Yearly Traffic Safety Analysis

66 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Keokuk County, total traffic crashes decreased by 34% year-over-year, falling from 100 in the prior period to 66 in the current period. This trend was accompanied by a significant reduction in harm, with total fatalities dropping from 4 to 1 and injuries decreasing from 53 to 21. The most notable shift was the overall decline in crash volume and severity across the county.

66

▼ -34.0%was 100

Total Crash Events

1

▼ -75.0%was 4

Persons Killed

21

▼ -60.4%was 53

Persons Injured

1

▼ -75.0%was 4

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 · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Keokuk County indicates a significant downward trend year-over-year. Total crashes fell by 34%, from 100 to 66. Correspondingly, the number of people killed in crashes decreased from 4 to 1, and the number of people injured was reduced from 53 to 21.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 4↓-75.0%

21

Motorists Injured

Prior: 53↓-60.4%

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · 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 Saturday (21 crashes) in the prior year to Monday (15 crashes) in the current year. Similarly, the peak hour for crashes changed from the 7 a.m. morning hour (12 crashes) to the 9 p.m. evening hour (9 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased year-over-year. The proportion of crashes resulting in a fatality fell from 4.0% to 1.5%. Concurrently, the share of crashes with no reported injuries increased from 69% in the prior period to 77.3% in the current period, while the total count of injury-resulting crashes fell from 27 to 14.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.5%
↓-75.0%prior 4
Serious Injury4serious injury crashes6.1%
→0.0%prior 4
Minor Injury3minor injury crashes4.5%
↓-76.9%prior 13
Possible Injury7possible injury crashes10.6%
↓-30.0%prior 10
No Injury51no injury crashes77.3%
↓-26.1%prior 69

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the top contributing factor in both periods, though the count of these incidents decreased by 36% from 36 to 23. The number of crashes attributed to 'Ran off road - straight' saw a significant reduction, dropping from 11 incidents to 3. Conversely, crashes involving 'FTYROW: Making left turn' doubled in count from 2 to 4.

Officer-Reported Primary Contributing Cause

Animal23 (34.8%)↓-36.1%prior 36
FTYROW: Making left turn4 (6.1%)
FTYROW: From stop sign3 (4.5%)
Ran off road - straight3 (4.5%)↓-72.7%prior 11
Ran Stop Sign3 (4.5%)
Other (explain in narrative): Other3 (4.5%)
Followed too close2 (3%)
FTYROW: From parked position2 (3%)
Lost Control2 (3%)↓-80.0%prior 10
Operating vehicle in an reckless, erratic, careless, negligent manner2 (3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Year-over-year, a larger proportion of crashes occurred in favorable conditions. The share of crashes on dry roads increased from 45% to 56.1%, and those in clear weather grew from 38% to 51.5% of the total. Crashes on dark, unlit roadways decreased in share from 22% to 13.6% of all incidents.

Weather

Clear34 (75.6%)
↓-10.5%prior 38
Cloudy8 (17.8%)
↓-52.9%prior 17
Fog, smoke, smog3 (6.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight30 (63.8%)
↓-25.0%prior 40
Dark - roadway not lighted9 (19.1%)
↓-59.1%prior 22
Dark - roadway lighted5 (10.6%)
Dark - unknown roadway lighting2 (4.3%)
Dusk1 (2.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry37 (82.2%)
↓-17.8%prior 45
Wet3 (6.7%)
↓-75.0%prior 12
Gravel2 (4.4%)
Ice/frost2 (4.4%)
Snow1 (2.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes saw a shift; crashes involving Ford vehicles decreased from 25 to 9, while Chevrolet remained a top make in both periods. The age demographics of persons involved in crashes also changed, with the 16-20 age group's representation increasing from 13.6% of all persons involved to 19.4%. The share of most other age groups remained relatively stable.

Top Vehicle Makes (97 vehicles)

1
CHEV14 (14.4%)
↓-39.1%prior 23
2
FORD9 (9.3%)
↓-64.0%prior 25
3
GMC8 (8.2%)
→0.0%prior 8
4
CHEVROLET8 (8.2%)
↓-11.1%prior 9
5
DODGE5 (5.2%)
6
DODG5 (5.2%)
↓-28.6%prior 7
7
KIA4 (4.1%)
8
TOYT4 (4.1%)
↓-42.9%prior 7
9
JEEP3 (3.1%)
↓-40.0%prior 5
10
NISS3 (3.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (50 persons with recorded sex)

Male29 (58.0%)
↓-65.1%prior 83
Female21 (42.0%)
↓-53.3%prior 45

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 66
  • Total persons involved: 103
  • Total vehicles involved: 97

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: 2024." Published September 9, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2024-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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