Yearly Traffic Safety Analysis

72 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Keokuk County, total traffic crashes increased from 66 in 2024 to 72 in 2025, representing a 9.1% year-over-year rise. During this same period, the number of people injured in these crashes grew by 42.9%, from 21 to 30. The number of fatalities also increased, doubling from one to two.

▲ 9.1%was 66

Total Crash Events

▲ 100.0%was 1

Persons Killed

▲ 42.9%was 21

Persons Injured

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

Trend Summary

Overall traffic safety trends in Keokuk County worsened year-over-year. The total number of crashes rose by 9.1%, from 66 to 72. This increase in crashes was accompanied by a more significant rise in negative outcomes, with total injuries increasing 42.9% from 21 to 30 and fatalities increasing from 1 to 2.

Vulnerable Road User Casualties

Motorists Killed

Prior: 1↑100.0%

Motorists Injured

Prior: 21↑42.9%

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 peak day for crashes was Thursday with 19 incidents, a change from the prior year when Monday was the peak day with 15 crashes. The peak hour for collisions shifted slightly earlier to 8 p.m. with 9 crashes, compared to 9 p.m. in the previous year, which also saw 9 crashes during its peak hour.

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 increased compared to the prior year. The number of fatal crashes doubled from 1 to 2, and the fatal crash rate rose from 1.52% to 2.78%. The proportion of crashes resulting in any injury (fatal, serious, minor, or possible) increased from 21.2% in 2024 to 27.7% in 2025. This was driven by a notable increase in minor injury crashes, which tripled from 3 to 9 incidents.

Outcome by Severity (Crash Events)

Fatal2fatal crashes2.8%

↑100.0%prior 1

Serious Injury5serious injury crashes6.9%

↑25.0%prior 4

Minor Injury9minor injury crashes12.5%

↑200.0%prior 3

Possible Injury6possible injury crashes8.3%

↓-14.3%prior 7

No Injury50no injury crashes69.4%

↓-2.0%prior 51

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 from 23 in 2024 to 26 in 2025. The most significant year-over-year change was in 'Lost Control' incidents, which increased from 2 crashes to 9. Crashes attributed to 'Ran Stop Sign' also increased from 3 to 5. Conversely, crashes involving a failure to yield while making a left turn decreased from 4 to 2.

Officer-Reported Primary Contributing Cause

Animal26 (36.1%)↑13.0%prior 23

Lost Control9 (12.5%)

Driving too fast for conditions5 (6.9%)

Ran Stop Sign5 (6.9%)

Operating vehicle in an reckless, erratic, careless, negligent manner5 (6.9%)

Crossed centerline (undivided)4 (5.6%)

FTYROW: From stop sign3 (4.2%)

Cargo/equipment loss or shift2 (2.8%)

Ran off road - straight2 (2.8%)

FTYROW: Making left turn2 (2.8%)

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

Road & Environmental Conditions

Year-over-year, there was a shift in the conditions under which crashes occurred. While crashes on dry roads remained constant at 37, incidents in cloudy weather nearly doubled, rising from 8 to 15. Crashes in dark, unlighted roadway conditions also saw an increase, growing from 9 incidents in 2024 to 13 in 2025. Crashes in clear weather saw a slight decrease from 34 to 30.

Weather

Clear30 (63.8%)

↓-11.8%prior 34

Cloudy15 (31.9%)

↑87.5%prior 8

Fog, smoke, smog1 (2.1%)

Snow1 (2.1%)

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

Lighting

Daylight29 (61.7%)

↓-3.3%prior 30

Dark - roadway not lighted13 (27.7%)

↑44.4%prior 9

Dark - roadway lighted4 (8.5%)

↓-20.0%prior 5

Dawn1 (2.1%)

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

Road Surface

Dry37 (78.7%)

→0.0%prior 37

Gravel2 (4.3%)

Other (explain in narrative)2 (4.3%)

Snow2 (4.3%)

Wet2 (4.3%)

Ice/frost1 (2.1%)

Mud, dirt1 (2.1%)

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

Vehicles & Demographics

Comparing the two periods, the makes of vehicles involved in crashes showed some changes. The count of Ford vehicles doubled from 9 to 18, and Chevrolet vehicles (listed as 'CHEV') increased from 14 to 20. The age demographics of persons involved in crashes also shifted; the 35-44 age group became the most represented with 23 individuals, up from 12 in the prior year. Meanwhile, involvement of the 16-20 age group decreased from 20 to 17 persons.

Top Vehicle Makes (102 vehicles)

CHEV20 (19.6%)

↑42.9%prior 14

FORD18 (17.6%)

↑100.0%prior 9

GMC8 (7.8%)

→0.0%prior 8

NISS7 (6.9%)

CHEVROLET5 (4.9%)

↓-37.5%prior 8

DODG4 (3.9%)

↓-20.0%prior 5

JEEP4 (3.9%)

DODGE3 (2.9%)

↓-40.0%prior 5

TOYO3 (2.9%)

RAM3 (2.9%)

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

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

Sex Distribution (44 persons with recorded sex)

Male30 (68.2%)

↑3.4%prior 29

Female14 (31.8%)

↓-33.3%prior 21

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: 72
Total persons involved: 107
Total vehicles involved: 102

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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72 CRASHES INIOWA, IA2025