Yearly Traffic Safety Analysis

90 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Osceola County, total traffic crashes decreased by 18.2%, from 110 incidents in 2016 to 90 in 2017. Despite this overall reduction in crashes, the number of fatalities recorded saw a significant increase, rising from one in the prior year to four in the current year.

90

▼ -18.2%was 110

Total Crash Events

4

▲ 300.0%was 1

Persons Killed

30

▼ -26.8%was 41

Persons Injured

3

▲ 200.0%was 1

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Osceola County showed a downward trend, declining from 110 in 2016 to 90 in 2017. However, this positive trend in crash volume was contrasted by a sharp rise in crash severity. The number of fatal crashes increased from one to three, and the total number of persons killed rose from one to four year-over-year, while total injuries fell from 41 to 30.

Vulnerable Road User Casualties

4

Motorists Killed

Prior: 1↑300.0%

30

Motorists Injured

Prior: 41↓-26.8%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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 2017, Saturday was the peak day for crashes with 21 incidents, a change from 2016 when Friday was the peak day with 25 incidents. The peak hour for crashes also shifted slightly, moving from 8 p.m. (14 crashes) in 2016 to 7 p.m. (8 crashes) in 2017.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened significantly despite a drop in total incidents. The number of fatal crashes tripled from one in 2016 to three in 2017, and the fatal crash rate increased from 0.9% to 3.3% of all crashes. While the total number of injury-related crashes decreased from 29 in 2016 to 21 in 2017, the number of fatalities increased from one to four. The proportion of crashes resulting in no injuries remained stable, accounting for 73.3% of incidents in 2017 compared to 72.7% in 2016.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes3.3%
↑200.0%prior 1
Serious Injury5serious injury crashes5.6%
↓-16.7%prior 6
Minor Injury7minor injury crashes7.8%
↓-22.2%prior 9
Possible Injury9possible injury crashes10%
↓-35.7%prior 14
No Injury66no injury crashes73.3%
↓-17.5%prior 80

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the top contributing factor in both periods, though the count of these incidents decreased from 33 in 2016 to 25 in 2017, a 24% drop. The second most common factor, 'Lost Control,' saw its count increase from 13 to 15 incidents year-over-year. Other notable changes include a decrease in 'Ran off road - straight' incidents from 10 to 6, and an increase in crashes for 'FTYROW: At uncontrolled intersection' from 4 to 6.

Officer-Reported Primary Contributing Cause

Animal25 (27.8%)↓-24.2%prior 33
Lost Control15 (16.7%)↑15.4%prior 13
FTYROW: At uncontrolled intersection6 (6.7%)
Ran off road - straight6 (6.7%)↓-40.0%prior 10
Driving too fast for conditions5 (5.6%)→0.0%prior 5
FTYROW: From stop sign4 (4.4%)↓-33.3%prior 6
Driver Distraction: Inattentive/lost in thought3 (3.3%)
Made improper turn3 (3.3%)
Operating vehicle in an reckless, erratic, careless, negligent manner3 (3.3%)↓-40.0%prior 5
Other (explain in narrative): Other3 (3.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The conditions under which crashes occurred saw some shifts between the two years. In 2017, a higher proportion of crashes happened in clear weather (55.6%) and on dry roads (55.6%) compared to 2016 (43.6% and 47.3%, respectively). The share of crashes occurring in darkness without roadway lighting remained relatively consistent, accounting for 26.7% of crashes in 2017 versus 23.6% in 2016.

Weather

Clear50 (69.4%)
↑4.2%prior 48
Cloudy10 (13.9%)
↓-52.4%prior 21
Blowing Snow4 (5.6%)
Freezing rain/drizzle3 (4.2%)
Fog, smoke, smog2 (2.8%)
Rain1 (1.4%)
Severe Winds1 (1.4%)
Snow1 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight40 (55.6%)
↓-14.9%prior 47
Dark - roadway not lighted24 (33.3%)
↓-7.7%prior 26
Dark - roadway lighted5 (6.9%)
Dawn2 (2.8%)
Dusk1 (1.4%)
↓-80.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry50 (69.4%)
↓-3.8%prior 52
Ice/frost9 (12.5%)
↑12.5%prior 8
Gravel5 (6.9%)
Wet4 (5.6%)
↓-50.0%prior 8
Snow2 (2.8%)
↓-75.0%prior 8
Mud, dirt1 (1.4%)
Sand1 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The top three vehicle makes involved in crashes—Ford, Chevrolet, and Dodge—remained the same in both 2016 and 2017, with involvement counts for each decreasing in line with the overall drop in crashes. Analysis of person demographics shows a notable shift in age distribution; the number of individuals aged 16-20 involved in crashes dropped from 25 in 2016 to 10 in 2017. Conversely, the 0-15 age group saw an increase from one person involved in 2016 to six in 2017.

Top Vehicle Makes (119 vehicles)

1
FORD22 (18.5%)
↓-31.3%prior 32
2
CHEV16 (13.4%)
↑45.5%prior 11
3
CHEVROLET9 (7.6%)
↓-52.6%prior 19
4
DODG7 (5.9%)
↓-12.5%prior 8
5
DODGE6 (5%)
↓-45.5%prior 11
6
NISSAN5 (4.2%)
7
PETERBILT5 (4.2%)
↓-16.7%prior 6
8
VOLVO4 (3.4%)
9
FREIGHTLINER4 (3.4%)
10
CHRY3 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (84 persons with recorded sex)

Male56 (66.7%)
↓-18.8%prior 69
Female28 (33.3%)
↓-33.3%prior 42

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 90
  • Total persons involved: 145
  • Total vehicles involved: 119

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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