Yearly Traffic Safety Analysis

518 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Plymouth County, total crashes increased by 8.4% from 478 in 2018 to 518 in 2019. While total fatalities and injuries decreased year-over-year, the most notable shift was a 63.6% increase in crashes involving a driver under the influence, which rose from 11 incidents in 2018 to 18 in 2019.

518

▲ 8.4%was 478

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

151

▼ -13.2%was 174

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 Plymouth County rose from 2018 to 2019, with total incidents increasing by 8.4% from 478 to 518. Despite the rise in total crashes, the number of people injured and killed saw a decline. Total injuries fell by 13.2% from 174 to 151, and fatalities decreased from 3 to 2.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

2

Pedestrians Injured

Prior: 4↓-50.0%

2

Cyclists Injured

Prior: 3↓-33.3%

147

Motorists Injured

Prior: 167↓-12.0%

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 temporal pattern of crashes shifted between the two periods. In 2019, Monday was the peak day for crashes with 90 incidents, a change from 2018 when Wednesday saw the most crashes (85). The peak hour also shifted slightly, moving from 3 p.m. in 2018 (36 crashes) to 4 p.m. in 2019 (38 crashes), maintaining a consistent afternoon rush hour peak.

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 showed a mixed trend year-over-year. The fatal crash count decreased from 3 in 2018 to 2 in 2019, with the corresponding rate falling from 0.63% to 0.39% of total crashes. While the share of 'Possible Injury' crashes decreased from 13.0% to 8.9%, the proportion of 'Minor Injury' crashes grew from 11.3% to 12.5%. Crashes resulting in no injury also increased as a share of the total, rising from 72.4% to 75.3%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.4%
↓-33.3%prior 3
Serious Injury15serious injury crashes2.9%
↑15.4%prior 13
Minor Injury65minor injury crashes12.5%
↑20.4%prior 54
Possible Injury46possible injury crashes8.9%
↓-25.8%prior 62
No Injury390no injury crashes75.3%
↑12.7%prior 346

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, increasing in count from 107 in 2018 to 136 in 2019. 'Driving too fast for conditions' saw a notable decrease, falling from the second-ranked factor with 43 crashes in 2018 to the fourth-ranked with 31 crashes in 2019. 'Lost Control' became the second most common factor in 2019 with 41 incidents, a slight decrease from 42 the prior year. The top five factors remained largely consistent, with their relative order shifting slightly.

Officer-Reported Primary Contributing Cause

Animal136 (26.3%)↑27.1%prior 107
Lost Control41 (7.9%)↓-2.4%prior 42
FTYROW: From stop sign33 (6.4%)↑10.0%prior 30
Driving too fast for conditions31 (6%)↓-27.9%prior 43
Followed too close27 (5.2%)↑35.0%prior 20
Other (explain in narrative): Other26 (5%)↓-3.7%prior 27
Ran off road - left19 (3.7%)↑26.7%prior 15
FTYROW: At uncontrolled intersection19 (3.7%)↑5.6%prior 18
Ran off road - straight17 (3.3%)↓-26.1%prior 23
FTYROW: Making left turn15 (2.9%)↑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

In both years, the majority of crashes occurred in clear weather (243 in 2019 vs. 238 in 2018) and during daylight hours (265 vs. 248). There was a notable decrease in crashes attributed to adverse winter road conditions. Crashes on icy or frosty roads fell from 53 in 2018 to 38 in 2019, and those on snowy surfaces decreased from 38 to 26. Conversely, crashes on wet roads increased from 39 to 51.

Weather

Clear243 (60.8%)
↑2.1%prior 238
Cloudy91 (22.8%)
↑37.9%prior 66
Rain25 (6.3%)
↑31.6%prior 19
Freezing rain/drizzle19 (4.8%)
→0.0%prior 19
Snow14 (3.5%)
↓-44.0%prior 25
Blowing Snow4 (1.0%)
↓-42.9%prior 7
Fog, smoke, smog3 (0.8%)
↓-50.0%prior 6
Severe Winds1 (0.3%)

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

Lighting

Daylight265 (66.3%)
↑6.9%prior 248
Dark - roadway not lighted79 (19.8%)
↓-8.1%prior 86
Dark - roadway lighted33 (8.3%)
→0.0%prior 33
Dusk13 (3.3%)
↑18.2%prior 11
Dawn9 (2.3%)
↓-18.2%prior 11
Dark - unknown roadway lighting1 (0.3%)

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

Road Surface

Dry265 (66.1%)
↑10.0%prior 241
Wet51 (12.7%)
↑30.8%prior 39
Ice/frost38 (9.5%)
↓-28.3%prior 53
Snow26 (6.5%)
↓-31.6%prior 38
Gravel13 (3.2%)
↑116.7%prior 6
Slush5 (1.2%)
↓-28.6%prior 7
Mud, dirt1 (0.2%)
Water (standing or moving)1 (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 persons involved in crashes reveals a significant demographic shift, with the 26-34 age group's involvement increasing from 100 individuals in 2018 to 191 in 2019. In contrast, the number of persons in the 16-20 age group decreased from 160 to 131. The top vehicle makes involved in crashes remained consistent, with Ford, Chevrolet, and GMC leading in both years, showing only minor fluctuations in their respective counts.

Top Vehicle Makes (769 vehicles)

1
FORD149 (19.4%)
↑12.0%prior 133
2
CHEV118 (15.3%)
↑8.3%prior 109
3
CHEVROLET54 (7%)
↑22.7%prior 44
4
GMC42 (5.5%)
↑31.3%prior 32
5
DODG32 (4.2%)
↑28.0%prior 25
6
JEEP29 (3.8%)
↓-9.4%prior 32
7
DODGE22 (2.9%)
↑57.1%prior 14
8
KIA19 (2.5%)
↓-5.0%prior 20
9
PONT19 (2.5%)
↓-24.0%prior 25
10
HOND17 (2.2%)
→0.0%prior 17

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

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

Sex Distribution (727 persons with recorded sex)

Male445 (61.2%)
↑38.6%prior 321
Female282 (38.8%)
↑28.8%prior 219

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: 518
  • Total persons involved: 1,077
  • Total vehicles involved: 769

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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