Yearly Traffic Safety Analysis

208 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Clarke County recorded 208 traffic crashes, a slight increase of just under 1% from the 206 crashes reported in 2018. While the overall crash volume remained stable, the number of fatalities was halved, decreasing from 4 in the prior year to 2 in 2019. The most notable shift was a 66.7% increase in serious injury crashes, which rose from 6 to 10 incidents year-over-year.

208

▲ 1.0%was 206

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

59

▼ -4.8%was 62

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 crash volume in Clarke County remained relatively stable, increasing by just two incidents from 206 in 2018 to 208 in 2019. Despite the stable crash total, the number of resulting injuries saw a slight decrease of 4.8% from 62 to 59. Fatalities showed a significant downward trend, dropping by 50% from 4 in the previous year to 2 in the current period.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 4↓-50.0%

59

Motorists Injured

Prior: 59→0.0%

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 timing of crashes shifted between the two periods. In 2019, the peak days for crashes were Thursday and Friday, each with 36 incidents, a change from 2018 when Sunday was the peak day with 35 crashes. The peak hour for collisions also moved slightly earlier in the day, from the 3 p.m. hour in 2018 (19 crashes) to the 2 p.m. hour in 2019 (17 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 the number of fatal crashes decreased from 3 in 2018 to 2 in 2019, crashes resulting in serious injuries increased by 66.7%, rising from 6 to 10 incidents. This caused the share of serious injury crashes to grow from 2.9% to 4.8% of all collisions. Conversely, the count of minor injury crashes fell from 24 to 20, and the proportion of crashes with no injuries increased from 76.2% to 77.4% of the total.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1%
↓-33.3%prior 3
Serious Injury10serious injury crashes4.8%
↑66.7%prior 6
Minor Injury20minor injury crashes9.6%
↓-16.7%prior 24
Possible Injury15possible injury crashes7.2%
↓-6.3%prior 16
No Injury161no injury crashes77.4%
↑2.5%prior 157

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 involving an animal remained the top contributing factor in both periods, though the count of such incidents decreased by 21.4% from 56 in 2018 to 44 in 2019. "Lost Control" moved up to become the second-most cited factor, with its count increasing 52.6% from 19 to 29. A significant change was observed in crashes due to "FTYROW: From stop sign," which saw a 250% increase in count, rising from 4 incidents in 2018 to 14 in 2019.

Officer-Reported Primary Contributing Cause

Animal44 (21.2%)↓-21.4%prior 56
Lost Control29 (13.9%)↑52.6%prior 19
Driving too fast for conditions16 (7.7%)↑6.7%prior 15
Other (explain in narrative): Other16 (7.7%)↑100.0%prior 8
FTYROW: From stop sign14 (6.7%)
Followed too close10 (4.8%)↑25.0%prior 8
Ran off road - straight10 (4.8%)↓-52.4%prior 21
Other (explain in narrative): No improper action7 (3.4%)
FTYROW: Making left turn7 (3.4%)→0.0%prior 7
Ran off road - left5 (2.4%)↓-50.0%prior 10

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

Road & Environmental Conditions

Crashes were more likely to occur in clear and dry conditions in 2019 compared to 2018. The number of crashes in clear weather increased from 74 to 111, and their share of total crashes grew from 35.9% to 53.4%. Similarly, collisions on dry roads rose from 90 to 117. Incidents occurring under adverse conditions, including rain, snow, and on wet or icy surfaces, all saw a decrease in count from the previous year.

Weather

Clear111 (65.7%)
↑50.0%prior 74
Cloudy30 (17.8%)
↓-33.3%prior 45
Snow10 (5.9%)
↓-33.3%prior 15
Rain8 (4.7%)
↓-42.9%prior 14
Freezing rain/drizzle6 (3.6%)
Fog, smoke, smog2 (1.2%)
↓-60.0%prior 5
Blowing Snow1 (0.6%)
Severe Winds1 (0.6%)

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

Lighting

Daylight110 (65.1%)
↑14.6%prior 96
Dark - roadway not lighted36 (21.3%)
↓-7.7%prior 39
Dark - roadway lighted16 (9.5%)
↓-11.1%prior 18
Dusk5 (3.0%)
↓-16.7%prior 6
Dawn2 (1.2%)

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

Road Surface

Dry117 (69.2%)
↑30.0%prior 90
Wet17 (10.1%)
↓-34.6%prior 26
Ice/frost15 (8.9%)
→0.0%prior 15
Snow14 (8.3%)
↑7.7%prior 13
Gravel5 (3.0%)
↓-54.5%prior 11
Other (explain in narrative)1 (0.6%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford and Chevrolet representing the most common makes in both 2018 and 2019. There was a notable shift in the age distribution of persons involved in crashes, with a significant increase in the 45-54 age group (from 39 to 69 persons) and the 55-64 age group (from 40 to 68 persons). Conversely, the number of persons involved from the 65+ age group decreased from 56 to 51.

Top Vehicle Makes (308 vehicles)

1
FORD50 (16.2%)
↓-2.0%prior 51
2
CHEV39 (12.7%)
↓-11.4%prior 44
3
CHEVROLET21 (6.8%)
→0.0%prior 21
4
DODG14 (4.5%)
↑7.7%prior 13
5
DODGE11 (3.6%)
→0.0%prior 11
6
FREIGHTLINER11 (3.6%)
↑37.5%prior 8
7
GMC10 (3.2%)
↑25.0%prior 8
8
NISS9 (2.9%)
↑28.6%prior 7
9
JEEP9 (2.9%)
↑28.6%prior 7
10
HOND7 (2.3%)
↑16.7%prior 6

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

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

Sex Distribution (295 persons with recorded sex)

Male187 (63.4%)
↑28.1%prior 146
Female108 (36.6%)
↑66.2%prior 65

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: 208
  • Total persons involved: 414
  • Total vehicles involved: 308

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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