Yearly Traffic Safety Analysis

359 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Winneshiek County recorded 359 total crashes, a 3.2% decrease from the 371 crashes reported in 2016. While overall crashes saw a slight decline, the most significant year-over-year change was a substantial reduction in single-vehicle, non-collision incidents, which fell from 247 to 149. Fatalities were also halved, dropping from 4 in 2016 to 2 in 2017.

359

▼ -3.2%was 371

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

84

▼ -13.4%was 97

Persons Injured

2

▼ -50.0%was 4

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Winneshiek County showed a downward trend from 2016 to 2017. Total crashes decreased by 3.2%, from 371 to 359. This decline was accompanied by a 13.4% reduction in total injuries (from 97 to 84) and a 50% drop in fatalities (from 4 to 2).

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 4↓-50.0%

1

Pedestrians Injured

Prior: 4↓-75.0%

83

Motorists Injured

Prior: 91↓-8.8%

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 temporal patterns of crashes shifted between 2016 and 2017. The peak day for crashes moved from Monday (65 crashes) in 2016 to Saturday (61 crashes) in 2017. Similarly, the peak hour shifted slightly later in the evening, from 5 p.m. (35 crashes) in the prior year to 6 p.m. (32 crashes) in the current year.

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

The severity of crashes decreased from 2016 to 2017. The number of fatal crashes was halved, falling from 4 to 2, which corresponded to a drop in the fatal crash rate from 1.08 to 0.56 per 100 crashes. The count of serious injury crashes also declined from 10 to 6. Crashes resulting in no injuries constituted the majority in both periods, accounting for 79.0% of crashes in 2016 and 80.2% in 2017.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.6%
↓-50.0%prior 4
Serious Injury6serious injury crashes1.7%
↓-40.0%prior 10
Minor Injury30minor injury crashes8.4%
↓-6.3%prior 32
Possible Injury33possible injury crashes9.2%
↑3.1%prior 32
No Injury288no injury crashes80.2%
↓-1.7%prior 293

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 involving an animal remained the top contributing factor in both periods, with a nearly unchanged count of 153 in 2017 compared to 155 in 2016. However, there were notable shifts in other major factors. Crashes attributed to 'Lost Control' decreased by 40% in count, from 45 incidents in 2016 to 27 in 2017. Conversely, crashes involving 'Failure to Yield Right of Way from a stop sign' increased in count by 41.7%, from 12 to 17, making it the third most common factor in 2017.

Officer-Reported Primary Contributing Cause

Animal153 (42.6%)↓-1.3%prior 155
Lost Control27 (7.5%)↓-40.0%prior 45
FTYROW: From stop sign17 (4.7%)↑41.7%prior 12
Ran off road - left16 (4.5%)→0.0%prior 16
Driving too fast for conditions15 (4.2%)↓-37.5%prior 24
Other (explain in narrative): Other13 (3.6%)↑18.2%prior 11
Ran off road - straight12 (3.3%)↓-20.0%prior 15
Followed too close9 (2.5%)↓-18.2%prior 11
Ran Stop Sign9 (2.5%)↑50.0%prior 6
FTYROW: Making left turn8 (2.2%)↑14.3%prior 7

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

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with the majority of incidents in both 2016 and 2017 occurring in clear weather and on dry roads. However, there was a notable decrease in crashes occurring in darkness on unlighted roadways, which fell from 74 in 2016 to 43 in 2017. Crashes on adverse road surfaces like snow, ice, or slush also saw a decline, dropping from a combined 53 incidents in 2016 to 33 in 2017.

Weather

Clear157 (65.4%)
↑1.9%prior 154
Cloudy45 (18.8%)
↓-18.2%prior 55
Fog, smoke, smog11 (4.6%)
↑120.0%prior 5
Snow11 (4.6%)
↓-26.7%prior 15
Freezing rain/drizzle7 (2.9%)
↑16.7%prior 6
Rain4 (1.7%)
↓-60.0%prior 10
Blowing Snow2 (0.8%)
↓-60.0%prior 5
Severe Winds2 (0.8%)
Other (explain in narrative)1 (0.4%)

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

Lighting

Daylight158 (65.8%)
↑7.5%prior 147
Dark - roadway not lighted43 (17.9%)
↓-41.9%prior 74
Dark - roadway lighted23 (9.6%)
↑21.1%prior 19
Dusk11 (4.6%)
→0.0%prior 11
Dawn4 (1.7%)
↓-33.3%prior 6
Dark - unknown roadway lighting1 (0.4%)

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

Road Surface

Dry174 (72.8%)
↑10.1%prior 158
Ice/frost21 (8.8%)
↑40.0%prior 15
Wet19 (7.9%)
↓-34.5%prior 29
Gravel12 (5.0%)
↓-7.7%prior 13
Snow11 (4.6%)
↓-67.6%prior 34
Other (explain in narrative)1 (0.4%)
Slush1 (0.4%)

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

Vehicles & Demographics

Ford and Chevrolet remained the two most common vehicle makes involved in crashes in both 2016 and 2017, with counts of 70 and 98 respectively in the current period. The number of Jeeps involved in crashes saw a notable increase, rising from 16 in 2016 to 28 in 2017. Regarding driver and passenger demographics, the number of persons aged 16-20 involved in crashes remained stable at 85, while involvement for the 21-25 age group decreased from 64 to 49.

Top Vehicle Makes (480 vehicles)

1
FORD70 (14.6%)
↓-9.1%prior 77
2
CHEVROLET54 (11.3%)
→0.0%prior 54
3
CHEV44 (9.2%)
↓-8.3%prior 48
4
JEEP28 (5.8%)
↑75.0%prior 16
5
GMC19 (4%)
↓-13.6%prior 22
6
PONT18 (3.8%)
↑28.6%prior 14
7
BUIC18 (3.8%)
↓-18.2%prior 22
8
DODG14 (2.9%)
→0.0%prior 14
9
DODGE12 (2.5%)
↓-53.8%prior 26
10
CHRY12 (2.5%)
↑50.0%prior 8

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

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

Sex Distribution (324 persons with recorded sex)

Male178 (54.9%)
↓-18.0%prior 217
Female146 (45.1%)
↓-13.1%prior 168

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: 359
  • Total persons involved: 542
  • Total vehicles involved: 480

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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