate, non-pass rate, and covered-period KPIs
- Quarterly pass/fail/reject/abort trend with blanks shown separately
- Covered-county map; unavailable counties remain gray
- Non-pass risk versus vehicle age with intervals
- Clear notices for partial periods and limited feed coverage
Reliability explorer
- Search supported canonical make/model cohorts
- Compare up to three cohorts across vehicle-age bands
- Observed versus model-adjusted risk toggle
- Outcome-mix bar and uncertainty-aware ranked dot plot
- County, make/model, age band, fuel, program, and period filters
- Support size and interval displayed for every estimate
Next-test risk estimator
Inputs are coarsened, non-identifying attributes: county, supported make/model, vehicle-age band, fuel, prior episode outcome, time-since-prior band, season, and approved program category.
Output a calibrated non-pass probability, uncertainty interval, relevant baseline, and aggregate factor contributions. Never request VIN, plate, exact address, station, free text, or current-test diagnostics.
Data and methods
- Coverage timeline and source/year missingness heatmap
- Episode and target definitions
slc/slcosource-era explanation- Temporal split, PR-AUC, Brier score, and calibration plot
- Subgroup/source-era performance
- Leakage controls, DMV gaps, partial periods, and limitations
Public data contract
| Dataset | Safe grain |
|---|---|
data_manifest |
Data cutoff, deterministic release ID, model versions, definitions and exclusions |
overview_period_county |
Quarter/year × public county with rounded support and outcome rates |
cohort_scorecard |
Approved make/model × age band, optionally coarsened county/fuel |
age_risk_curve |
Approved cohort × age point/band with risk, interval and support |
prediction_lookup |
Only supported coarsened input combinations and calibrated outputs |
filter_catalog |
Publishable categories and valid combinations |
model_diagnostics |
Approved partition-level metrics; locked metrics require a separate release gate |
coverage_quality |
Source era × year volume, blank rate, linkage and availability |
Use purpose-built outputs rather than a single high-dimensional browser cube. Bolt receives only these sanitized, versioned assets—never countydata credentials or private analytical rows.
Publication controls
- Suppress cells below 100 eligible inspections.
- Also require at least 100 distinct private vehicle tokens in every published cell; tokens and distinct counts never enter the public asset.
- Suppress when an outcome or its complement has fewer than 10 records.
- Require at least 10 distinct vehicles contributing each binary class.
- Apply complementary suppression so totals cannot reconstruct hidden cells.
- Combine rare categories, coarsen model years, and round displayed counts.
- Recheck thresholds after every filter combination.
- Do not include suppressed rows in browser bundles, API responses, downloads, analytics logs, or hidden chart layers.
- Downloads contain only the sanitized summary currently displayed.
Visual direction
Use a restrained Utah/desert palette: teal pass, red fail, amber reject, purple abort, and gray missing. Use probability bars, calibrated dot plots, confidence bands, and cohort comparisons instead of gauges. Do not rely on color alone.
Stretch pages
- Failure-to-pass journeys with funnels, attempts-to-pass and survival curves
- Four-class outcome probabilities
- Source-scoped OBD early-warning analysis
- Automated aggregate refresh with a dedicated read-only role
- Model-drift monitoring