Yearly Traffic Safety Analysis

163 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Cherokee County, total traffic crashes decreased from 207 in 2021 to 163 in 2022, a 21.3% reduction. Despite the overall drop in collisions, the number of people injured rose by 38.8%, from 49 to 68. The number of fatalities also increased from one in the prior period to two in the current period.

163

▼ -21.3%was 207

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

68

▲ 38.8%was 49

Persons Injured

2

▲ 100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 crash volume in Cherokee County shows a downward trend, with 44 fewer incidents in 2022 compared to 2021, representing a 21.3% decrease. However, this trend did not extend to crash severity, as total injuries increased from 49 to 68, and fatalities rose from one to two over the same period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Cyclists Killed

Prior: 0→%

1

Motorists Killed

Prior: 1→0.0%

1

Pedestrians Injured

Prior: 0→%

0

Cyclists Injured

Prior: 0→0.0%

67

Motorists Injured

Prior: 49↑36.7%

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 peak hour for crashes was consistent year-over-year, occurring in the 5 p.m. hour for both 2021 (23 crashes) and 2022 (18 crashes). However, the peak day for collisions shifted from Friday (37 crashes) in 2021 to Wednesday (29 crashes) in 2022. Crash distribution across the week was more varied in 2022, with Monday and Wednesday being the most frequent days, unlike the prior year's concentration on Thursday and Friday.

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

Crash severity worsened in 2022 compared to 2021. The number of fatal crashes doubled from one to two, and the fatal crash rate increased from 0.48 to 1.23 per 100 crashes. The proportion of crashes resulting in any injury grew from 19.8% of all crashes in 2021 to 31.3% in 2022. Correspondingly, the share of crashes with no reported injuries fell from 79.7% to 67.5%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.2%
↑100.0%prior 1
Serious Injury11serious injury crashes6.7%
↑83.3%prior 6
Minor Injury28minor injury crashes17.2%
↑27.3%prior 22
Possible Injury12possible injury crashes7.4%
↓-7.7%prior 13
No Injury110no injury crashes67.5%
↓-33.3%prior 165

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 with animals remained the leading contributing factor in both periods, though the count of such incidents decreased by 30% from 70 in 2021 to 49 in 2022. "Lost Control" became a more prominent factor, with its crash count increasing from 7 to 16, moving it to the second-ranked cause in 2022. Conversely, factors like "Followed too close" (from 16 to 6 crashes) and "Failure to yield from a stop sign" (from 13 to 5 crashes) saw significant decreases in their counts.

Officer-Reported Primary Contributing Cause

Animal49 (30.1%)↓-30.0%prior 70
Lost Control16 (9.8%)↑128.6%prior 7
Driving too fast for conditions12 (7.4%)
Ran off road - straight10 (6.1%)↓-16.7%prior 12
Other (explain in narrative): Other9 (5.5%)→0.0%prior 9
Ran off road - left7 (4.3%)↓-36.4%prior 11
Followed too close6 (3.7%)↓-62.5%prior 16
FTYROW: Making left turn6 (3.7%)↓-14.3%prior 7
FTYROW: From stop sign5 (3.1%)↓-61.5%prior 13
Ran Stop Sign4 (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

The proportion of crashes occurring in clear weather and daylight conditions remained stable year-over-year at approximately 55% and 50%, respectively. However, there was a notable shift in crashes related to adverse road surfaces. The share of crashes on dry roads decreased from 52.2% in 2021 to 46.0% in 2022. Concurrently, crashes on icy or frosty roads increased from 3 to 13, and those on snowy roads rose from 10 to 14, indicating a greater impact of winter conditions in 2022.

Weather

Clear90 (73.8%)
↓-20.4%prior 113
Snow9 (7.4%)
↑80.0%prior 5
Cloudy9 (7.4%)
↓-47.1%prior 17
Freezing rain/drizzle5 (4.1%)
Rain3 (2.5%)
↓-62.5%prior 8
Fog, smoke, smog2 (1.6%)
Blowing Snow2 (1.6%)
Severe Winds1 (0.8%)
↓-80.0%prior 5
Sleet, hail1 (0.8%)

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

Lighting

Daylight83 (67.5%)
↓-20.2%prior 104
Dark - roadway not lighted22 (17.9%)
↓-24.1%prior 29
Dark - roadway lighted10 (8.1%)
↓-9.1%prior 11
Dawn5 (4.1%)
Dusk3 (2.4%)

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

Road Surface

Dry75 (60.5%)
↓-30.6%prior 108
Snow14 (11.3%)
↑40.0%prior 10
Gravel13 (10.5%)
↑18.2%prior 11
Ice/frost13 (10.5%)
Wet4 (3.2%)
↓-76.5%prior 17
Slush3 (2.4%)
Other (explain in narrative)1 (0.8%)
Mud, dirt1 (0.8%)

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

Vehicles & Demographics

Ford and Chevrolet-affiliated makes (Chev, GMC) were the most frequently involved vehicles in crashes in both 2021 and 2022, though their total counts decreased in line with the overall trend. In terms of persons involved, the 26-34 and 35-44 age groups were highly represented in both years. Notably, the number of persons aged 65 and older involved in crashes increased from 48 to 53, making it one of the top two most-represented age groups in 2022.

Top Vehicle Makes (231 vehicles)

1
FORD38 (16.5%)
↓-22.4%prior 49
2
CHEV35 (15.2%)
↓-20.5%prior 44
3
DODG14 (6.1%)
↑16.7%prior 12
4
BUIC14 (6.1%)
↑7.7%prior 13
5
GMC12 (5.2%)
↓-62.5%prior 32
6
CHEVROLET10 (4.3%)
↓-41.2%prior 17
7
JEEP8 (3.5%)
↑60.0%prior 5
8
RAM7 (3%)
9
HOND7 (3%)
10
PETERBILT7 (3%)

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

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

Sex Distribution (212 persons with recorded sex)

Male126 (59.4%)
↓-10.0%prior 140
Female86 (40.6%)
↓-14.0%prior 100

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: 163
  • Total persons involved: 358
  • Total vehicles involved: 231

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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