Yearly Traffic Safety Analysis

99 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Osceola County recorded 99 total crashes, an increase of 12.5% from the 88 crashes reported in 2018. The total number of injuries rose slightly from 38 to 40 year-over-year. The most significant change was the occurrence of 2 fatal crashes resulting in 2 fatalities in 2019, whereas there were no fatalities in the prior year.

99

▲ 12.5%was 88

Total Crash Events

2

Persons Killed

40

▲ 5.3%was 38

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (2) 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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Osceola County increased from 2018 to 2019. The total number of crashes rose by 11, from 88 to 99, representing a 12.5% year-over-year increase. While total injuries saw a marginal increase from 38 to 40, the county experienced 2 fatalities in 2019 after having none in 2018.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 0→%

40

Motorists Injured

Prior: 37↑8.1%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes showed some shifts between the two periods. While the peak hour for collisions remained the 5 p.m. hour in both 2018 (10 crashes) and 2019 (11 crashes), the peak day changed. In 2018, crashes peaked on Mondays, Tuesdays, and Thursdays with 15 incidents each, whereas in 2019, Monday became the clear peak day with 20 crashes. Notably, November 2019 experienced a significant spike with 21 crashes, compared to 14 in November 2018.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased in 2019, primarily due to the introduction of fatal incidents. The county recorded 2 fatal crashes in 2019, representing 2% of all crashes, compared to none in 2018. The number of serious injury crashes decreased from 4 in 2018 to 2 in 2019, while the total number of people injured increased slightly from 38 to 40.

Outcome by Severity (Crash Events)

Fatal2fatal crashes2%
Serious Injury2serious injury crashes2%
↓-50.0%prior 4
Minor Injury15minor injury crashes15.2%
↑7.1%prior 14
Possible Injury9possible injury crashes9.1%
↓-18.2%prior 11
No Injury71no injury crashes71.7%
↑20.3%prior 59

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both years, though the count decreased slightly from 26 crashes in 2018 to 24 in 2019. A notable shift occurred with 'Ran off road - straight' crashes, which increased from just 1 in 2018 to 10 in 2019. Conversely, crashes attributed to 'Driving too fast for conditions' saw a significant drop, falling from 13 incidents in 2018 to 5 in 2019. Crashes from 'Following too close' more than doubled, increasing from 3 to 7 year-over-year.

Officer-Reported Primary Contributing Cause

Animal24 (24.2%)↓-7.7%prior 26
Ran off road - straight10 (10.1%)
Followed too close7 (7.1%)
FTYROW: At uncontrolled intersection6 (6.1%)→0.0%prior 6
Lost Control5 (5.1%)↓-28.6%prior 7
Driving too fast for conditions5 (5.1%)↓-61.5%prior 13
Improper Backing5 (5.1%)
FTYROW: From stop sign4 (4%)↓-42.9%prior 7
Ran Stop Sign4 (4%)
Driver Distraction: Other interior distraction3 (3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in both years predominantly occurred in daylight and clear weather conditions. A significant change was observed in road surface conditions, where crashes on icy or frosty roads more than doubled, increasing from 8 incidents in 2018 to 17 in 2019. Crashes on dry roads remained stable with 36 in 2018 and 37 in 2019.

Weather

Clear55 (69.6%)
↑37.5%prior 40
Cloudy8 (10.1%)
↓-33.3%prior 12
Rain4 (5.1%)
Snow3 (3.8%)
↓-40.0%prior 5
Fog, smoke, smog3 (3.8%)
Freezing rain/drizzle3 (3.8%)
Severe Winds1 (1.3%)
Sleet, hail1 (1.3%)
Blowing Snow1 (1.3%)
↓-80.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight53 (67.9%)
↑17.8%prior 45
Dark - roadway not lighted19 (24.4%)
↑46.2%prior 13
Dawn2 (2.6%)
Dark - roadway lighted2 (2.6%)
Dusk2 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry37 (46.8%)
↑2.8%prior 36
Ice/frost17 (21.5%)
↑112.5%prior 8
Snow10 (12.7%)
→0.0%prior 10
Wet9 (11.4%)
Gravel3 (3.8%)
Other (explain in narrative)1 (1.3%)
Mud, dirt1 (1.3%)
Slush1 (1.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet were the most common vehicle makes involved in crashes in both 2018 and 2019, with their numbers remaining relatively stable. An analysis of persons involved in crashes shows an increase across several age brackets. The number of individuals aged 16-20 involved in crashes increased from 16 to 24, and the 55-64 age group saw an increase from 19 to 29 persons involved year-over-year.

Top Vehicle Makes (145 vehicles)

1
FORD29 (20%)
↑16.0%prior 25
2
CHEV14 (9.7%)
↑27.3%prior 11
3
CHEVROLET10 (6.9%)
↓-37.5%prior 16
4
DODG8 (5.5%)
↑60.0%prior 5
5
PETERBILT5 (3.4%)
6
DEER4 (2.8%)
7
FREIGHTLINER4 (2.8%)
8
GMC4 (2.8%)
↓-33.3%prior 6
9
CHRY4 (2.8%)
10
INTERNATIONA4 (2.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (132 persons with recorded sex)

Male77 (58.3%)
↑48.1%prior 52
Female55 (41.7%)
↑12.2%prior 49

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 10, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 99
  • Total persons involved: 215
  • Total vehicles involved: 145

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: 2019." Published September 10, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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Osceola County, IA Crash Report — 2019 | ThatCarHitMe.com