Yearly Traffic Safety Analysis

404 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In 2021, Plymouth County recorded 404 total vehicle crashes, a marginal increase from the 402 crashes documented in 2020. Despite the stable crash volume, the number of fatalities decreased significantly, falling from 9 in 2020 to 4 in 2021. The number of injuries also saw a notable decline, dropping from 149 to 118 year-over-year.

404

▲ 0.5%was 402

Total Crash Events

4

▼ -55.6%was 9

Persons Killed

118

▼ -20.8%was 149

Persons Injured

4

▼ -50.0%was 8

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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

The overall crash trend in Plymouth County remained stable, with a total of 404 crashes in 2021 compared to 402 in the prior year, an increase of less than 1%. However, the severity of these incidents decreased, as total fatalities fell by 55.6% from 9 to 4, and total injuries declined by 20.8% from 149 to 118.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

1

Cyclists Killed

Prior: 0→%

2

Motorists Killed

Prior: 9↓-77.8%

2

Pedestrians Injured

Prior: 0→%

0

Cyclists Injured

Prior: 0→0.0%

116

Motorists Injured

Prior: 149↓-22.1%

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 temporal patterns of crashes showed some shifts between the two periods. While Friday remained the peak day for crashes in both 2021 (70 crashes) and 2020 (72 crashes), the peak hour moved from the 4 PM hour in 2020 to the 5 PM hour in 2021. Monthly crash distribution also varied, with the highest volumes in 2021 occurring in November (48) and October (42), whereas 2020 saw its highest totals in October (55) and January (53).

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

Crash severity decreased in 2021 compared to the previous year. The number of fatal crashes was cut in half, dropping from 8 in 2020 to 4 in 2021, and their share of all crashes fell from 2.0% to 1.0%. Crashes resulting in serious injuries also declined from 12 to 8. Correspondingly, the proportion of crashes with no reported injuries increased from 71.4% in 2020 to 74.0% in 2021.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1%
↓-50.0%prior 8
Serious Injury8serious injury crashes2%
↓-33.3%prior 12
Minor Injury53minor injury crashes13.1%
↓-7.0%prior 57
Possible Injury40possible injury crashes9.9%
↑5.3%prior 38
No Injury299no injury crashes74%
↑4.2%prior 287

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 involving an animal remained the leading contributing factor in both years, increasing in count from 88 crashes in 2020 to 96 in 2021. 'Lost Control' was the second-most cited factor in both periods, also rising from 37 to 42 incidents. Notably, crashes attributed to 'Driving too fast for conditions' decreased by 46.7% (from 30 to 16 incidents), while those involving 'Failure to yield right-of-way from a stop sign' increased by 36.8% (from 19 to 26 incidents).

Officer-Reported Primary Contributing Cause

Animal96 (23.8%)↑9.1%prior 88
Lost Control42 (10.4%)↑13.5%prior 37
FTYROW: From stop sign26 (6.4%)↑36.8%prior 19
Other (explain in narrative): Other20 (5%)↑42.9%prior 14
FTYROW: At uncontrolled intersection20 (5%)↑33.3%prior 15
Ran off road - straight18 (4.5%)↓-5.3%prior 19
Ran off road - left17 (4.2%)↓-10.5%prior 19
Followed too close16 (4%)↓-36.0%prior 25
Ran Stop Sign16 (4%)↓-5.9%prior 17
Driving too fast for conditions16 (4%)↓-46.7%prior 30

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 majority of crashes in both periods occurred in clear weather and on dry road surfaces. In 2021, the number of crashes happening in daylight conditions decreased from 230 to 215, while crashes in dark conditions increased from 81 to 92. The count of crashes on adverse road surfaces like ice, snow, or wet pavement saw a slight decline from 87 in 2020 to 83 in 2021.

Weather

Clear234 (73.1%)
↑5.4%prior 222
Cloudy46 (14.4%)
↓-11.5%prior 52
Snow12 (3.8%)
↓-14.3%prior 14
Rain11 (3.4%)
↑83.3%prior 6
Blowing Snow9 (2.8%)
↑28.6%prior 7
Freezing rain/drizzle4 (1.3%)
↓-66.7%prior 12
Fog, smoke, smog3 (0.9%)
Severe Winds1 (0.3%)

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

Lighting

Daylight215 (66.8%)
↓-6.5%prior 230
Dark - roadway not lighted57 (17.7%)
↓-6.6%prior 61
Dark - roadway lighted30 (9.3%)
↑50.0%prior 20
Dusk9 (2.8%)
Dawn6 (1.9%)
↓-14.3%prior 7
Dark - unknown roadway lighting5 (1.6%)

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

Road Surface

Dry240 (74.3%)
↑1.7%prior 236
Ice/frost23 (7.1%)
↓-34.3%prior 35
Snow20 (6.2%)
↓-9.1%prior 22
Wet19 (5.9%)
↑35.7%prior 14
Gravel13 (4.0%)
↑85.7%prior 7
Slush5 (1.5%)
Mud, dirt2 (0.6%)
Sand1 (0.3%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes shifted between the two years. While Ford was the most common make in 2020 with 115 vehicles, its involvement decreased to 95 in 2021. In contrast, Chevrolet-branded vehicles became the most frequent, increasing from 105 to 135. An analysis of persons involved shows a decrease in the number of individuals aged 16-25, from 199 in 2020 to 182 in 2021. The count of individuals aged 65 and older remained nearly the same, at 87 in 2021 compared to 89 in the prior year.

Top Vehicle Makes (601 vehicles)

1
FORD95 (15.8%)
↓-17.4%prior 115
2
CHEV84 (14%)
↑31.3%prior 64
3
CHEVROLET51 (8.5%)
↑24.4%prior 41
4
GMC32 (5.3%)
↑23.1%prior 26
5
DODG19 (3.2%)
↓-13.6%prior 22
6
HONDA18 (3%)
↑100.0%prior 9
7
JEEP18 (3%)
↑5.9%prior 17
8
TOYO16 (2.7%)
↑33.3%prior 12
9
RAM14 (2.3%)
↑16.7%prior 12
10
BUIC14 (2.3%)
↓-41.7%prior 24

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

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

Sex Distribution (481 persons with recorded sex)

Male277 (57.6%)
↓-24.7%prior 368
Female204 (42.4%)
↑1.0%prior 202

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: 404
  • Total persons involved: 751
  • Total vehicles involved: 601

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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