Yearly Traffic Safety Analysis

115 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Hancock County, total traffic crashes remained nearly stable, with 115 incidents in 2025 compared to 114 in 2024, an increase of less than 1%. Despite the consistent crash volume, there was a significant improvement in outcomes. The most notable year-over-year shift was a 50% reduction in fatalities, from two to one, and a 34.6% decrease in total injuries, from 52 to 34.

115

▲ 0.9%was 114

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

34

▼ -34.6%was 52

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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash volume in Hancock County was stable year-over-year, increasing by a single incident from 114 to 115. However, the severity of these crashes decreased notably. The number of fatalities was halved from two to one, and the total number of injuries fell from 52 in the prior period to 34 in the current period.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 2↓-50.0%

1

Cyclists Injured

Prior: 1→0.0%

33

Motorists Injured

Prior: 51↓-35.3%

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 timing of crashes shifted between the two periods. The peak day for collisions moved from Tuesday and Saturday (19 crashes each) in 2024 to Wednesday (25 crashes) in 2025. A similar change occurred in the peak hour, which shifted from the 7 a.m. morning hour in the prior year (10 crashes) to the 3 p.m. afternoon hour in the current year (13 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 total crashes were stable, the severity distribution changed. The number of fatal crashes decreased from two to one, and the fatal crash rate fell from 1.8% to 0.9% of all crashes. Conversely, the count of serious injury crashes more than doubled, rising from three to seven. The proportion of crashes resulting in no injuries increased from 64.0% in 2024 to 75.7% in 2025.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.9%
↓-50.0%prior 2
Serious Injury7serious injury crashes6.1%
↑133.3%prior 3
Minor Injury9minor injury crashes7.8%
↓-60.9%prior 23
Possible Injury11possible injury crashes9.6%
↓-15.4%prior 13
No Injury87no injury crashes75.7%
↑19.2%prior 73

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

The leading contributing factors for crashes showed some changes year-over-year. "Driving too fast for conditions" remained a top cause, increasing slightly from 11 incidents in 2024 to 12 in 2025. Crashes attributed to "Driver Distraction: Other interior distraction" more than doubled, with the count increasing from 4 to 9. In contrast, crashes involving "Ran off road - straight" decreased from 11 to 4, and incidents of "Ran Stop Sign" dropped from 7 to just 1.

Officer-Reported Primary Contributing Cause

Other (explain in narrative): Other15 (13%)↑200.0%prior 5
Driving too fast for conditions12 (10.4%)↑9.1%prior 11
Ran off road - left9 (7.8%)↑28.6%prior 7
Driver Distraction: Other interior distraction9 (7.8%)
Operating vehicle in an reckless, erratic, careless, negligent manner6 (5.2%)
Lost Control6 (5.2%)→0.0%prior 6
Animal5 (4.3%)↓-16.7%prior 6
Ran off road - straight4 (3.5%)↓-63.6%prior 11
Followed too close4 (3.5%)↓-20.0%prior 5
Improper Backing4 (3.5%)↓-50.0%prior 8

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

Road & Environmental Conditions

Most crashes in both periods occurred in clear weather and on dry roads, though the counts for both conditions decreased slightly. Crashes in clear weather fell from 86 to 80, and those on dry roads dropped from 81 to 71. There was a notable increase in crashes attributed to adverse road surfaces, with incidents on snow-covered roads rising from 7 to 13 and those on icy roads increasing from 9 to 12. Crashes during daylight hours increased from 70 to 76.

Weather

Clear80 (73.4%)
↓-7.0%prior 86
Snow8 (7.3%)
Cloudy8 (7.3%)
↓-11.1%prior 9
Rain4 (3.7%)
Blowing Snow4 (3.7%)
Fog, smoke, smog2 (1.8%)
Freezing rain/drizzle1 (0.9%)
Severe Winds1 (0.9%)
Blowing sand, soil, dirt1 (0.9%)

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

Lighting

Daylight76 (69.1%)
↑8.6%prior 70
Dark - roadway not lighted25 (22.7%)
↑25.0%prior 20
Dark - roadway lighted3 (2.7%)
↓-66.7%prior 9
Dawn2 (1.8%)
Dark - unknown roadway lighting2 (1.8%)
Dusk2 (1.8%)
↓-66.7%prior 6

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

Road Surface

Dry71 (65.1%)
↓-12.3%prior 81
Snow13 (11.9%)
↑85.7%prior 7
Ice/frost12 (11.0%)
↑33.3%prior 9
Wet6 (5.5%)
↑20.0%prior 5
Gravel6 (5.5%)
↓-25.0%prior 8
Slush1 (0.9%)

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 (35 vehicles) and Chevrolet (33 vehicles) leading in the current period, similar to the prior period's counts of 37 and 31, respectively. The age demographics of people involved in collisions shifted; the 65+ age group saw its involvement increase from 26 to 31 individuals, becoming the largest group in the current period. In contrast, the number of persons aged 16-20 involved in crashes decreased from 30 to 20.

Top Vehicle Makes (178 vehicles)

1
FORD35 (19.7%)
↓-5.4%prior 37
2
CHEV33 (18.5%)
↑6.5%prior 31
3
DODG13 (7.3%)
↑85.7%prior 7
4
GMC12 (6.7%)
↑9.1%prior 11
5
TOYT9 (5.1%)
6
HOND8 (4.5%)
7
RAM7 (3.9%)
8
CHEVROLET7 (3.9%)
→0.0%prior 7
9
CHRY7 (3.9%)
10
JEEP5 (2.8%)
→0.0%prior 5

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

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

Sex Distribution (119 persons with recorded sex)

Male78 (65.5%)
↑1.3%prior 77
Female41 (34.5%)
↑7.9%prior 38

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: 115
  • Total persons involved: 184
  • Total vehicles involved: 178

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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