Yearly Traffic Safety Analysis

375 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Winneshiek County, total traffic crashes decreased by 5.5% from 397 in 2018 to 375 in 2019. Despite the overall drop in collisions, the severity of crashes worsened significantly. The most notable year-over-year shift was the increase in total fatalities from 2 in 2018 to 5 in 2019, and a rise in total injuries from 89 to 103.

375

▼ -5.5%was 397

Total Crash Events

5

▲ 150.0%was 2

Persons Killed

103

▲ 15.7%was 89

Persons Injured

5

▲ 400.0%was 1

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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 collisions in Winneshiek County saw a 5.5% decrease, from 397 in 2018 to 375 in 2019. However, this downward trend in crash volume was accompanied by a rise in severity. Total injuries increased by 15.7% from 89 to 103, and fatalities more than doubled from 2 to 5 year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 2↑150.0%

1

Pedestrians Injured

Prior: 1→0.0%

102

Motorists Injured

Prior: 87↑17.2%

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 remained largely consistent between the two periods, with peaks occurring during the evening commute and on weekends. The peak day for crashes shifted from Friday (66 crashes) in 2018 to Saturday (69 crashes) in 2019. Similarly, the peak hour for collisions moved slightly earlier, from 5 p.m. (41 crashes) in the prior year to 4 p.m. (32 crashes) in the current year.

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 increased from 2018 to 2019. The number of fatal crashes rose from 1 to 5, and the total number of persons killed increased from 2 to 5. The number of crashes resulting in any injury (fatal, serious, minor, or possible) grew from 68 in 2018 to 83 in 2019. Consequently, the proportion of crashes involving an injury or fatality increased from 17.1% of all crashes in 2018 to 22.1% in 2019.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.3%
↑400.0%prior 1
Serious Injury14serious injury crashes3.7%
↑7.7%prior 13
Minor Injury34minor injury crashes9.1%
↑13.3%prior 30
Possible Injury30possible injury crashes8%
↑25.0%prior 24
No Injury292no injury crashes77.9%
↓-11.2%prior 329

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 years, though the count of these incidents decreased by 23.3% from 180 in 2018 to 138 in 2019. The second and third-ranked factors, 'Lost Control' and 'Driving too fast for conditions,' also retained their rankings while their counts decreased by 24.4% (from 41 to 31) and 20.7% (from 29 to 23), respectively. Despite the decrease in counts, animal-related collisions still accounted for 36.8% of all crashes in 2019.

Officer-Reported Primary Contributing Cause

Animal138 (36.8%)↓-23.3%prior 180
Lost Control31 (8.3%)↓-24.4%prior 41
Driving too fast for conditions23 (6.1%)↓-20.7%prior 29
Ran off road - straight22 (5.9%)↑69.2%prior 13
FTYROW: From stop sign14 (3.7%)↑40.0%prior 10
Ran off road - left14 (3.7%)↑100.0%prior 7
Other (explain in narrative): Other11 (2.9%)↓-38.9%prior 18
Driver Distraction: Other interior distraction10 (2.7%)↑42.9%prior 7
Driver Distraction: Exterior distraction10 (2.7%)
Followed too close9 (2.4%)↓-47.1%prior 17

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 2018 and 2019, most crashes occurred in clear weather and on dry roads. However, there was a notable increase in crashes on adverse road surfaces in 2019. Collisions on roads with snow increased from 26 to 30, and crashes on icy or frosty surfaces rose from 15 to 26. The number of crashes during snowy weather conditions also increased from 13 to 20 year-over-year.

Weather

Clear168 (63.6%)
↑4.3%prior 161
Cloudy49 (18.6%)
↓-14.0%prior 57
Snow20 (7.6%)
↑53.8%prior 13
Rain11 (4.2%)
↑37.5%prior 8
Freezing rain/drizzle6 (2.3%)
↑20.0%prior 5
Fog, smoke, smog6 (2.3%)
Blowing Snow3 (1.1%)
↓-50.0%prior 6
Other (explain in narrative)1 (0.4%)

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

Lighting

Daylight167 (63.3%)
↓-1.8%prior 170
Dark - roadway not lighted62 (23.5%)
↑19.2%prior 52
Dark - roadway lighted22 (8.3%)
↑37.5%prior 16
Dusk8 (3.0%)
↓-38.5%prior 13
Dawn5 (1.9%)
→0.0%prior 5

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

Road Surface

Dry170 (64.6%)
↓-1.7%prior 173
Snow30 (11.4%)
↑15.4%prior 26
Ice/frost26 (9.9%)
↑73.3%prior 15
Wet20 (7.6%)
↑11.1%prior 18
Gravel14 (5.3%)
↓-17.6%prior 17
Slush3 (1.1%)
↓-40.0%prior 5

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

Vehicles & Demographics

Chevrolet and Ford vehicles were the most frequently involved makes in crashes during both periods. The number of Chevrolet vehicles involved remained stable with 126 in 2018 and 130 in 2019, while Ford vehicles saw a notable increase from 69 to 96. Regarding driver age, the 16-20 age group saw the largest increase in persons involved in crashes, rising from 98 individuals in 2018 to 128 in 2019.

Top Vehicle Makes (523 vehicles)

1
FORD96 (18.4%)
↑39.1%prior 69
2
CHEV79 (15.1%)
↑12.9%prior 70
3
CHEVROLET51 (9.8%)
↓-8.9%prior 56
4
GMC32 (6.1%)
↑33.3%prior 24
5
BUIC20 (3.8%)
↑5.3%prior 19
6
TOYOTA18 (3.4%)
↑80.0%prior 10
7
TOYT17 (3.3%)
↑30.8%prior 13
8
DODG16 (3.1%)
↓-27.3%prior 22
9
JEEP15 (2.9%)
↓-28.6%prior 21
10
HONDA12 (2.3%)
↑50.0%prior 8

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

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

Sex Distribution (478 persons with recorded sex)

Male265 (55.4%)
↑13.7%prior 233
Female213 (44.6%)
↑29.1%prior 165

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: 375
  • Total persons involved: 778
  • Total vehicles involved: 523

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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