Yearly Traffic Safety Analysis

762 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Clinton County recorded 762 total crashes, an 8.4% decrease from the 832 crashes reported in 2018. Despite the overall decline in collisions and a 15.1% drop in injuries, the number of fatalities increased from 6 to 8 year-over-year. A significant contributing factor was a 45.2% decrease in crashes involving DUIs, which fell from 31 in 2018 to 17 in 2019.

762

▼ -8.4%was 832

Total Crash Events

8

▲ 33.3%was 6

Persons Killed

253

▼ -15.1%was 298

Persons Injured

8

▲ 33.3%was 6

Fatal Crash Events

Note: "Persons Killed" (8) counts individual fatalities across all crash events. "Fatal" in the severity table below (8) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Clinton County showed a downward trend between 2018 and 2019, with total collisions decreasing by 8.4% from 832 to 762. The number of people injured also declined by 15.1%, from 298 to 253. However, this trend did not extend to the most severe outcomes, as the number of fatalities rose from 6 in 2018 to 8 in 2019.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

7

Motorists Killed

Prior: 6↑16.7%

5

Pedestrians Injured

Prior: 5→0.0%

10

Cyclists Injured

Prior: 4↑150.0%

238

Motorists Injured

Prior: 289↓-17.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained relatively consistent year-over-year. The peak hour for collisions was 3 PM in both 2019 and 2018, with nearly identical crash counts of 75 and 76, respectively. The peak day for crashes shifted slightly from Thursday (132 crashes) in 2018 to Friday (135 crashes) in 2019. The busiest month shifted from October in 2018 (98 crashes) to January in 2019 (88 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes decreased, the fatal crash rate rose from 0.72% in 2018 to 1.05% in 2019, with the number of fatal crashes increasing from 6 to 8. The proportion of serious injury crashes declined from 2.5% of all crashes (21 incidents) to 1.4% (11 incidents). Conversely, crashes resulting in possible injuries increased as a share of the total, rising from 14.7% in 2018 to 19.4% in 2019.

Outcome by Severity (Crash Events)

Fatal8fatal crashes1%
↑33.3%prior 6
Serious Injury11serious injury crashes1.4%
↓-47.6%prior 21
Minor Injury54minor injury crashes7.1%
↓-30.8%prior 78
Possible Injury148possible injury crashes19.4%
↑21.3%prior 122
No Injury541no injury crashes71%
↓-10.6%prior 605

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the top contributing factor in both periods, though the count of such incidents decreased by 26.2% from 145 in 2018 to 107 in 2019. Crashes attributed to 'Lost Control' increased in count from 55 to 64, becoming the second-leading factor. 'Driving too fast for conditions' saw a notable 62.5% increase in count, rising from 24 incidents in 2018 to 39 in 2019.

Officer-Reported Primary Contributing Cause

Animal107 (14%)↓-26.2%prior 145
Lost Control64 (8.4%)↑16.4%prior 55
FTYROW: From stop sign59 (7.7%)↑15.7%prior 51
Followed too close52 (6.8%)↑10.6%prior 47
Other (explain in narrative): Other52 (6.8%)↓-17.5%prior 63
Driving too fast for conditions39 (5.1%)↑62.5%prior 24
Ran Stop Sign34 (4.5%)↓-8.1%prior 37
Ran off road - left33 (4.3%)↓-8.3%prior 36
Ran off road - straight33 (4.3%)↓-2.9%prior 34
FTYROW: Making left turn23 (3%)↓-32.4%prior 34

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring on dry roads decreased from 61.5% in 2018 to 58.9% in 2019. Correspondingly, crashes on adverse road surfaces like snow, ice, or slush increased, accounting for 16.5% of all incidents in 2019 compared to 10.0% in the prior year. The share of crashes happening in clear weather fell from 56.3% to 52.1%, while the distribution of crashes by lighting conditions remained largely stable.

Weather

Clear397 (58.9%)
↓-15.2%prior 468
Cloudy152 (22.6%)
↑10.9%prior 137
Snow46 (6.8%)
↑70.4%prior 27
Rain42 (6.2%)
↑13.5%prior 37
Freezing rain/drizzle14 (2.1%)
↓-6.7%prior 15
Blowing Snow11 (1.6%)
Fog, smoke, smog5 (0.7%)
↓-50.0%prior 10
Severe Winds4 (0.6%)
Other (explain in narrative)3 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight470 (69.2%)
↑0.2%prior 469
Dark - roadway not lighted96 (14.1%)
↓-4.0%prior 100
Dark - roadway lighted80 (11.8%)
↓-11.1%prior 90
Dusk16 (2.4%)
↓-20.0%prior 20
Dawn15 (2.2%)
↓-16.7%prior 18
Dark - unknown roadway lighting2 (0.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry449 (66.3%)
↓-12.3%prior 512
Wet91 (13.4%)
↑9.6%prior 83
Snow63 (9.3%)
↑57.5%prior 40
Ice/frost50 (7.4%)
↑78.6%prior 28
Slush13 (1.9%)
↓-13.3%prior 15
Gravel10 (1.5%)
↓-47.4%prior 19
Mud, dirt1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (1,249 vehicles)

1
FORD189 (15.1%)
↓-12.1%prior 215
2
CHEV172 (13.8%)
↓-16.1%prior 205
3
CHEVROLET113 (9%)
↓-1.7%prior 115
4
GMC61 (4.9%)
↑5.2%prior 58
5
DODG46 (3.7%)
↑2.2%prior 45
6
TOYT45 (3.6%)
↓-13.5%prior 52
7
NR44 (3.5%)
↓-26.7%prior 60
8
TOYOTA37 (3%)
↓-15.9%prior 44
9
JEEP37 (3%)
↑32.1%prior 28
10
CHRY36 (2.9%)
↑12.5%prior 32

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

254 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (1,100 persons with recorded sex)

Male640 (58.2%)
↑23.6%prior 518
Female460 (41.8%)
↑13.0%prior 407

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Person-level records linked to crash events

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Iowa Crash Data, accessed programmatically via the ArcGIS Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.

Data Retrieval

  • Access method: ArcGIS Open Data API (SoQL queries)
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 762
  • Total persons involved: 1,710
  • Total vehicles involved: 1,249

Analytical Methodology

  • Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
  • Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
  • Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
  • Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
  • Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
  • Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
  • AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.

Limitations & Disclaimers

  • Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
  • Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
  • Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
  • AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
  • Percentages are calculated from reported data and are subject to rounding.

Non-Affiliation Disclosure

This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.

Data License

The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.

Corrections & Feedback

If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.

Suggested Citation

ThatCarHitMe.com (Injuria.ai). "iowa, IA Crash Intelligence Report: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-annual-report

About the Publisher

ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.

Questions about this report's data or methodology: data@injuria.ai

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