Yearly Traffic Safety Analysis

314 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Clay County recorded 314 total vehicle crashes, an increase of 6.1% from the 296 crashes reported in 2021. While total crashes and injuries (101, up from 78) rose, the number of fatalities decreased from two to one. A notable shift in contributing factors was observed, with crashes attributed to 'driving too fast for conditions' increasing in count from 8 to 21 incidents year-over-year.

314

▲ 6.1%was 296

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

101

▲ 29.5%was 78

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic collisions in Clay County showed an upward trend in 2022 compared to the previous year. The total number of crashes increased by 6.1% from 296 to 314. Similarly, the number of people injured in these incidents rose by 29.5%, from 78 in 2021 to 101 in 2022.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 2↓-50.0%

4

Pedestrians Injured

Prior: 2↑100.0%

1

Cyclists Injured

Prior: 2↓-50.0%

96

Motorists Injured

Prior: 74↑29.7%

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · 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 2021 and 2022. The most common day for crashes moved from Thursday (49 crashes) in the prior year to Friday (51 crashes) in the current year. The peak hour for collisions also shifted slightly, from the 6 p.m. hour with 25 crashes in 2021 to the 5 p.m. hour with 24 crashes in 2022.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the number of fatal crashes decreased from two in 2021 to one in 2022, the overall proportion of crashes involving injuries increased. Crashes resulting in minor injuries rose from 18 to 30, increasing their share of all crashes from 6.1% to 9.6%. Conversely, the count of serious injury crashes decreased slightly from 7 to 6.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
↓-50.0%prior 2
Serious Injury6serious injury crashes1.9%
↓-14.3%prior 7
Minor Injury30minor injury crashes9.6%
↑66.7%prior 18
Possible Injury42possible injury crashes13.4%
↑10.5%prior 38
No Injury235no injury crashes74.8%
↑1.7%prior 231

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the most frequent contributing factor in both periods, though the count decreased slightly from 62 in 2021 to 59 in 2022. A significant year-over-year change was the increase in crashes attributed to 'driving too fast for conditions,' which rose in count from 8 to 21 incidents. In contrast, crashes due to 'followed too close' decreased from 21 to 9 incidents.

Officer-Reported Primary Contributing Cause

Animal59 (18.8%)↓-4.8%prior 62
Other (explain in narrative): Other42 (13.4%)↑68.0%prior 25
Driving too fast for conditions21 (6.7%)↑162.5%prior 8
FTYROW: From stop sign20 (6.4%)↓-13.0%prior 23
FTYROW: Making left turn15 (4.8%)↑7.1%prior 14
Ran Stop Sign14 (4.5%)↑16.7%prior 12
Ran off road - left14 (4.5%)↑7.7%prior 13
Lost Control11 (3.5%)↓-26.7%prior 15
Followed too close9 (2.9%)↓-57.1%prior 21
Operating vehicle in an reckless, erratic, careless, negligent manner8 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in clear weather and on dry roads remained the most common scenario in both years. A notable shift occurred in crashes related to winter weather; incidents on snow-covered road surfaces more than doubled from 12 to 28. Similarly, crashes reported in snowy weather conditions increased from 3 to 13 year-over-year.

Weather

Clear192 (73.0%)
↑9.7%prior 175
Cloudy34 (12.9%)
↑3.0%prior 33
Snow13 (4.9%)
Blowing Snow9 (3.4%)
↑80.0%prior 5
Rain7 (2.7%)
↓-46.2%prior 13
Severe Winds3 (1.1%)
Freezing rain/drizzle3 (1.1%)
Sleet, hail1 (0.4%)
Other (explain in narrative)1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Daylight198 (75.0%)
↑13.1%prior 175
Dark - roadway not lighted30 (11.4%)
↑20.0%prior 25
Dark - roadway lighted22 (8.3%)
↓-8.3%prior 24
Dusk9 (3.4%)
↑80.0%prior 5
Dawn3 (1.1%)
↓-66.7%prior 9
Dark - unknown roadway lighting2 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field

Road Surface

Dry190 (72.0%)
↑11.1%prior 171
Snow28 (10.6%)
↑133.3%prior 12
Ice/frost17 (6.4%)
↓-15.0%prior 20
Wet16 (6.1%)
↓-15.8%prior 19
Gravel9 (3.4%)
↓-18.2%prior 11
Slush3 (1.1%)
Sand1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet were the most common vehicle makes involved in crashes in both periods, with the number of vehicles from both brands increasing in 2022. Analysis of persons involved shows a notable increase in the 35-44 age group (from 69 to 101 individuals) and the 55-64 age group (from 69 to 110 individuals). Conversely, the number of people in the 16-20 age group involved in crashes decreased from 93 to 86.

Top Vehicle Makes (511 vehicles)

1
FORD108 (21.1%)
↑22.7%prior 88
2
CHEV96 (18.8%)
↑33.3%prior 72
3
CHEVROLET30 (5.9%)
↓-26.8%prior 41
4
GMC27 (5.3%)
→0.0%prior 27
5
JEEP25 (4.9%)
↑56.3%prior 16
6
DODG22 (4.3%)
↓-8.3%prior 24
7
BUIC17 (3.3%)
↑6.3%prior 16
8
CHRY17 (3.3%)
↑6.3%prior 16
9
NISS12 (2.3%)
↑50.0%prior 8
10
TOYO11 (2.2%)
↑83.3%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records

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

Sex Distribution (471 persons with recorded sex)

Male284 (60.3%)
↑32.1%prior 215
Female187 (39.7%)
↑6.3%prior 176

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 314
  • Total persons involved: 690
  • Total vehicles involved: 511

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: 2022." Published September 9, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2022-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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