Yearly Traffic Safety Analysis

117 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Decatur County, total vehicle crashes increased slightly from 112 in the prior period to 117 in the current period, a 4.5% rise. While total incidents were up, the number of fatalities decreased from 4 to 3. The most notable year-over-year shift was a significant increase in crashes involving animals, which rose from 33 to 46 incidents.

117

▲ 4.5%was 112

Total Crash Events

3

▼ -25.0%was 4

Persons Killed

47

▲ 14.6%was 41

Persons Injured

2

▼ -50.0%was 4

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

Trend Summary

Overall traffic crash trends in Decatur County show a slight increase year-over-year. Total crashes rose by 4.5% from 112 to 117, and the number of people injured increased by 14.6% from 41 to 47. However, the number of fatalities decreased from 4 in the prior period to 3 in the current period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 4↓-25.0%

0

Other Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 0→%

45

Motorists Injured

Prior: 38↑18.4%

1

Other Injured

Prior: 3↓-66.7%

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. In the current year, the peak days for crashes were Thursday and Friday, each with 24 incidents. This contrasts with the prior year, when Sunday was the peak day with 21 crashes. The peak hour for collisions shifted slightly earlier, from 7 p.m. (12 crashes) in the prior period to 6 p.m. (11 crashes) in the current period.

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

The severity of crashes decreased year-over-year. The number of fatal crashes was halved, dropping from 4 in the prior period to 2 in the current period, and the fatal crash rate fell from 3.57% to 1.71%. While the number of serious injury crashes increased slightly from 6 to 7, crashes resulting in possible injury decreased from 18 to 16.

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

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.7%
↓-50.0%prior 4
Serious Injury7serious injury crashes6%
↑16.7%prior 6
Minor Injury13minor injury crashes11.1%
↑30.0%prior 10
Possible Injury16possible injury crashes13.7%
↓-11.1%prior 18
No Injury79no injury crashes67.5%
↑6.8%prior 74

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 leading contributing factor in both periods, with the count increasing by 39.4% from 33 to 46 crashes. "Lost Control" was the second most common factor in both years, with a nearly stable count of 14 in the prior period and 13 in the current. Notably, crashes attributed to "Ran Stop Sign" and "FTYROW: From stop sign" both increased from 1 incident to 5 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal46 (39.3%)↑39.4%prior 33
Lost Control13 (11.1%)↓-7.1%prior 14
Ran Stop Sign5 (4.3%)
Other (explain in narrative): Other5 (4.3%)
FTYROW: From stop sign5 (4.3%)
Ran off road - left5 (4.3%)
Ran off road - straight5 (4.3%)↓-50.0%prior 10
Driving too fast for conditions4 (3.4%)
Improper Backing3 (2.6%)
Driver Distraction: Other interior distraction3 (2.6%)

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

Road & Environmental Conditions

There was a notable shift towards more crashes occurring in adverse conditions. The number of crashes on wet roads more than doubled, increasing from 7 to 15 incidents year-over-year. Correspondingly, crashes during rain also more than doubled from 3 to 7. While daylight remained the most common lighting condition, its share of crashes decreased from 48.2% in the prior period to 37.6% in the current period.

Weather

Clear50 (64.9%)
↓-18.0%prior 61
Cloudy14 (18.2%)
→0.0%prior 14
Rain7 (9.1%)
Snow3 (3.9%)
Fog, smoke, smog2 (2.6%)
Blowing Snow1 (1.3%)

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

Lighting

Daylight44 (55.7%)
↓-18.5%prior 54
Dark - roadway not lighted21 (26.6%)
↓-8.7%prior 23
Dusk5 (6.3%)
→0.0%prior 5
Dark - unknown roadway lighting4 (5.1%)
Dawn3 (3.8%)
Dark - roadway lighted2 (2.5%)

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

Road Surface

Dry51 (66.2%)
↓-23.9%prior 67
Wet15 (19.5%)
↑114.3%prior 7
Snow4 (5.2%)
Ice/frost3 (3.9%)
Gravel2 (2.6%)
↓-66.7%prior 6
Slush2 (2.6%)

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

Vehicles & Demographics

An analysis of persons involved in crashes shows a significant decrease in younger age groups; the number of persons aged 16-20 involved in crashes fell from 36 to 14, and the 26-34 age group dropped from 41 to 26. In terms of vehicle makes, combining 'CHEV' and 'CHEVROLET' shows 44 crashes involving Chevrolet vehicles in the current period, making it the most frequent make, up from 29 in the prior period. Ford, the top make in the prior period with 24 crashes, was involved in 16 crashes in the current period.

Top Vehicle Makes (156 vehicles)

1
CHEVROLET23 (14.7%)
↑91.7%prior 12
2
CHEV21 (13.5%)
↑23.5%prior 17
3
FORD16 (10.3%)
↓-33.3%prior 24
4
GMC6 (3.8%)
↓-25.0%prior 8
5
NISS5 (3.2%)
6
TOYOTA5 (3.2%)
7
BUIC5 (3.2%)
8
HYUN5 (3.2%)
9
DODG5 (3.2%)
↓-50.0%prior 10
10
FREIGHTLINER4 (2.6%)
↓-55.6%prior 9

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

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

Sex Distribution (61 persons with recorded sex)

Male44 (72.1%)
↓-51.6%prior 91
Female17 (27.9%)
↓-69.1%prior 55

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: 117
  • Total persons involved: 165
  • Total vehicles involved: 156

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

ThatCarHitMe.com · An Injuria.ai Company