Yearly Traffic Safety Analysis

296 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Clayton County recorded 296 total crashes, a 9.5% decrease from the 327 crashes documented in 2018. While overall crashes declined, the number of people injured rose by 22.4%, from 67 to 82. The most significant year-over-year change was a 60% reduction in fatalities, which fell from 5 in 2018 to 2 in 2019.

296

▼ -9.5%was 327

Total Crash Events

2

▼ -60.0%was 5

Persons Killed

82

▲ 22.4%was 67

Persons Injured

2

▼ -33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 Clayton County trended downward, decreasing by 9.5% from 327 in 2018 to 296 in 2019. This trend included a 33.3% reduction in the number of fatal crashes, from 3 to 2. In contrast to the decline in total and fatal crashes, the number of persons injured increased by 22.4% year-over-year.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 5↓-60.0%

82

Motorists Injured

Prior: 67↑22.4%

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 shifted between the two periods. In 2018, the peak day for crashes was Friday with 61 incidents, which shifted to Monday with 51 incidents in 2019. The peak hour also changed, moving from the 5 p.m. hour (28 crashes) in 2018 to the 7 p.m. hour (28 crashes) in 2019.

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

Crash severity outcomes were mixed year-over-year. The fatal crash rate improved, with fatal incidents comprising 0.7% of all crashes in 2019, down from 0.9% in 2018. However, the proportion of crashes resulting in an injury of any severity increased from 16.5% of all crashes in 2018 to 19.3% in 2019, with the count of serious injury crashes rising from 6 to 7.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.7%
↓-33.3%prior 3
Serious Injury7serious injury crashes2.4%
↑16.7%prior 6
Minor Injury26minor injury crashes8.8%
↑8.3%prior 24
Possible Injury24possible injury crashes8.1%
→0.0%prior 24
No Injury237no injury crashes80.1%
↓-12.2%prior 270

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 leading contributing factor in both periods, though the count of these incidents decreased by 20.1% from 154 in 2018 to 123 in 2019. "Driving too fast for conditions" became the second-most cited factor in 2019, with its count increasing by 57.9% from 19 to 30 crashes. Crashes attributed to "Ran off road - left" saw the largest percentage growth, more than doubling from 13 incidents in 2018 to 28 in 2019.

Officer-Reported Primary Contributing Cause

Animal123 (41.6%)↓-20.1%prior 154
Driving too fast for conditions30 (10.1%)↑57.9%prior 19
Ran off road - left28 (9.5%)↑115.4%prior 13
Lost Control17 (5.7%)↓-15.0%prior 20
Ran off road - straight13 (4.4%)↓-7.1%prior 14
Driver Distraction: Other interior distraction11 (3.7%)↑10.0%prior 10
Other (explain in narrative): Other10 (3.4%)↓-33.3%prior 15
Followed too close8 (2.7%)↑60.0%prior 5
FTYROW: From stop sign6 (2%)→0.0%prior 6
Operating vehicle in an reckless, erratic, careless, negligent manner5 (1.7%)↓-44.4%prior 9

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 adverse road surfaces increased significantly in 2019. Crashes on dry roads accounted for 42.5% of the total, down from 66.7% in 2018. Concurrently, crashes on icy or frost-covered surfaces tripled from 9 to 27, and collisions on snow-covered roads increased from 21 to 25. The share of crashes in clear weather also decreased, while the share in cloudy conditions and snow increased.

Weather

Clear106 (58.9%)
↓-21.5%prior 135
Cloudy35 (19.4%)
↑40.0%prior 25
Snow12 (6.7%)
↑50.0%prior 8
Freezing rain/drizzle8 (4.4%)
Rain7 (3.9%)
↓-41.7%prior 12
Blowing Snow4 (2.2%)
Sleet, hail3 (1.7%)
Severe Winds2 (1.1%)
Fog, smoke, smog2 (1.1%)
Other (explain in narrative)1 (0.6%)

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

Lighting

Daylight117 (64.3%)
↑4.5%prior 112
Dark - roadway not lighted46 (25.3%)
↓-9.8%prior 51
Dusk8 (4.4%)
→0.0%prior 8
Dark - roadway lighted5 (2.7%)
↓-28.6%prior 7
Dawn5 (2.7%)
↓-54.5%prior 11
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry90 (48.6%)
↓-28.6%prior 126
Ice/frost27 (14.6%)
↑200.0%prior 9
Snow25 (13.5%)
↑19.0%prior 21
Wet20 (10.8%)
↑17.6%prior 17
Gravel16 (8.6%)
↑14.3%prior 14
Slush4 (2.2%)
Other (explain in narrative)2 (1.1%)
Mud, dirt1 (0.5%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford and Chevrolet models leading in both years. The number of Fords involved increased from 76 to 81, while the combined count for Chevrolet models decreased from 124 to 99. An analysis of persons involved in crashes shows an increase in representation from the 26-34 age group (from 84 to 107 individuals) and the 65+ age group (from 73 to 92 individuals).

Top Vehicle Makes (383 vehicles)

1
FORD81 (21.1%)
↑6.6%prior 76
2
CHEV74 (19.3%)
→0.0%prior 74
3
CHEVROLET25 (6.5%)
↓-50.0%prior 50
4
DODG24 (6.3%)
↑14.3%prior 21
5
JEEP16 (4.2%)
↑23.1%prior 13
6
DODGE14 (3.7%)
↓-17.6%prior 17
7
GMC13 (3.4%)
↓-18.8%prior 16
8
TOYT11 (2.9%)
↓-8.3%prior 12
9
BUICK10 (2.6%)
10
PONT8 (2.1%)
↓-20.0%prior 10

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

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

Sex Distribution (358 persons with recorded sex)

Male225 (62.8%)
↑19.0%prior 189
Female133 (37.2%)
↑64.2%prior 81

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: 296
  • Total persons involved: 601
  • Total vehicles involved: 383

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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