Yearly Traffic Safety Analysis

238 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Clay County recorded 238 total crashes, a 22.7% decrease from the 308 crashes reported in 2024. Despite the overall reduction in collisions, the number of fatalities increased from 2 to 3 year-over-year. The total number of injuries remained nearly stable, with 93 in 2025 compared to 94 in the prior year.

238

▼ -22.7%was 308

Total Crash Events

3

▲ 50.0%was 2

Persons Killed

93

▼ -1.1%was 94

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (3) 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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in Clay County shows a significant decrease in traffic crashes year-over-year. Total collisions fell by 22.7%, from 308 in 2024 to 238 in 2025. However, this positive trend in crash volume did not extend to crash severity, as fatalities increased from 2 to 3, while total injuries saw a negligible decrease from 94 to 93.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 2↑50.0%

1

Pedestrians Injured

Prior: 3↓-66.7%

4

Cyclists Injured

Prior: 2↑100.0%

88

Motorists Injured

Prior: 89↓-1.1%

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted between the two periods. In 2025, the highest crash volumes occurred on Friday and Saturday (41 crashes each), a change from 2024 when Wednesday was the peak day with 58 crashes. Similarly, the peak hour for collisions moved from 6 p.m. in 2024 (28 crashes) to 3 p.m. in 2025 (24 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the total number of crashes declined, the severity of crashes increased proportionally in 2025. The share of fatal crashes rose from 0.6% to 0.8% of all incidents, and the share of serious injury crashes increased significantly from 1.9% to 3.8%. This shift is also reflected in the decrease in the proportion of no-injury crashes, which fell from 74.7% of all crashes in 2024 to 68.9% in 2025.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.8%
→0.0%prior 2
Serious Injury9serious injury crashes3.8%
↑50.0%prior 6
Minor Injury28minor injury crashes11.8%
↓-20.0%prior 35
Possible Injury35possible injury crashes14.7%
→0.0%prior 35
No Injury164no injury crashes68.9%
↓-28.7%prior 230

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the top contributing factor in both years, though the count dropped from 61 in 2024 to 39 in 2025. 'Followed too close' moved up in the rankings, with incidents increasing from 11 to 19. Conversely, crashes attributed to 'Driving too fast for conditions' decreased from 22 to 12. 'Ran Stop Sign' also saw an increase in count, rising from 14 incidents in the prior year to 18 in the current year.

Officer-Reported Primary Contributing Cause

Animal39 (16.4%)↓-36.1%prior 61
Followed too close19 (8%)↑72.7%prior 11
Ran Stop Sign18 (7.6%)↑28.6%prior 14
FTYROW: From stop sign14 (5.9%)↓-30.0%prior 20
Other (explain in narrative): Other13 (5.5%)↓-65.8%prior 38
Ran off road - left12 (5%)↑20.0%prior 10
Driving too fast for conditions12 (5%)↓-45.5%prior 22
FTYROW: Making left turn9 (3.8%)↓-25.0%prior 12
FTYROW: From driveway8 (3.4%)↑14.3%prior 7
FTYROW: From yield sign8 (3.4%)↑14.3%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions remained largely consistent between 2024 and 2025. In both periods, the majority of incidents occurred during daylight hours, on dry road surfaces, and in clear weather. For example, crashes in daylight accounted for 64.3% of the total in 2025, compared to 62.7% in 2024. There was no significant year-over-year shift in the proportion of crashes occurring under adverse weather, lighting, or road surface conditions.

Weather

Clear147 (74.2%)
↓-21.0%prior 186
Cloudy30 (15.2%)
↓-18.9%prior 37
Rain6 (3.0%)
↓-33.3%prior 9
Snow5 (2.5%)
↓-28.6%prior 7
Blowing Snow4 (2.0%)
Severe Winds3 (1.5%)
Other (explain in narrative)1 (0.5%)
Freezing rain/drizzle1 (0.5%)
Fog, smoke, smog1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight153 (76.1%)
↓-20.7%prior 193
Dark - roadway not lighted20 (10.0%)
↓-23.1%prior 26
Dark - roadway lighted18 (9.0%)
↓-14.3%prior 21
Dusk4 (2.0%)
↓-33.3%prior 6
Dawn3 (1.5%)
↓-40.0%prior 5
Dark - unknown roadway lighting3 (1.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry144 (72.0%)
↓-20.4%prior 181
Snow20 (10.0%)
↑25.0%prior 16
Wet16 (8.0%)
↓-33.3%prior 24
Ice/frost8 (4.0%)
↓-50.0%prior 16
Gravel8 (4.0%)
↓-33.3%prior 12
Slush3 (1.5%)
Other (explain in narrative)1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford and Chevrolet accounting for the highest numbers in both years. In 2025, Chevrolet-badged vehicles (CHEV/CHEVROLET) were involved in 98 crashes, while Ford was involved in 58, a reversal from 2024 when Ford led with 96 vehicles to Chevrolet's 85. Regarding driver and passenger demographics, the 65+ age group saw a notable decrease in involvement from 112 persons in 2024 to 73 in 2025. Conversely, involvement for the 16-20 age group increased from 56 to 64 persons.

Top Vehicle Makes (401 vehicles)

1
CHEV76 (19%)
↓-10.6%prior 85
2
FORD58 (14.5%)
↓-39.6%prior 96
3
CHEVROLET22 (5.5%)
↑10.0%prior 20
4
JEEP22 (5.5%)
↓-8.3%prior 24
5
GMC19 (4.7%)
↑26.7%prior 15
6
DODG19 (4.7%)
↓-17.4%prior 23
7
BUIC13 (3.2%)
↓-48.0%prior 25
8
RAM12 (3%)
↓-25.0%prior 16
9
TOYO12 (3%)
↓-29.4%prior 17
10
CHRY11 (2.7%)
↓-47.6%prior 21

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

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

Sex Distribution (256 persons with recorded sex)

Male144 (56.3%)
↓-21.3%prior 183
Female112 (43.8%)
↓-12.5%prior 128

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 238
  • Total persons involved: 424
  • Total vehicles involved: 401

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: 2025." Published September 9, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2025-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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