Yearly Traffic Safety Analysis

371 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Winneshiek County, there were 371 total crashes in 2016, a 2.1% decrease from the 379 crashes recorded in 2015. While overall crashes and injuries declined, the most notable year-over-year shift was the doubling of traffic fatalities, which increased from 2 in 2015 to 4 in 2016.

371

▼ -2.1%was 379

Total Crash Events

4

▲ 100.0%was 2

Persons Killed

97

▼ -14.9%was 114

Persons Injured

4

▲ 100.0%was 2

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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

The overall trend in Winneshiek County shows a slight decrease in traffic collisions, with total crashes falling from 379 in 2015 to 371 in 2016. Correspondingly, the number of people injured decreased by 14.9%, from 114 to 97. However, this downward trend in crashes and injuries was contrasted by a sharp increase in fatalities, which doubled from 2 to 4 over the same period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 2↑100.0%

0

Other Killed

Prior: 0→0.0%

4

Pedestrians Injured

Prior: 2↑100.0%

1

Cyclists Injured

Prior: 0→%

91

Motorists Injured

Prior: 112↓-18.8%

1

Other Injured

Prior: 0→%

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 showed some shifts between the two periods. In 2016, Monday was the peak day for crashes with 65 incidents, a change from 2015 which had a three-way tie for the peak day between Tuesday, Wednesday, and Saturday (59 crashes each). The evening commute hour remained the most frequent time for crashes, with 5 p.m. being the peak hour in both 2016 (35 crashes) and 2015 (40 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

Crash severity worsened in 2016 compared to the prior year. The number of fatal crashes doubled from 2 to 4, causing the fatal crash rate to increase from 0.5% to 1.1% of all crashes. Conversely, crashes resulting in injuries saw a decline; serious injury crashes fell from 16 to 10, and minor injury crashes decreased from 38 to 32. The proportion of crashes with no injuries increased slightly from 76.8% in 2015 to 79.0% in 2016.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.1%
↑100.0%prior 2
Serious Injury10serious injury crashes2.7%
↓-37.5%prior 16
Minor Injury32minor injury crashes8.6%
↓-15.8%prior 38
Possible Injury32possible injury crashes8.6%
→0.0%prior 32
No Injury293no injury crashes79%
↑0.7%prior 291

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 with animals remained the top contributing factor in both periods, with the count of such incidents increasing by 29.2% from 120 in 2015 to 155 in 2016. 'Lost Control' was the second-most cited factor in both years, with a stable count of 45 in 2016 compared to 46 in 2015. A notable decrease was seen in crashes attributed to 'Failure to Yield Right of Way from a Stop Sign,' which fell from 21 incidents in 2015 to 12 in 2016.

Officer-Reported Primary Contributing Cause

Animal155 (41.8%)↑29.2%prior 120
Lost Control45 (12.1%)↓-2.2%prior 46
Driving too fast for conditions24 (6.5%)↑14.3%prior 21
Ran off road - left16 (4.3%)↓-11.1%prior 18
Ran off road - straight15 (4%)↓-31.8%prior 22
FTYROW: From stop sign12 (3.2%)↓-42.9%prior 21
Other (explain in narrative): Other11 (3%)↓-8.3%prior 12
Followed too close11 (3%)↓-8.3%prior 12
FTYROW: Making left turn7 (1.9%)
Other (explain in narrative): No improper action6 (1.6%)

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, crash conditions showed some variations. Crashes on dry roads decreased from 244 in 2015 to 158 in 2016, while crashes on wet roads increased from 19 to 29. In terms of weather, incidents during clear conditions fell from 228 to 154, and crashes in snow decreased from 28 to 15. The number of crashes occurring in daylight also saw a reduction, from 215 in 2015 to 147 in 2016.

Weather

Clear154 (60.4%)
↓-32.5%prior 228
Cloudy55 (21.6%)
↓-15.4%prior 65
Snow15 (5.9%)
↓-46.4%prior 28
Rain10 (3.9%)
→0.0%prior 10
Freezing rain/drizzle6 (2.4%)
Blowing Snow5 (2.0%)
Fog, smoke, smog5 (2.0%)
Severe Winds2 (0.8%)
Other (explain in narrative)2 (0.8%)
Sleet, hail1 (0.4%)

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

Lighting

Daylight147 (57.0%)
↓-31.6%prior 215
Dark - roadway not lighted74 (28.7%)
↓-20.4%prior 93
Dark - roadway lighted19 (7.4%)
↓-13.6%prior 22
Dusk11 (4.3%)
↓-15.4%prior 13
Dawn6 (2.3%)
↑20.0%prior 5
Dark - unknown roadway lighting1 (0.4%)

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

Road Surface

Dry158 (61.7%)
↓-35.2%prior 244
Snow34 (13.3%)
↓-5.6%prior 36
Wet29 (11.3%)
↑52.6%prior 19
Ice/frost15 (5.9%)
↓-21.1%prior 19
Gravel13 (5.1%)
↓-38.1%prior 21
Slush4 (1.6%)
↓-20.0%prior 5
Sand3 (1.2%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained broadly consistent, with Chevrolet and Ford being the top two in both years. In 2016, Chevrolet was involved in 102 crashes and Ford in 77, compared to 120 and 94 respectively in 2015. The age demographics of people involved in crashes saw a shift, with a notable decrease in the 16-20 age group from 99 individuals in 2015 to 83 in 2016. Conversely, involvement for the 26-34 age group increased from 82 to 87.

Top Vehicle Makes (489 vehicles)

1
FORD77 (15.7%)
↓-18.1%prior 94
2
CHEVROLET54 (11%)
↓-50.5%prior 109
3
CHEV48 (9.8%)
↑336.4%prior 11
4
DODGE26 (5.3%)
↓-7.1%prior 28
5
GMC22 (4.5%)
→0.0%prior 22
6
BUIC22 (4.5%)
↑340.0%prior 5
7
HONDA19 (3.9%)
↑35.7%prior 14
8
JEEP16 (3.3%)
↓-5.9%prior 17
9
TOYOTA14 (2.9%)
↓-17.6%prior 17
10
DODG14 (2.9%)

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

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

Sex Distribution (385 persons with recorded sex)

Male217 (56.4%)
↓-22.2%prior 279
Female168 (43.6%)
↓-18.8%prior 207

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: 371
  • Total persons involved: 561
  • Total vehicles involved: 489

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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