Yearly Traffic Safety Analysis

185 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In Cherokee County, total traffic crashes increased by 13.5% in 2023, rising from 163 incidents in 2022 to 185. Despite the growth in total collisions, the number of fatalities decreased from two to one, and total injuries declined from 68 to 63. The most significant shift was an increase in the number of serious injury crashes, which rose from 11 in 2022 to 16 in 2023.

185

▲ 13.5%was 163

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

63

▼ -7.4%was 68

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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend shows an increase in crash volume in Cherokee County. Total collisions rose from 163 in 2022 to 185 in 2023, representing a 13.5% year-over-year increase. However, this rise in crashes was accompanied by a slight decrease in both fatalities and total injuries.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

62

Motorists Injured

Prior: 67↓-7.5%

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · 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 minor shifts between the two periods. The peak day for collisions moved from Wednesday in 2022 (29 crashes) to Tuesday in 2023 (36 crashes). The peak hour for crashes shifted slightly later, from 5 p.m. in the prior year (18 crashes) to 6 p.m. in the current year (17 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes increased, the severity profile shifted. The number of fatal crashes decreased from two in 2022 to one in 2023, causing the fatal crash rate to fall from 1.23 to 0.54 per 100 crashes. Conversely, crashes resulting in serious injuries increased from 11 to 16. The proportion of crashes with no reported injuries grew from 67.5% in 2022 to 74.6% in 2023.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
↓-50.0%prior 2
Serious Injury16serious injury crashes8.6%
↑45.5%prior 11
Minor Injury19minor injury crashes10.3%
↓-32.1%prior 28
Possible Injury11possible injury crashes5.9%
↓-8.3%prior 12
No Injury138no injury crashes74.6%
↑25.5%prior 110

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in both periods, with counts increasing from 49 in 2022 to 51 in 2023. 'Lost Control' was the second-most cited factor in both years, rising from 16 to 17 incidents. A notable change was observed in crashes attributed to 'Ran Stop Sign', where the count more than doubled from 4 in the prior year to 9 in the current year.

Officer-Reported Primary Contributing Cause

Animal51 (27.6%)↑4.1%prior 49
Lost Control17 (9.2%)↑6.3%prior 16
Other (explain in narrative): Other15 (8.1%)↑66.7%prior 9
Driving too fast for conditions11 (5.9%)↓-8.3%prior 12
Ran Stop Sign9 (4.9%)
FTYROW: From stop sign7 (3.8%)↑40.0%prior 5
FTYROW: At uncontrolled intersection7 (3.8%)
Ran off road - straight6 (3.2%)↓-40.0%prior 10
FTYROW: Making left turn6 (3.2%)→0.0%prior 6
Ran off road - left6 (3.2%)↓-14.3%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The majority of crashes in both years occurred during daylight hours in clear weather. The number of crashes on dry road surfaces increased from 75 in 2022 to 101 in 2023, consistent with the overall rise in collisions. Crashes under adverse road conditions, such as snow or ice, saw a combined decrease from 27 incidents in the prior year to 20 in the current year.

Weather

Clear103 (73.6%)
↑14.4%prior 90
Cloudy14 (10.0%)
↑55.6%prior 9
Snow10 (7.1%)
↑11.1%prior 9
Rain7 (5.0%)
Freezing rain/drizzle3 (2.1%)
↓-40.0%prior 5
Fog, smoke, smog3 (2.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight106 (75.2%)
↑27.7%prior 83
Dark - roadway not lighted20 (14.2%)
↓-9.1%prior 22
Dark - roadway lighted8 (5.7%)
↓-20.0%prior 10
Dawn5 (3.5%)
→0.0%prior 5
Dark - unknown roadway lighting2 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry101 (72.1%)
↑34.7%prior 75
Snow12 (8.6%)
↓-14.3%prior 14
Wet11 (7.9%)
Ice/frost8 (5.7%)
↓-38.5%prior 13
Gravel6 (4.3%)
↓-53.8%prior 13
Slush2 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

Chevrolet and Ford were the most common vehicle makes involved in crashes in both years; the count for Chevrolet vehicles rose from 45 to 58 year-over-year. In terms of persons involved, the 65+ age group saw an increase from 53 individuals in 2022 to 67 in 2023. The 21-25 age group also grew from 29 to 42 individuals, while the number of persons aged 16-20 involved in crashes declined from 48 to 40.

Top Vehicle Makes (275 vehicles)

1
FORD42 (15.3%)
↑10.5%prior 38
2
CHEV39 (14.2%)
↑11.4%prior 35
3
CHEVROLET19 (6.9%)
↑90.0%prior 10
4
DODG17 (6.2%)
↑21.4%prior 14
5
TOYT12 (4.4%)
↑71.4%prior 7
6
GMC12 (4.4%)
→0.0%prior 12
7
BUICK7 (2.5%)
8
BUIC7 (2.5%)
↓-50.0%prior 14
9
JEEP7 (2.5%)
↓-12.5%prior 8
10
NISS7 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (252 persons with recorded sex)

Male155 (61.5%)
↑23.0%prior 126
Female97 (38.5%)
↑12.8%prior 86

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 185
  • Total persons involved: 399
  • Total vehicles involved: 275

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: 2023." Published September 9, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2023-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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