Yearly Traffic Safety Analysis

85 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In Osceola County, total crashes rose from 61 in 2022 to 85 in 2023, a 39.3% increase. The number of people injured also grew significantly, rising 65.2% from 23 to 38, while fatalities remained unchanged at two. The most substantial change in crash causation was a 66.7% increase in collisions involving animals, which rose from 15 to 25 incidents.

85

▲ 39.3%was 61

Total Crash Events

2

Persons Killed

38

▲ 65.2%was 23

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (2) 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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, Osceola County experienced a significant upward trend in traffic collisions, with total crashes increasing by 39.3% from 61 in 2022 to 85 in 2023. This increase was accompanied by a 65.2% rise in injuries, from 23 to 38. The number of fatalities, however, held steady at two for both years.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 2→0.0%

38

Motorists Injured

Prior: 23↑65.2%

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted notably between the two periods. In 2023, the peak days for crashes were Sunday and Monday, with 15 incidents each, a change from 2022's peak on Wednesday which saw 12 crashes. The peak hour also moved from 9 p.m. (9 crashes) in the prior year to the afternoon at 3 p.m. (13 crashes) in the current year.

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the total number of fatal crashes decreased from two in 2022 to one in 2023, the number of serious injury crashes saw a notable increase from one to five. Crashes resulting in minor injuries also rose from six to 11. Despite the rise in total collisions, the proportion of non-injury crashes remained relatively stable, accounting for 69.4% of incidents in 2023 compared to 67.2% in 2022.

Severity is per crash event (most severe injury). 1 fatal crash events resulted in 2 persons killed.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.2%
↓-50.0%prior 2
Serious Injury5serious injury crashes5.9%
↑400.0%prior 1
Minor Injury11minor injury crashes12.9%
↑83.3%prior 6
Possible Injury9possible injury crashes10.6%
↓-18.2%prior 11
No Injury59no injury crashes69.4%
↑43.9%prior 41

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals were the leading contributing factor in both years, with the count increasing by 66.7% from 15 incidents in 2022 to 25 in 2023. "Lost Control" remained a significant factor, with its count changing slightly from nine to eight crashes. Conversely, crashes attributed to failure to yield from a stop sign decreased by 50%, from eight incidents in 2022 to four in 2023.

Officer-Reported Primary Contributing Cause

Animal25 (29.4%)↑66.7%prior 15
Lost Control8 (9.4%)↓-11.1%prior 9
Driving too fast for conditions5 (5.9%)
Ran off road - left5 (5.9%)
Ran off road - straight4 (4.7%)
FTYROW: At uncontrolled intersection4 (4.7%)
Ran Stop Sign4 (4.7%)
FTYROW: From stop sign4 (4.7%)↓-50.0%prior 8
Driver Distraction: Other interior distraction4 (4.7%)
Other (explain in narrative): Other3 (3.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurred more frequently under adverse conditions in 2023 compared to 2022. Incidents on non-dry road surfaces like wet, snow, or ice increased from 15 to 22, and crashes during adverse weather such as rain or snow rose from 5 to 12. Crashes in daylight increased in count from 30 to 47, though their share of the total remained proportionally similar at approximately 55% in 2023 versus 49% in 2022.

Weather

Clear41 (66.1%)
↑7.9%prior 38
Cloudy6 (9.7%)
↓-14.3%prior 7
Freezing rain/drizzle4 (6.5%)
Blowing Snow3 (4.8%)
Rain3 (4.8%)
Snow2 (3.2%)
Fog, smoke, smog2 (3.2%)
Severe Winds1 (1.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight47 (73.4%)
↑56.7%prior 30
Dark - roadway not lighted13 (20.3%)
→0.0%prior 13
Dark - roadway lighted2 (3.1%)
Dawn1 (1.6%)
Dusk1 (1.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry38 (59.4%)
↑18.8%prior 32
Wet10 (15.6%)
Snow8 (12.5%)
↑14.3%prior 7
Gravel4 (6.3%)
Ice/frost4 (6.3%)
↓-20.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes remained relatively consistent, with Ford and Chevrolet being the top two in both periods. Ford-made vehicles involved in crashes increased from 18 to 25, while the combined count for Chevrolet vehicles decreased slightly from 23 to 21. Analysis of persons involved shows a significant increase in the 16-20 age group (from 9 to 23 people) and the 45-54 age group (from 17 to 36 people).

Top Vehicle Makes (121 vehicles)

1
FORD25 (20.7%)
↑38.9%prior 18
2
CHEV13 (10.7%)
↓-13.3%prior 15
3
CHEVROLET8 (6.6%)
→0.0%prior 8
4
GMC6 (5%)
5
DODG6 (5%)
6
BUIC5 (4.1%)
7
JEEP5 (4.1%)
8
FREIGHTLINER5 (4.1%)
↓-16.7%prior 6
9
DEER3 (2.5%)
10
SUBARU3 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (113 persons with recorded sex)

Male74 (65.5%)
↑25.4%prior 59
Female39 (34.5%)
↑77.3%prior 22

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
  • Report generated: September 10, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 85
  • Total persons involved: 188
  • Total vehicles involved: 121

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: 2023." Published September 10, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2023-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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