Yearly Traffic Safety Analysis

358 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In Winneshiek County, total traffic crashes increased by 12.9% from 317 in 2020 to 358 in 2021. This rise was accompanied by a 26.8% increase in injuries, from 82 to 104, and a doubling of fatalities from 2 to 4. The most notable percentage change was a 125% increase in crashes involving driving under the influence (DUI), which rose from 8 incidents in 2020 to 18 in 2021.

358

▲ 12.9%was 317

Total Crash Events

4

▲ 100.0%was 2

Persons Killed

104

▲ 26.8%was 82

Persons Injured

3

▲ 50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (4) 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 · 2021-01-01 to 2021-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety trends in Winneshiek County worsened year-over-year. The total number of crashes rose from 317 in 2020 to 358 in 2021, an increase of 12.9%. Correspondingly, the number of people injured increased by 26.8% from 82 to 104, and the number of fatalities doubled from 2 to 4.

Vulnerable Road User Casualties

4

Motorists Killed

Prior: 2↑100.0%

104

Motorists Injured

Prior: 80↑30.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · 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 2021, the peak day for crashes was Saturday with 72 incidents, a change from Friday (49 crashes) in the prior year. The peak hour also shifted an hour later, from 5 p.m. in 2020 (33 crashes) to 6 p.m. in 2021 (28 crashes). The distribution of crashes in 2021 showed a stronger concentration on Friday and Saturday, whereas in 2020, crashes were more evenly spread throughout the week.

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes increased from 2020 to 2021. The number of fatal crashes increased from 2 to 3, and the total number of people killed rose from 2 to 4. Crashes resulting in any type of injury (serious, minor, or possible) grew from 65 in 2020 to 86 in 2021, representing a larger share of total crashes (24.0% in 2021 vs. 20.5% in 2020).

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.8%
↑50.0%prior 2
Serious Injury12serious injury crashes3.4%
↑20.0%prior 10
Minor Injury40minor injury crashes11.2%
↑21.2%prior 33
Possible Injury34possible injury crashes9.5%
↑54.5%prior 22
No Injury269no injury crashes75.1%
↑7.6%prior 250

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the leading contributing factor in both years, with counts increasing from 131 in 2020 to 138 in 2021. While the top three factors—animal collisions, losing control, and running off the road—retained their rankings, there were notable shifts in other categories. Crashes attributed to 'Failure to Yield Right of Way from a stop sign' doubled in count, from 7 incidents in 2020 to 14 in 2021. Conversely, crashes involving 'Following too close' decreased from 14 to 8 incidents.

Officer-Reported Primary Contributing Cause

Animal138 (38.5%)↑5.3%prior 131
Lost Control33 (9.2%)↓-2.9%prior 34
Ran off road - straight19 (5.3%)↑11.8%prior 17
Ran off road - left17 (4.7%)↑88.9%prior 9
Driving too fast for conditions15 (4.2%)↓-6.3%prior 16
FTYROW: From stop sign14 (3.9%)↑100.0%prior 7
Other (explain in narrative): Other14 (3.9%)↑180.0%prior 5
Ran Stop Sign8 (2.2%)↑60.0%prior 5
Followed too close8 (2.2%)↓-42.9%prior 14
Driver Distraction: Other interior distraction7 (2%)↑16.7%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The conditions under which crashes occurred remained broadly similar, with most incidents in both years happening in daylight on dry roads. However, there was a decrease in the number of crashes attributed to adverse weather, particularly snow, which fell from 18 incidents in 2020 to 7 in 2021. Consequently, the share of crashes occurring on adverse road surfaces like snow, ice, or wet pavement decreased from 26.7% of reported incidents in 2020 to 22.8% in 2021.

Weather

Clear166 (70.3%)
↑14.5%prior 145
Cloudy44 (18.6%)
↑29.4%prior 34
Rain9 (3.8%)
↑28.6%prior 7
Snow7 (3.0%)
↓-61.1%prior 18
Freezing rain/drizzle5 (2.1%)
Severe Winds2 (0.8%)
Other (explain in narrative)1 (0.4%)
Blowing Snow1 (0.4%)
Blowing sand, soil, dirt1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Weather condition at time of crash

Lighting

Daylight141 (58.8%)
↑8.5%prior 130
Dark - roadway not lighted65 (27.1%)
↑12.1%prior 58
Dark - roadway lighted20 (8.3%)
↑53.8%prior 13
Dusk9 (3.8%)
↑28.6%prior 7
Dawn3 (1.3%)
↓-57.1%prior 7
Dark - unknown roadway lighting2 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Lighting condition field

Road Surface

Dry170 (70.5%)
↑19.7%prior 142
Wet21 (8.7%)
↑40.0%prior 15
Snow17 (7.1%)
↓-34.6%prior 26
Gravel16 (6.6%)
↓-5.9%prior 17
Ice/frost15 (6.2%)
↑50.0%prior 10
Slush1 (0.4%)
↓-80.0%prior 5
Mud, dirt1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes were consistent year-over-year, with Ford and Chevrolet vehicles being the most common in both periods. Ford vehicle involvements increased from 81 to 100, while Chevrolet involvements remained stable at 103 in 2020 and 105 in 2021. Analysis of persons involved shows a notable increase in the 65+ age group, which grew from 76 individuals in 2020 to 104 in 2021.

Top Vehicle Makes (504 vehicles)

1
FORD100 (19.8%)
↑23.5%prior 81
2
CHEV64 (12.7%)
↓-4.5%prior 67
3
CHEVROLET41 (8.1%)
↑13.9%prior 36
4
GMC28 (5.6%)
↑33.3%prior 21
5
JEEP20 (4%)
↑53.8%prior 13
6
HOND20 (4%)
↑300.0%prior 5
7
HONDA15 (3%)
↑50.0%prior 10
8
DODG14 (2.8%)
↓-26.3%prior 19
9
BUIC14 (2.8%)
→0.0%prior 14
10
TOYT14 (2.8%)
↑133.3%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Vehicle unit records

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

Sex Distribution (371 persons with recorded sex)

Male220 (59.3%)
↓-5.6%prior 233
Female151 (40.7%)
↓-6.8%prior 162

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · 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: 2021-01-01 through 2021-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2021-01-01 through 2021-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 358
  • Total persons involved: 655
  • Total vehicles involved: 504

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: 2021." Published September 9, 2026. Reporting period: 2021-01-01 to 2021-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2021-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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