Yearly Traffic Safety Analysis

315 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In Clayton County, total vehicle crashes remained relatively stable, increasing slightly from 309 in 2020 to 315 in 2021. This minor 1.9% rise in total incidents was accompanied by a concerning 50% increase in fatalities, from 4 to 6 deaths. However, the total number of injuries reported saw a significant year-over-year decrease of 26.3%, falling from 76 in 2020 to 56 in 2021.

315

▲ 1.9%was 309

Total Crash Events

6

▲ 50.0%was 4

Persons Killed

56

▼ -26.3%was 76

Persons Injured

6

▲ 50.0%was 4

Fatal Crash Events

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (6) 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

Overall crash trends in Clayton County show a slight increase in volume from 309 incidents in 2020 to 315 in 2021. While the total number of crashes rose by only 1.9%, fatal crashes increased from 4 to 6. Conversely, the number of people injured in crashes declined from 76 to 56, suggesting a complex shift in crash outcomes rather than a simple upward or downward trend.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

5

Motorists Killed

Prior: 4↑25.0%

1

Pedestrians Injured

Prior: 1→0.0%

55

Motorists Injured

Prior: 71↓-22.5%

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 year-over-year. The peak day for collisions moved from Friday (60 crashes) in 2020 to Saturday (53 crashes) in 2021, with Wednesday crashes seeing a notable increase from 23 to 48. The peak hour also shifted later into the evening, from 6 p.m. in 2020 (24 crashes) to a tie between 8 p.m. and 9 p.m. in 2021 (28 crashes each).

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 outcomes changed notably between the two periods. The number and rate of fatal crashes increased, rising from 4 incidents (1.3% of total) in 2020 to 6 incidents (1.9% of total) in 2021. In contrast, serious injury crashes decreased significantly, falling from 13 in the prior year to 7 in the current year. The proportion of crashes resulting in no injuries grew from 79.9% in 2020 to 81.9% in 2021.

Outcome by Severity (Crash Events)

Fatal6fatal crashes1.9%
↑50.0%prior 4
Serious Injury7serious injury crashes2.2%
↓-46.2%prior 13
Minor Injury26minor injury crashes8.3%
→0.0%prior 26
Possible Injury18possible injury crashes5.7%
↓-5.3%prior 19
No Injury258no injury crashes81.9%
↑4.5%prior 247

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 periods, increasing in count from 145 in 2020 to 157 in 2021. "Lost Control" became the second-most cited factor in 2021 with 23 incidents, up from 19 incidents in the prior year. Crashes attributed to "Driving too fast for conditions" also increased from 12 to 15. Conversely, incidents where a driver "Ran off road - left" decreased from 19 to 16.

Officer-Reported Primary Contributing Cause

Animal157 (49.8%)↑8.3%prior 145
Lost Control23 (7.3%)↑21.1%prior 19
Ran off road - left16 (5.1%)↓-15.8%prior 19
Driving too fast for conditions15 (4.8%)↑25.0%prior 12
Other (explain in narrative): Other14 (4.4%)
Ran off road - straight11 (3.5%)↑10.0%prior 10
Driver Distraction: Other interior distraction9 (2.9%)↑50.0%prior 6
Operating vehicle in an reckless, erratic, careless, negligent manner8 (2.5%)↑14.3%prior 7
FTYROW: From stop sign7 (2.2%)→0.0%prior 7
Driver Distraction: Exterior distraction5 (1.6%)

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 shifted between periods. Crashes happening in daylight decreased from 120 in 2020 to 94 in 2021, while those in unlit dark conditions increased from 39 to 50. Similarly, incidents on dry roads fell from 123 to 104, while crashes on roads with snow or gravel both increased, from 15 to 20 incidents for each condition.

Weather

Clear122 (72.2%)
↓-3.9%prior 127
Cloudy21 (12.4%)
↓-12.5%prior 24
Rain10 (5.9%)
↑25.0%prior 8
Snow10 (5.9%)
↑25.0%prior 8
Freezing rain/drizzle5 (3.0%)
↓-16.7%prior 6
Fog, smoke, smog1 (0.6%)

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

Lighting

Daylight94 (55.3%)
↓-21.7%prior 120
Dark - roadway not lighted50 (29.4%)
↑28.2%prior 39
Dark - roadway lighted14 (8.2%)
↑27.3%prior 11
Dawn8 (4.7%)
Dusk4 (2.4%)

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

Road Surface

Dry104 (61.2%)
↓-15.4%prior 123
Snow20 (11.8%)
↑33.3%prior 15
Gravel20 (11.8%)
↑33.3%prior 15
Wet11 (6.5%)
↓-8.3%prior 12
Ice/frost10 (5.9%)
→0.0%prior 10
Slush3 (1.8%)
Sand1 (0.6%)
Other (explain in narrative)1 (0.6%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford (72) and Chevrolet (92 combined) leading in 2021, similar to their top rankings in 2020 (77 and 102, respectively). The age demographics of persons involved in crashes showed a shift; the share of individuals in the 16-20 age group decreased from 13.2% in 2020 to 10.4% in 2021. Concurrently, the representation of the 26-34 age group (12.9% to 15.6%) and the 65+ age group (10.7% to 13.2%) increased.

Top Vehicle Makes (376 vehicles)

1
FORD72 (19.1%)
↓-6.5%prior 77
2
CHEV53 (14.1%)
↓-18.5%prior 65
3
CHEVROLET39 (10.4%)
↑5.4%prior 37
4
DODG22 (5.9%)
↑15.8%prior 19
5
GMC17 (4.5%)
↑88.9%prior 9
6
JEEP14 (3.7%)
↑7.7%prior 13
7
DODGE14 (3.7%)
↓-26.3%prior 19
8
PONT9 (2.4%)
9
CHRY9 (2.4%)
10
TOYT8 (2.1%)
→0.0%prior 8

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

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

Sex Distribution (295 persons with recorded sex)

Male173 (58.6%)
↓-27.3%prior 238
Female122 (41.4%)
↓-8.3%prior 133

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: 315
  • Total persons involved: 537
  • Total vehicles involved: 376

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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