Yearly Traffic Safety Analysis

730 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Warren County, total crashes decreased slightly from 742 in 2018 to 730 in 2019, a change of -1.6%. While overall crash volume remained stable, the number of fatalities saw a significant reduction, falling from 5 in the prior year to 1 in the current year. The total number of injuries, however, increased by 7.5% from 213 to 229.

730

▼ -1.6%was 742

Total Crash Events

1

▼ -80.0%was 5

Persons Killed

229

▲ 7.5%was 213

Persons Injured

1

▼ -80.0%was 5

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 trends in Warren County were relatively stable year-over-year, with a minor 1.6% decrease in total incidents from 742 to 730. However, the outcomes of these crashes shifted, showing a notable 80% decrease in fatalities from 5 to 1. Conversely, total injuries rose by 7.5%, from 213 in 2018 to 229 in 2019.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 5↓-80.0%

2

Pedestrians Injured

Prior: 2→0.0%

3

Cyclists Injured

Prior: 1↑200.0%

224

Motorists Injured

Prior: 210↑6.7%

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 showed some shifts between the two periods. The peak day for crashes moved from Friday (130 crashes) in 2018 to Thursday (122 crashes) in 2019. The peak hour for collisions remained consistent at the 7 a.m. hour in both years, with crash counts during this time increasing from 64 to 70.

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 improved significantly year-over-year, with fatal crashes decreasing from 5 in 2018 to 1 in 2019. This corresponds to a drop in the fatal crash rate from 0.7% to 0.1% of all incidents. The number of crashes resulting in serious injuries increased from 15 to 19, while crashes with possible injuries decreased from 97 to 91. The proportion of non-injury crashes remained stable at approximately 76% in both periods.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.1%
↓-80.0%prior 5
Serious Injury19serious injury crashes2.6%
↑26.7%prior 15
Minor Injury65minor injury crashes8.9%
↑1.6%prior 64
Possible Injury91possible injury crashes12.5%
↓-6.2%prior 97
No Injury554no injury crashes75.9%
↓-1.2%prior 561

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 leading contributing factor in both years, with a consistent count of 168 in 2018 and 169 in 2019. The top five factors shifted slightly, with 'Driving too fast for conditions' increasing by 27.5% from 40 to 51 incidents. 'Failure to yield from a stop sign' also saw a notable increase in count, rising from 35 to 45 crashes. Conversely, incidents attributed to 'Followed too close' decreased from 53 to 49.

Officer-Reported Primary Contributing Cause

Animal169 (23.2%)↑0.6%prior 168
Lost Control64 (8.8%)↑10.3%prior 58
Driving too fast for conditions51 (7%)↑27.5%prior 40
Followed too close49 (6.7%)↓-7.5%prior 53
FTYROW: From stop sign45 (6.2%)↑28.6%prior 35
Ran off road - left42 (5.8%)↑20.0%prior 35
Other (explain in narrative): Other35 (4.8%)↓-25.5%prior 47
Ran off road - straight31 (4.2%)↓-3.1%prior 32
Driver Distraction: Other interior distraction25 (3.4%)↑4.2%prior 24
FTYROW: Making left turn19 (2.6%)↓-42.4%prior 33

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 environmental conditions under which crashes occurred showed some year-over-year changes, particularly related to road surface. Crashes on snowy or icy roads increased notably, with snow-related incidents rising from 31 to 51 and ice-related incidents increasing from 31 to 50. Correspondingly, crashes on dry and wet surfaces saw a decrease. The number of crashes in daylight was identical at 402 for both years, and the distribution across other lighting conditions remained largely consistent.

Weather

Clear349 (58.6%)
↓-7.7%prior 378
Cloudy143 (24.0%)
↑7.5%prior 133
Snow41 (6.9%)
↑20.6%prior 34
Rain33 (5.5%)
↓-25.0%prior 44
Freezing rain/drizzle13 (2.2%)
↓-27.8%prior 18
Blowing Snow11 (1.8%)
Fog, smoke, smog4 (0.7%)
Other (explain in narrative)2 (0.3%)

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

Lighting

Daylight402 (67.2%)
→0.0%prior 402
Dark - roadway not lighted99 (16.6%)
↓-12.4%prior 113
Dark - roadway lighted55 (9.2%)
↓-9.8%prior 61
Dawn26 (4.3%)
↑62.5%prior 16
Dusk16 (2.7%)
↓-33.3%prior 24

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

Road Surface

Dry391 (65.4%)
↓-9.1%prior 430
Wet73 (12.2%)
↓-21.5%prior 93
Snow51 (8.5%)
↑64.5%prior 31
Ice/frost50 (8.4%)
↑61.3%prior 31
Gravel23 (3.8%)
↑35.3%prior 17
Slush7 (1.2%)
→0.0%prior 7
Mud, dirt1 (0.2%)
Sand1 (0.2%)
Other (explain in narrative)1 (0.2%)

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

Vehicles & Demographics

Analysis of vehicles and persons involved shows a shift in demographics. The number of persons aged 26-34 involved in crashes increased from 203 to 247, while involvement for the 45-54 age group decreased from 221 to 201. Among vehicle makes, Ford remained the most frequently involved, but its count dropped from 243 to 198. Conversely, Chevrolet vehicles (listed as 'CHEV') saw an increase in crash involvement from 162 to 183.

Top Vehicle Makes (1,122 vehicles)

1
FORD198 (17.6%)
↓-18.5%prior 243
2
CHEV183 (16.3%)
↑13.0%prior 162
3
CHEVROLET69 (6.1%)
↑30.2%prior 53
4
DODG66 (5.9%)
↓-14.3%prior 77
5
JEEP53 (4.7%)
↑10.4%prior 48
6
TOYT53 (4.7%)
↓-29.3%prior 75
7
GMC43 (3.8%)
↑53.6%prior 28
8
HOND36 (3.2%)
↑28.6%prior 28
9
DODGE31 (2.8%)
↑24.0%prior 25
10
NISS26 (2.3%)
↓-13.3%prior 30

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

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

Sex Distribution (1,042 persons with recorded sex)

Male581 (55.8%)
↑5.3%prior 552
Female461 (44.2%)
↑14.4%prior 403

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: 730
  • Total persons involved: 1,553
  • Total vehicles involved: 1,122

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

ThatCarHitMe.com · An Injuria.ai Company