Yearly Traffic Safety Analysis

85 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Osceola County, total crashes decreased from 99 in 2019 to 85 in 2020, a 14.1% reduction. During this period, fatalities fell from two to one, while total injuries remained nearly unchanged at 41. A notable year-over-year shift was a significant increase in crashes involving driving under the influence, which rose from 3 to 11.

85

▼ -14.1%was 99

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

41

▲ 2.5%was 40

Persons Injured

1

▼ -50.0%was 2

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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic collisions in Osceola County showed a downward trend, with total crashes decreasing by 14.1% from 99 in the prior year to 85 in the current year. This decline was accompanied by a reduction in fatalities from two to one, although the number of people injured remained stable, increasing slightly from 40 to 41.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 2↓-50.0%

41

Motorists Injured

Prior: 40↑2.5%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal crash patterns shifted significantly between the two periods. The peak day for crashes moved from Monday (20 crashes) in the prior year to Saturday (20 crashes) in the current year. The peak hour for collisions also shifted from the 5 p.m. evening commute hour (11 crashes) to the 11 p.m. hour (10 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The number of fatal crashes was halved, decreasing from two in the prior period to one in the current period, with the fatal crash rate falling from 2.0% to 1.2%. While total injuries remained nearly constant (40 versus 41), the number of serious injury crashes increased from two to three. The count of crashes resulting in no injury decreased from 71 to 55.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.2%
↓-50.0%prior 2
Serious Injury3serious injury crashes3.5%
↑50.0%prior 2
Minor Injury16minor injury crashes18.8%
↑6.7%prior 15
Possible Injury10possible injury crashes11.8%
↑11.1%prior 9
No Injury55no injury crashes64.7%
↓-22.5%prior 71

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both periods, with counts of 24 and 20, respectively. The most significant change was in crashes due to 'Driving too fast for conditions,' which saw its count increase by 160% from 5 to 13, making it the second-ranked factor in the current period. Conversely, 'Ran off road - straight' crashes decreased from 10 incidents to 2.

Officer-Reported Primary Contributing Cause

Animal20 (23.5%)↓-16.7%prior 24
Driving too fast for conditions13 (15.3%)↑160.0%prior 5
Lost Control9 (10.6%)↑80.0%prior 5
Operating vehicle in an reckless, erratic, careless, negligent manner6 (7.1%)
Driver Distraction: Inattentive/lost in thought5 (5.9%)
Swerving/Evasive Action5 (5.9%)
Other (explain in narrative): Vision obstructed4 (4.7%)
Driver Distraction: Other interior distraction3 (3.5%)
Ran off road - left2 (2.4%)
FTYROW: From parked position2 (2.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes on dry roads increased from 37.4% to 47.1% year-over-year, while crashes on icy or snowy surfaces decreased in count from 28 to 20. There was a notable shift in lighting, with crashes in daylight conditions decreasing from 53 to 34, and crashes in dark, lighted conditions increasing from 2 to 11. Crashes attributed to 'Blowing Snow' also saw an increase, from one incident to eight.

Weather

Clear45 (65.2%)
↓-18.2%prior 55
Cloudy8 (11.6%)
→0.0%prior 8
Blowing Snow8 (11.6%)
Snow4 (5.8%)
Freezing rain/drizzle1 (1.4%)
Rain1 (1.4%)
Severe Winds1 (1.4%)
Fog, smoke, smog1 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight34 (49.3%)
↓-35.8%prior 53
Dark - roadway not lighted20 (29.0%)
↑5.3%prior 19
Dark - roadway lighted11 (15.9%)
Dusk3 (4.3%)
Dawn1 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry40 (58.0%)
↑8.1%prior 37
Ice/frost10 (14.5%)
↓-41.2%prior 17
Snow8 (11.6%)
↓-20.0%prior 10
Gravel4 (5.8%)
Slush2 (2.9%)
Mud, dirt2 (2.9%)
Wet2 (2.9%)
↓-77.8%prior 9
Water (standing or moving)1 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet remained the most frequently involved vehicle makes in both periods, with relatively stable counts. An analysis of persons involved shows that while the total number of people in crashes decreased from 215 to 179, the number of individuals in the 16-20 and 21-25 age groups saw slight increases in their raw counts. The 65+ age group saw a significant drop in involvement, from 29 individuals to 11.

Top Vehicle Makes (112 vehicles)

1
FORD25 (22.3%)
↓-13.8%prior 29
2
CHEVROLET13 (11.6%)
↑30.0%prior 10
3
CHEV12 (10.7%)
↓-14.3%prior 14
4
DODG7 (6.3%)
↓-12.5%prior 8
5
TOYOTA5 (4.5%)
6
DODGE5 (4.5%)
7
GMC4 (3.6%)
8
HONDA3 (2.7%)
9
FREIGHTLINER3 (2.7%)
10
CHRYSLER3 (2.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

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

Sex Distribution (103 persons with recorded sex)

Male72 (69.9%)
↓-6.5%prior 77
Female31 (30.1%)
↓-43.6%prior 55

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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: 2020-01-01 through 2020-12-31
  • Report generated: September 10, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 85
  • Total persons involved: 179
  • Total vehicles involved: 112

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: 2020." Published September 10, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2020-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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