Yearly Traffic Safety Analysis

147 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Lyon County recorded 147 total crashes, a 22.5% increase from the 120 crashes documented in 2016. While total fatalities remained unchanged at two for both years, the number of crashes involving a driver under the influence of alcohol increased from 6 to 11. Despite the rise in total collisions, the number of reported injuries decreased from 62 to 56.

147

▲ 22.5%was 120

Total Crash Events

2

Persons Killed

56

▼ -9.7%was 62

Persons Injured

2

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 Lyon County showed an upward trend, increasing from 120 in 2016 to 147 in 2017, a 22.5% rise. In contrast, the number of people injured in these incidents declined by 9.7%, from 62 to 56. The number of fatalities held steady at two for both years.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

1

Cyclists Injured

Prior: 0→%

55

Motorists Injured

Prior: 61↓-9.8%

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, the peak day for crashes was Sunday with 25 incidents, a change from 2016 when Friday was the peak day with 27 crashes. The most frequent crash hour also moved from the morning commute in 2016 (8 a.m. with 10 crashes) to the evening in 2017 (8 p.m. with 14 crashes).

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 number of fatal crashes remained constant at two in both 2016 and 2017, though the fatal crash rate decreased from 1.67% to 1.36% of all crashes due to the higher total crash volume in 2017. The proportion of crashes resulting in any type of injury fell, with injury-related incidents accounting for 29.3% of all crashes in 2017 compared to 36.7% in 2016. Correspondingly, no-injury crashes increased as a share of the total, rising from 61.7% to 69.4%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.4%
→0.0%prior 2
Serious Injury3serious injury crashes2%
↓-40.0%prior 5
Minor Injury22minor injury crashes15%
↑4.8%prior 21
Possible Injury18possible injury crashes12.2%
→0.0%prior 18
No Injury102no injury crashes69.4%
↑37.8%prior 74

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 leading contributing factor in both years, with the count increasing from 25 incidents in 2016 to 38 in 2017. The number of crashes attributed to 'Followed too close' doubled, rising from 5 to 10 incidents. Conversely, crashes where 'Driving too fast for conditions' was a factor decreased from 13 incidents in 2016 to 8 in 2017.

Officer-Reported Primary Contributing Cause

Animal38 (25.9%)↑52.0%prior 25
Lost Control14 (9.5%)↑7.7%prior 13
Followed too close10 (6.8%)↑100.0%prior 5
Driving too fast for conditions8 (5.4%)↓-38.5%prior 13
FTYROW: From stop sign7 (4.8%)↑16.7%prior 6
Ran off road - left6 (4.1%)
Ran Stop Sign5 (3.4%)
Ran off road - straight5 (3.4%)↓-58.3%prior 12
Made improper turn4 (2.7%)
Driver Distraction: Other interior distraction4 (2.7%)

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

Road & Environmental Conditions

The proportion of crashes occurring in daylight was similar across both periods, accounting for 52.4% of crashes in 2017 and 50% in 2016. Crashes on dry road surfaces made up a larger share in 2017 (66.7%) compared to 2016 (61.7%). Notably, incidents on roads with ice or frost decreased, dropping from 12 crashes in 2016 to 6 in 2017.

Weather

Clear97 (77.6%)
↑19.8%prior 81
Cloudy18 (14.4%)
↑125.0%prior 8
Rain3 (2.4%)
Snow3 (2.4%)
↓-57.1%prior 7
Blowing Snow2 (1.6%)
Fog, smoke, smog1 (0.8%)
↓-83.3%prior 6
Freezing rain/drizzle1 (0.8%)

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

Lighting

Daylight77 (61.6%)
↑28.3%prior 60
Dark - roadway not lighted34 (27.2%)
→0.0%prior 34
Dark - roadway lighted7 (5.6%)
→0.0%prior 7
Dusk4 (3.2%)
↓-33.3%prior 6
Dawn3 (2.4%)

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

Road Surface

Dry98 (78.4%)
↑32.4%prior 74
Gravel12 (9.6%)
↑9.1%prior 11
Ice/frost6 (4.8%)
↓-50.0%prior 12
Snow4 (3.2%)
↓-55.6%prior 9
Wet3 (2.4%)
Sand1 (0.8%)
Other (explain in narrative)1 (0.8%)

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

Vehicles & Demographics

Ford and Chevrolet were the most common vehicle makes involved in crashes in both years, with the number of Chevrolets increasing from 44 in 2016 to 52 in 2017. An analysis of the age of persons involved shows a notable increase in the 16-20 age group, which grew from 29 individuals in 2016 to 40 in 2017. The 35-44 age group also saw an increase from 30 to 41 persons, while the 65+ age group saw a decrease from 29 to 24.

Top Vehicle Makes (216 vehicles)

1
FORD34 (15.7%)
↑6.3%prior 32
2
CHEVROLET28 (13%)
↓-9.7%prior 31
3
CHEV24 (11.1%)
↑84.6%prior 13
4
DODGE12 (5.6%)
5
GMC11 (5.1%)
↑22.2%prior 9
6
DODG10 (4.6%)
↑66.7%prior 6
7
HONDA8 (3.7%)
↑60.0%prior 5
8
PETERBILT7 (3.2%)
↑40.0%prior 5
9
PONT7 (3.2%)
10
PONTIAC7 (3.2%)
↑40.0%prior 5

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

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

Sex Distribution (152 persons with recorded sex)

Male96 (63.2%)
↑9.1%prior 88
Female56 (36.8%)
↑60.0%prior 35

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: 147
  • Total persons involved: 253
  • Total vehicles involved: 216

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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