Yearly Traffic Safety Analysis

76 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In 2021, Osceola County recorded 76 total crashes, a 10.6% decrease from the 85 crashes reported in 2020. While overall collisions declined, the most significant year-over-year change was a 54.5% reduction in crashes involving a driver under the influence, which fell from 11 in 2020 to 5 in 2021.

76

▼ -10.6%was 85

Total Crash Events

1

Persons Killed

30

▼ -26.8%was 41

Persons Injured

1

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 · 2021-01-01 to 2021-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Osceola County showed a downward trend from 2020 to 2021. Total crashes decreased by 10.6%, from 85 to 76. The number of people injured in these incidents also fell by 26.8%, from 41 to 30, while the number of fatalities remained unchanged at one death in each period.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Motorists Killed

Prior: 1↓-100.0%

0

Pedestrians Injured

Prior: 0→0.0%

30

Motorists Injured

Prior: 41↓-26.8%

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · 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 2021, the peak day for crashes was Friday with 21 incidents, a change from Saturday (20 incidents) in the prior year. The peak hour for collisions moved significantly earlier, from 11 p.m. in 2020 (10 crashes) to 6 p.m. in 2021 (7 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the number of fatal crashes remained constant at one in both 2021 and 2020, the fatal crash rate increased slightly from 1.18% to 1.32% of all crashes. The proportion of crashes resulting in minor injuries saw a notable decrease, dropping from 18.8% of all crashes in 2020 (16 incidents) to 11.8% in 2021 (9 incidents). Correspondingly, the share of non-injury crashes rose from 64.7% to 71.1% over the same period.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.3%
→0.0%prior 1
Serious Injury3serious injury crashes3.9%
→0.0%prior 3
Minor Injury9minor injury crashes11.8%
↓-43.8%prior 16
Possible Injury9possible injury crashes11.8%
↓-10.0%prior 10
No Injury54no injury crashes71.1%
↓-1.8%prior 55

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both periods, with the count increasing from 20 crashes in 2020 to 23 in 2021. Incidents attributed to 'Driving too fast for conditions' saw a significant reduction, falling by 61.5% from 13 crashes to 5. Conversely, crashes involving 'Driver Distraction: Other interior distraction' doubled in count, from 3 to 6, becoming the second-most cited factor in 2021.

Officer-Reported Primary Contributing Cause

Animal23 (30.3%)↑15.0%prior 20
Driver Distraction: Other interior distraction6 (7.9%)
Lost Control6 (7.9%)↓-33.3%prior 9
Driving too fast for conditions5 (6.6%)↓-61.5%prior 13
FTYROW: From stop sign5 (6.6%)
Ran off road - straight5 (6.6%)
Driver Distraction: Inattentive/lost in thought2 (2.6%)↓-60.0%prior 5
Exceeded authorized speed2 (2.6%)
FTYROW: At uncontrolled intersection2 (2.6%)
FTYROW: From driveway2 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While clear weather and dry roads accounted for the majority of crashes in both years, there was a slight shift toward more challenging conditions in 2021. The proportion of crashes occurring in darkness (lighted or unlighted) increased from 36.5% in 2020 to 38.2% in 2021. Similarly, the share of crashes on non-dry road surfaces like snow, ice, or wet pavement rose from 34.1% to 35.5% year-over-year.

Weather

Clear37 (60.7%)
↓-17.8%prior 45
Cloudy6 (9.8%)
↓-25.0%prior 8
Snow5 (8.2%)
Blowing Snow4 (6.6%)
↓-50.0%prior 8
Rain4 (6.6%)
Severe Winds2 (3.3%)
Freezing rain/drizzle2 (3.3%)
Fog, smoke, smog1 (1.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Weather condition at time of crash

Lighting

Daylight31 (50.0%)
↓-8.8%prior 34
Dark - roadway not lighted23 (37.1%)
↑15.0%prior 20
Dark - roadway lighted6 (9.7%)
↓-45.5%prior 11
Dawn1 (1.6%)
Dusk1 (1.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Lighting condition field

Road Surface

Dry34 (54.8%)
↓-15.0%prior 40
Snow12 (19.4%)
↑50.0%prior 8
Wet7 (11.3%)
Ice/frost5 (8.1%)
↓-50.0%prior 10
Gravel3 (4.8%)
Other (explain in narrative)1 (1.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes saw some changes between periods. While Chevrolet and Ford remained the top two makes, the number of Ford vehicles involved decreased from 25 in 2020 to 15 in 2021. The age demographics of persons involved also shifted, with a notable decrease in the 16-20 and 21-25 age groups, whose involvement fell by 50% and 57% respectively. Conversely, the 35-44 age group saw a 31.6% increase in involvement, from 19 persons in 2020 to 25 in 2021.

Top Vehicle Makes (102 vehicles)

1
FORD15 (14.7%)
↓-40.0%prior 25
2
CHEV14 (13.7%)
↑16.7%prior 12
3
CHEVROLET10 (9.8%)
↓-23.1%prior 13
4
GMC9 (8.8%)
5
HONDA6 (5.9%)
6
DODGE3 (2.9%)
↓-40.0%prior 5
7
CHRY3 (2.9%)
8
HOND3 (2.9%)
9
PONTIAC2 (2%)
10
TOYOTA2 (2%)
↓-60.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Vehicle unit records

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

Sex Distribution (76 persons with recorded sex)

Male44 (57.9%)
↓-38.9%prior 72
Female32 (42.1%)
↑3.2%prior 31

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · 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: 2021-01-01 through 2021-12-31
  • Report generated: September 10, 2026

Data Coverage

  • Reporting period: 2021-01-01 through 2021-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 76
  • Total persons involved: 134
  • 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: 2021." Published September 10, 2026. Reporting period: 2021-01-01 to 2021-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2021-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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