Yearly Traffic Safety Analysis

110 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Osceola County, total traffic crashes increased by 50.7% from 73 in 2015 to 110 in 2016. This substantial rise in collisions was accompanied by a 51.9% increase in injuries, from 27 to 41. The most significant contributing factor in both periods was collisions with animals, which saw a 73.7% increase in count from 19 incidents in 2015 to 33 in 2016.

110

▲ 50.7%was 73

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

41

▲ 51.9%was 27

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 · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety trends in Osceola County worsened from 2015 to 2016, with total crashes climbing from 73 to 110 (+50.7%). The number of people injured in these incidents also rose from 27 to 41 (+51.9%). Conversely, the number of fatalities decreased from two in 2015 to one in 2016.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 2↓-50.0%

41

Motorists Injured

Prior: 27↑51.9%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted significantly between the two years. In 2016, the peak day for crashes was Friday with 25 incidents, a change from Monday (14 incidents) in 2015. The peak hour also moved from the morning to the evening, with 7 a.m. being the peak in 2015 (7 crashes) and 8 p.m. being the peak in 2016 (14 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the total number of crashes increased, the severity profile showed a mixed trend. The number of fatal crashes decreased from two in 2015 to one in 2016, with the corresponding fatal crash rate dropping from 2.74% to 0.91%. The proportion of crashes involving any injury remained relatively stable, accounting for 28.8% of crashes in 2015 and 26.4% in 2016. However, the absolute number of serious injury crashes increased from 4 to 6.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.9%
↓-50.0%prior 2
Serious Injury6serious injury crashes5.5%
↑50.0%prior 4
Minor Injury9minor injury crashes8.2%
↓-18.2%prior 11
Possible Injury14possible injury crashes12.7%
↑133.3%prior 6
No Injury80no injury crashes72.7%
↑60.0%prior 50

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both years, with the count increasing by 73.7% from 19 crashes in 2015 to 33 in 2016. 'Lost Control' as a factor saw a 160% increase in count, rising from 5 incidents to 13, making it the second-leading cause in 2016. Conversely, crashes attributed to 'Failure to Yield Right of Way from a stop sign' decreased slightly from 7 to 6.

Officer-Reported Primary Contributing Cause

Animal33 (30%)↑73.7%prior 19
Lost Control13 (11.8%)↑160.0%prior 5
Ran off road - straight10 (9.1%)↑66.7%prior 6
Ran off road - left8 (7.3%)
FTYROW: From stop sign6 (5.5%)↓-14.3%prior 7
Operating vehicle in an reckless, erratic, careless, negligent manner5 (4.5%)
Driving too fast for conditions5 (4.5%)↓-16.7%prior 6
Followed too close4 (3.6%)
FTYROW: At uncontrolled intersection4 (3.6%)↓-33.3%prior 6
Driver Distraction: Exterior distraction3 (2.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Year-over-year data shows a notable shift in lighting conditions during crashes. The proportion of crashes occurring in darkness on unlit roadways increased from 12.3% (9 of 73 crashes) in 2015 to 23.6% (26 of 110 crashes) in 2016. Crashes in clear weather and on dry road surfaces made up a larger share of the total in 2016 compared to 2015, increasing from 37.0% to 43.6% and 39.7% to 47.3%, respectively.

Weather

Clear48 (57.8%)
↑77.8%prior 27
Cloudy21 (25.3%)
↑110.0%prior 10
Blowing Snow4 (4.8%)
Rain4 (4.8%)
Snow2 (2.4%)
↓-66.7%prior 6
Fog, smoke, smog1 (1.2%)
Severe Winds1 (1.2%)
Sleet, hail1 (1.2%)
Freezing rain/drizzle1 (1.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight47 (56.6%)
↑27.0%prior 37
Dark - roadway not lighted26 (31.3%)
↑188.9%prior 9
Dusk5 (6.0%)
Dark - roadway lighted2 (2.4%)
Dawn2 (2.4%)
Dark - unknown roadway lighting1 (1.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry52 (61.9%)
↑79.3%prior 29
Snow8 (9.5%)
↓-27.3%prior 11
Wet8 (9.5%)
Ice/frost8 (9.5%)
↑33.3%prior 6
Slush4 (4.8%)
Gravel2 (2.4%)
Sand1 (1.2%)
Mud, dirt1 (1.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet were the top two vehicle makes involved in crashes in both periods. The most significant demographic shift was observed in the 16-20 age group, where the number of persons involved in crashes more than doubled from 12 in 2015 to 25 in 2016. In contrast, involvement of the 26-34 age group remained nearly static, with 28 individuals in 2015 and 27 in 2016.

Top Vehicle Makes (155 vehicles)

1
FORD32 (20.6%)
↑45.5%prior 22
2
CHEVROLET19 (12.3%)
↑58.3%prior 12
3
DODGE11 (7.1%)
4
CHEV11 (7.1%)
↓-15.4%prior 13
5
DODG8 (5.2%)
↑60.0%prior 5
6
GMC8 (5.2%)
7
CHRYSLER7 (4.5%)
8
BUIC6 (3.9%)
9
PETERBILT6 (3.9%)
10
PONTIAC5 (3.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (111 persons with recorded sex)

Male69 (62.2%)
↑9.5%prior 63
Female42 (37.8%)
↑55.6%prior 27

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 110
  • Total persons involved: 179
  • Total vehicles involved: 155

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: 2016." Published September 9, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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