Clean label
Toyota CAMRY
42,377
hidden by splits
141,668
cleaned records
7
raw variants
29.9%
fragmented
Raw source labels
TOYT CAMRY
99,291
TOYOTA CAMRY
42,254
TOYO CAMRY
54
TOYTA CAMRY
25
TOYT - TOYOTA CAMRY
22
Vehicle records reviewed
5,412,441
2,595,125 crash records represented
Make and model changed
2,215,527
40.9% of reviewed vehicle party records
Raw labels collapsed
10,295
92,446 raw labels to 82,151 cleaned labels
Active cleaning rules
329
Rules feeding the warehouse before public vehicle analysis
For residents
A safer-street conversation should not depend on whether one model was typed three different ways.
For city staff
Bad labels can move a problem down the list before it ever reaches a grant memo, council packet, or safety plan.
For newsrooms
A ranking built from raw labels can turn a typo pattern into a headline. Cleaning is part of responsible reporting.
For police leaders
Fragmented records make it harder to see where training, forms, or review workflows are creating public confusion.
Why this matters
A real pattern can disappear into spelling noise.
Raw labels are not neutral. If a model is recorded several ways, a basic "top vehicles" chart undercounts it. The corrected view groups those labels into one analytical name, then keeps the raw variants visible.
Current example: Toyota CAMRY recovers 42,377 records that would otherwise sit under smaller aliases.
Clean label
Honda ACCORD
40,424
hidden by splits
135,569
cleaned records
4
raw variants
29.8%
fragmented
Raw source labels
HOND ACCORD
95,145
HONDA ACCORD
40,396
HOND - HONDA ACCORD
25
HONDA` ACCORD
3
Clean label
Honda CIVIC
39,338
hidden by splits
141,910
cleaned records
3
raw variants
27.7%
fragmented
Raw source labels
HOND CIVIC
102,572
HONDA CIVIC
39,319
HOND - HONDA CIVIC
19
Clean label
Toyota COROLLA
31,031
hidden by splits
110,892
cleaned records
9
raw variants
28.0%
fragmented
Raw source labels
TOYT COROLLA
79,861
TOYOTA COROLLA
30,914
TOYO COROLLA
58
TOYTA COROLLA
22
TOYT - TOYOTA COROLLA
21
Before and after
The leaderboard can move after labels are grouped.
This is where data quality becomes a decision risk. A raw-label chart can rank a vehicle lower simply because its records were scattered across aliases.
| After cleaning | Best raw version | Recovered | Decision shift |
|---|---|---|---|
#2 Toyota Camry 163,292 cleaned records | TOYT Camry raw rank #3 | 54,718 | +1 places |
#3 Honda Accord 157,906 cleaned records | HOND Accord raw rank #2 | 49,223 | +-1 places |
#1 Honda CIVIC 165,406 cleaned records | HOND Civic raw rank #1 | 48,226 | same rank |
#4 Toyota Corolla 128,085 cleaned records | TOYT Corolla raw rank #4 | 40,861 | same rank |
#15 Tesla Model 3 34,766 cleaned records | TESL 3 raw rank #98 | 27,328 | +83 places |
#6 Toyota Tacoma 78,367 cleaned records | TOYT Tacoma raw rank #7 | 23,949 | +1 places |
#5 Chevrolet Silverado 88,184 cleaned records | CHEV Silverado raw rank #6 | 23,261 | +1 places |
#7 Toyota Prius 72,015 cleaned records | TOYT Prius raw rank #8 | 21,441 | +1 places |
#21 Tesla Model Y 28,968 cleaned records | TESL Y raw rank #92 | 21,412 | +71 places |
#9 Nissan Altima 68,060 cleaned records | NISS Altima raw rank #11 | 20,973 | +2 places |
Where the answer is fragile
ZIPs most sensitive to raw labels.
These ZIP areas have the highest share of changed vehicle labels. They deserve extra caution before someone uses raw records to describe local crash patterns.
Unincorporated / 95387
859 of 1,093 vehicle records changed
78.6%
Freightliner CASCADIA
49
Honda CIVIC
35
Toyota CAMRY
35
Unincorporated / 93653
127 of 162 vehicle records changed
78.4%
Chevrolet SILVERADO
10
Toyota COROLLA
8
Honda ACCORD
4
South San Francisco / 94128
1,079 of 1,378 vehicle records changed
78.3%
Toyota COROLLA
56
Honda ACCORD
54
Toyota PRIUS
52
Unincorporated / 95527
104 of 133 vehicle records changed
78.2%
Toyota CAMRY
8
Chevrolet SILVERADO
5
Toyota HIGHLANDER
5
Unincorporated / 95962
111 of 143 vehicle records changed
77.6%
Honda ACCORD
5
Toyota CAMRY
5
Toyota COROLLA
5
Current highest-impact ZIP: Unincorporated / 95387.
