Yearly Traffic Safety Analysis

177 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In O'Brien County, total crashes rose from 151 in 2018 to 177 in 2019, a 17.2% increase. This rise was accompanied by an increase in total injuries from 67 to 79 and fatalities from 2 to 3. A notable shift occurred in crash severity, where the count of serious injury crashes decreased from 9 to 2, while minor injury crashes increased from 16 to 29.

177

▲ 17.2%was 151

Total Crash Events

3

▲ 50.0%was 2

Persons Killed

79

▲ 17.9%was 67

Persons Injured

3

▲ 50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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

Traffic crashes in O'Brien County showed a rising trend in 2019 compared to the previous year. The total number of collisions increased by 17.2%, from 151 to 177. Similarly, the number of people injured grew by 17.9% from 67 to 79, and fatalities increased from 2 to 3.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 2↑50.0%

2

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 1→0.0%

76

Motorists Injured

Prior: 65↑16.9%

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. While Tuesday remained the peak day for crashes in both 2018 (32 crashes) and 2019 (40 crashes), the peak hour shifted. In 2019, the highest number of crashes occurred at 3 p.m. (16 crashes), moving from the 12 p.m. peak (14 crashes) observed in 2018.

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

The severity of crashes shifted year-over-year. While the number of fatal crashes increased from 2 to 3, crashes resulting in serious injuries saw a significant drop from 9 incidents in 2018 to just 2 in 2019. Conversely, the number of crashes involving minor injuries increased from 16 to 29. The proportion of crashes with no injuries remained stable at approximately 64% in both years.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.7%
↑50.0%prior 2
Serious Injury2serious injury crashes1.1%
↓-77.8%prior 9
Minor Injury29minor injury crashes16.4%
↑81.3%prior 16
Possible Injury30possible injury crashes16.9%
↑11.1%prior 27
No Injury113no injury crashes63.8%
↑16.5%prior 97

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

The leading contributing factors for crashes changed between 2018 and 2019. In 2019, "Driving too fast for conditions" became the primary factor, with its crash count increasing from 12 to 27. The top factor from 2018, "Animal," saw its count decrease from 17 to 15 crashes. Failure to yield from a stop sign remained a top concern, with incidents increasing from 14 in 2018 to 18 in 2019.

Officer-Reported Primary Contributing Cause

Driving too fast for conditions27 (15.3%)↑125.0%prior 12
FTYROW: From stop sign18 (10.2%)↑28.6%prior 14
Lost Control16 (9%)↑14.3%prior 14
Ran off road - left15 (8.5%)↑36.4%prior 11
Animal15 (8.5%)↓-11.8%prior 17
Improper Backing10 (5.6%)↑42.9%prior 7
Ran off road - straight8 (4.5%)↑33.3%prior 6
Followed too close8 (4.5%)↓-27.3%prior 11
Made improper turn6 (3.4%)
Other (explain in narrative): Other6 (3.4%)

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

Road & Environmental Conditions

While the majority of crashes in both years occurred on dry roads in clear, daylight conditions, there were notable shifts in adverse condition crashes. The proportion of crashes happening in darkness increased, accounting for 28.2% of incidents in 2019 compared to 19.9% in 2018. Additionally, the share of crashes on roads with snow, ice, or slush grew from 27.8% in 2018 to 32.2% in 2019.

Weather

Clear97 (58.1%)
↑14.1%prior 85
Cloudy37 (22.2%)
↑68.2%prior 22
Snow14 (8.4%)
↑7.7%prior 13
Rain7 (4.2%)
→0.0%prior 7
Blowing Snow6 (3.6%)
Fog, smoke, smog5 (3.0%)
→0.0%prior 5
Freezing rain/drizzle1 (0.6%)

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

Lighting

Daylight110 (65.9%)
↑5.8%prior 104
Dark - roadway not lighted36 (21.6%)
↑80.0%prior 20
Dark - roadway lighted14 (8.4%)
↑75.0%prior 8
Dawn4 (2.4%)
Dusk3 (1.8%)

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

Road Surface

Dry86 (51.5%)
↑8.9%prior 79
Snow27 (16.2%)
↑12.5%prior 24
Ice/frost27 (16.2%)
↑80.0%prior 15
Wet18 (10.8%)
↑38.5%prior 13
Gravel4 (2.4%)
↓-20.0%prior 5
Slush3 (1.8%)
Mud, dirt2 (1.2%)

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

Vehicles & Demographics

An analysis of vehicles involved shows that Chevrolet-branded vehicles (64) surpassed Ford (38) as the most common make in 2019 crashes, reversing the order from 2018 when Ford led with 42 incidents to Chevrolet's 53. Regarding driver age, the proportion of persons aged 35-44 involved in crashes increased from 11.9% to 15.0% year-over-year. In contrast, the 16-20 age group's representation decreased proportionally from 16.2% of persons involved in 2018 to 12.0% in 2019.

Top Vehicle Makes (286 vehicles)

1
CHEV45 (15.7%)
↑21.6%prior 37
2
FORD38 (13.3%)
↓-9.5%prior 42
3
CHEVROLET19 (6.6%)
↑18.8%prior 16
4
GMC14 (4.9%)
↓-6.7%prior 15
5
DODG14 (4.9%)
↑16.7%prior 12
6
CHRY12 (4.2%)
↑20.0%prior 10
7
BUIC12 (4.2%)
↓-7.7%prior 13
8
PONT11 (3.8%)
→0.0%prior 11
9
PETERBILT9 (3.1%)
10
BUICK8 (2.8%)

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

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

Sex Distribution (261 persons with recorded sex)

Male149 (57.1%)
↑26.3%prior 118
Female112 (42.9%)
↑47.4%prior 76

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: 177
  • Total persons involved: 400
  • Total vehicles involved: 286

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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