Yearly Traffic Safety Analysis

174 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, O'Brien County recorded 174 total crashes, a slight decrease of 1.1% from the 176 crashes reported in 2016. The number of injuries also decreased from 84 to 72, and there were no fatalities in 2017, down from one the prior year. A notable shift occurred in contributing factors, where crashes involving animals nearly doubled from 11 in 2016 to 21 in 2017, becoming the leading factor.

174

▼ -1.1%was 176

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

72

▼ -14.3%was 84

Persons Injured

0

▼ -100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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 O'Brien County remained relatively stable year-over-year, with a slight 1.1% decrease from 176 incidents in 2016 to 174 in 2017. However, the outcomes of these crashes improved, as total injuries fell by 14.3% from 84 to 72, and fatalities dropped from one to zero.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 1↓-100.0%

0

Motorists Killed

Prior: 0→0.0%

1

Cyclists Injured

Prior: 0→%

71

Motorists Injured

Prior: 84↓-15.5%

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 temporal patterns of crashes showed some shifts between 2016 and 2017. The most common day for crashes moved from Friday (33 crashes) in 2016 to Wednesday (34 crashes) in 2017. While the peak hour for collisions remained the 4 p.m. hour in both years, the volume of crashes during this time increased from 19 to 24.

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 improved in 2017 compared to the prior year, with no fatal crashes recorded, down from one in 2016. The proportion of crashes resulting in no injuries increased from 64.2% to 71.8% of all incidents. Correspondingly, the share of crashes involving minor injuries decreased from 14.2% in 2016 to 8.6% in 2017.

Outcome by Severity (Crash Events)

Serious Injury7serious injury crashes4%
↑16.7%prior 6
Minor Injury15minor injury crashes8.6%
↓-40.0%prior 25
Possible Injury27possible injury crashes15.5%
↓-12.9%prior 31
No Injury125no injury crashes71.8%
↑10.6%prior 113

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

The leading contributing factors for crashes shifted significantly year-over-year. In 2017, collisions involving an 'Animal' became the top factor with 21 incidents, a 90.9% increase from 11 crashes in 2016. Conversely, the previous leading cause, 'FTYROW: From stop sign,' decreased in count from 18 to 11 incidents. Crashes attributed to 'Driver Distraction: Inattentive/lost in thought' also saw a notable increase, rising from 2 incidents in 2016 to 10 in 2017.

Officer-Reported Primary Contributing Cause

Animal21 (12.1%)↑90.9%prior 11
Ran off road - left14 (8%)↑7.7%prior 13
Driving too fast for conditions12 (6.9%)↓-20.0%prior 15
FTYROW: From stop sign11 (6.3%)↓-38.9%prior 18
Driver Distraction: Inattentive/lost in thought10 (5.7%)
Lost Control9 (5.2%)↓-35.7%prior 14
Ran Stop Sign9 (5.2%)↑12.5%prior 8
Followed too close9 (5.2%)→0.0%prior 9
FTYROW: From yield sign8 (4.6%)↑33.3%prior 6
Made improper turn8 (4.6%)

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 environmental conditions under which crashes occurred were largely similar year-over-year. In both 2016 and 2017, the majority of crashes happened in daylight (118 incidents each year) and on dry roads (107 incidents each year). There was a modest decrease in crashes on adverse road surfaces like ice, snow, or wet pavement, which accounted for 50 crashes in 2017 compared to 60 in 2016. Weather conditions also remained consistent, with clear or cloudy skies present during approximately 79% of crashes in both periods.

Weather

Clear90 (57.3%)
↓-3.2%prior 93
Cloudy47 (29.9%)
↓-2.1%prior 48
Rain7 (4.5%)
↑40.0%prior 5
Snow7 (4.5%)
↑16.7%prior 6
Blowing Snow4 (2.5%)
Fog, smoke, smog1 (0.6%)
Freezing rain/drizzle1 (0.6%)

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

Lighting

Daylight118 (75.2%)
→0.0%prior 118
Dark - roadway not lighted21 (13.4%)
↓-22.2%prior 27
Dark - roadway lighted8 (5.1%)
↓-42.9%prior 14
Dusk8 (5.1%)
Dawn2 (1.3%)
↓-60.0%prior 5

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

Road Surface

Dry107 (68.2%)
→0.0%prior 107
Wet17 (10.8%)
↑13.3%prior 15
Ice/frost16 (10.2%)
↓-5.9%prior 17
Snow10 (6.4%)
↓-33.3%prior 15
Gravel4 (2.5%)
↓-33.3%prior 6
Slush2 (1.3%)
↓-66.7%prior 6
Mud, dirt1 (0.6%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford and Chevrolet being the most common in both 2016 and 2017. The number of Fords involved increased from 51 to 60, while the number of Chevrolets decreased from a combined 65 to 59. An analysis of persons involved in crashes shows a notable increase in the 16-20 age group, which grew from 43 individuals in 2016 to 51 in 2017. Conversely, involvement for the 55-64 age group decreased from 48 to 32 people over the same period.

Top Vehicle Makes (276 vehicles)

1
FORD60 (21.7%)
↑17.6%prior 51
2
CHEV44 (15.9%)
↑83.3%prior 24
3
GMC16 (5.8%)
↑14.3%prior 14
4
CHEVROLET15 (5.4%)
↓-63.4%prior 41
5
CHRY12 (4.3%)
↑71.4%prior 7
6
BUIC11 (4%)
↑10.0%prior 10
7
DODGE11 (4%)
↓-21.4%prior 14
8
PONT10 (3.6%)
↑66.7%prior 6
9
FREIGHTLINER9 (3.3%)
↑28.6%prior 7
10
DODG9 (3.3%)
↓-40.0%prior 15

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

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

Sex Distribution (206 persons with recorded sex)

Male124 (60.2%)
↓-8.1%prior 135
Female82 (39.8%)
↓-5.7%prior 87

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: 174
  • Total persons involved: 327
  • Total vehicles involved: 276

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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