Reporting quality
Messy labels become public blind spots.
This is not a blame scoreboard. It is an audit trail for the places where public crash records need the most translation before anyone should compare them.
A fragmented record can make a vehicle trend, crash cause, or neighborhood risk look smaller than it is. That affects public meetings, newsroom charts, enforcement reviews, grant narratives, and the everyday driver trying to understand local danger.
Jurisdiction currently uses the CCRS city label as a public reporting proxy until a dedicated police-department dimension is exposed.
Places where vehicle labels changed most
Minimum 250 vehicle party records. Higher share means raw labels are more likely to distort local comparisons.
Seal Beach
3,762 changed vehicle labels, 894 raw values collapsed to 818
75.1%
vehicle labels changed
Lafayette
1,913 changed vehicle labels, 645 raw values collapsed to 582
71.5%
vehicle labels changed
Unincorporated
1,036,831 changed vehicle labels, 46,787 raw values collapsed to 42,519
71.1%
vehicle labels changed
Orinda
1,427 changed vehicle labels, 539 raw values collapsed to 486
70.7%
vehicle labels changed
South San Francisco
3,925 changed vehicle labels, 1,231 raw values collapsed to 1,114
69.7%
vehicle labels changed
Menlo Park
3,103 changed vehicle labels, 1,044 raw values collapsed to 971
69.4%
vehicle labels changed
Newport Beach
5,498 changed vehicle labels, 1,623 raw values collapsed to 1,563
68.8%
vehicle labels changed
Delano
1,953 changed vehicle labels, 582 raw values collapsed to 534
68.7%
vehicle labels changed
Year-by-year cleanup sensitivity
If this line stays high, the issue is not a one-year typo batch.
Lafayette
2020 to 2026
20
21
22
23
24
25
26
Orinda
2020 to 2026
20
21
22
23
24
25
26
Seal Beach
2020 to 2026
20
21
22
23
24
25
26
South San Francisco
2020 to 2026
20
21
22
23
24
25
26
Unincorporated
2020 to 2026
20
21
22
23
24
25
26
Crash-cause cleanup not shown yet
Primary Collision Factor means the reported crash cause or violation. Those mappings are still being reconciled, so this page avoids showing a misleading 0.0% score.
Cleaning progress
The current rulebook.
These mappings are applied before the public pages calculate vehicle rankings. Raw source values are preserved, so every cleaned label can still be audited.
Vehicle Make
77 clean labels
210
rules
Vehicle Make V2
38 clean labels
68
rules
Vehicle Model
9 clean labels
51
rules
Audit examples
Raw values mapped into clean labels.
Model 3
14 raw values
Vehicle Model
MAZD || MODEL 3TELSA || MODEL3TESL || MODEL 3TESLA MOTORS || MODEL 3TESLA || 3TESLA || Model3
Model Y
13 raw values
Vehicle Model
TELSA || MODEL YTESL || MODEL-YTESL || MODELY YTESLA / || MODEL Y /TESLA MOTORS || MODEL YTESLA || Y
Tesla
12 raw values
Vehicle Make
TELSATESLTESL - TESLATESLA /TESLA MOTORSTESLA-TSMR
Model S
9 raw values
Vehicle Model
TELSA || MODEL STESL || model sTESLA MOTORS || Model STESLA || MODel sTESLA || s modelTSMR || MODEL S
Model X
8 raw values
Vehicle Model
BMW || X-ModelsTELSA || MODEL XTESL || Model XTESLA MOTORS || MODEL XTESLA || model xTSMR || modelx
Blue Bird
7 raw values
Vehicle Make
BLBRDBLUBLUBBLUEBLUEBBLUEBIRD
scope
Summary totals include all vehicle labels. Example rankings filter out Unknown model labels so the public story focuses on resolved make-model cleanup.
privacy
The public report uses aggregate counts only and does not expose individual crash records.
comparison
Compares source vehicle labels to the current cleaned warehouse labels.
jurisdiction
Reporting-jurisdiction examples use CCRS city labels as the public agency proxy because no separate police-department dimension is currently exposed in the mart.
zip threshold
ZIP impact examples require at least 100 party vehicle records in the selected window.
Source: California Highway Patrol CCRS public records, processed independently in BigQuery. This page measures label standardization impact. It does not claim that one vehicle is more dangerous than another, and crash records are not adjusted for registrations, miles driven, or population exposure.