Yearly Traffic Safety Analysis

85 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Butler County recorded 85 total vehicle crashes, a 10.5% decrease from the 95 crashes reported in 2024. The most significant year-over-year change was the reduction in crash severity. Fatalities dropped from 2 in 2024 to 0 in 2025, and total injuries decreased by 44.7%, from 38 to 21.

85

▼ -10.5%was 95

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

21

▼ -44.7%was 38

Persons Injured

0

▼ -100.0%was 2

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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

Traffic crashes in Butler County showed a downward trend year-over-year. The total number of crashes fell from 95 to 85. This was accompanied by a notable improvement in outcomes, as fatalities were eliminated and the number of injuries fell from 38 to 21.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 2↓-100.0%

21

Motorists Injured

Prior: 38↓-44.7%

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 showed some consistency and some shifts between the two periods. Monday remained the peak day for crashes in both 2025 and 2024, with an identical count of 19 incidents. However, the peak hour for crashes shifted; while 5 PM was a joint peak hour in 2024 with 12 crashes, it became the single most frequent time in 2025 with 14 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

Crash severity significantly decreased in 2025 compared to the prior year. There were no fatal crashes in 2025, down from 2 fatal crashes in 2024. The number of serious injury crashes also fell from 5 to 2, and possible injury crashes decreased from 14 to 9. Consequently, the proportion of crashes resulting in no injuries increased from 68.4% in 2024 to 77.6% in 2025.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes2.4%
↓-60.0%prior 5
Minor Injury8minor injury crashes9.4%
↓-11.1%prior 9
Possible Injury9possible injury crashes10.6%
↓-35.7%prior 14
No Injury66no injury crashes77.6%
↑1.5%prior 65

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 with animals remained the leading contributing factor in both periods, with the count increasing slightly from 31 in 2024 to 32 in 2025. The number of crashes attributed to losing control more than doubled, increasing from 3 to 7 incidents. Conversely, crashes involving failure to yield the right-of-way while making a left turn decreased from 5 in 2024 to 2 in 2025.

Officer-Reported Primary Contributing Cause

Animal32 (37.6%)↑3.2%prior 31
Lost Control7 (8.2%)
FTYROW: From stop sign7 (8.2%)→0.0%prior 7
Ran off road - straight6 (7.1%)
Other (explain in narrative): Other5 (5.9%)
Driving too fast for conditions5 (5.9%)
Ran off road - left3 (3.5%)
Driver Distraction: Reaching for object(s)/fallen object(s)2 (2.4%)
Made improper turn2 (2.4%)
FTYROW: Making left turn2 (2.4%)↓-60.0%prior 5

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

Road & Environmental Conditions

While total crashes decreased, there were shifts in the conditions under which they occurred. Crashes in daylight decreased from 51 to 36, while those at dusk increased from 1 to 7. Collisions in clear weather fell from 50 to 42, but crashes on cloudy days rose from 6 to 10. Crashes on roads with snow or ice decreased from a combined 8 incidents in 2024 to 5 in 2025.

Weather

Clear42 (75.0%)
↓-16.0%prior 50
Cloudy10 (17.9%)
↑66.7%prior 6
Snow1 (1.8%)
↓-80.0%prior 5
Freezing rain/drizzle1 (1.8%)
Blowing Snow1 (1.8%)
Severe Winds1 (1.8%)

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

Lighting

Daylight36 (64.3%)
↓-29.4%prior 51
Dark - roadway not lighted9 (16.1%)
↑50.0%prior 6
Dusk7 (12.5%)
Dark - roadway lighted3 (5.4%)
↓-62.5%prior 8
Dawn1 (1.8%)

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

Road Surface

Dry43 (76.8%)
↓-8.5%prior 47
Gravel4 (7.1%)
↓-42.9%prior 7
Snow4 (7.1%)
↓-20.0%prior 5
Slush2 (3.6%)
Wet2 (3.6%)
↓-60.0%prior 5
Ice/frost1 (1.8%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes showed some changes year-over-year. The number of Chevrolet vehicles involved increased from 27 to 36, while Ford vehicles remained relatively stable, decreasing from 24 to 23. Regarding driver age, there was an increase in crash involvement for the 16-20 age group, from 18 individuals in 2024 to 23 in 2025. In contrast, involvement for the 55-64 age group saw a notable decrease from 23 to 14 persons.

Top Vehicle Makes (119 vehicles)

1
CHEV32 (26.9%)
↑45.5%prior 22
2
FORD23 (19.3%)
↓-4.2%prior 24
3
DODG6 (5%)
→0.0%prior 6
4
GMC6 (5%)
→0.0%prior 6
5
RAM5 (4.2%)
6
HOND4 (3.4%)
↓-20.0%prior 5
7
CHEVROLET4 (3.4%)
↓-20.0%prior 5
8
NISS3 (2.5%)
↓-50.0%prior 6
9
SUBA3 (2.5%)
10
CHRYSLER2 (1.7%)

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

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

Sex Distribution (66 persons with recorded sex)

Male54 (81.8%)
↑22.7%prior 44
Female12 (18.2%)
↓-52.0%prior 25

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: 85
  • Total persons involved: 122
  • Total vehicles involved: 119

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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