Yearly Traffic Safety Analysis

120 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Lyon County, total crashes decreased by 17.2% from 145 in 2015 to 120 in 2016. While the overall number of collisions and injuries fell, the most notable year-over-year change was the emergence of traffic fatalities, with two deaths recorded in 2016 compared to none in the prior year.

120

▼ -17.2%was 145

Total Crash Events

2

Persons Killed

62

▼ -12.7%was 71

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 · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety trends in Lyon County show a mix of improvements and new concerns. Total crashes declined from 145 to 120 year-over-year, and total injuries also saw a decrease from 71 to 62. However, this positive trend was offset by the introduction of fatal crashes, with two fatalities occurring in 2016 where none were recorded in 2015.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 0→%

1

Pedestrians Injured

Prior: 0→%

61

Motorists Injured

Prior: 69↓-11.6%

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 temporal patterns of crashes showed both consistency and change. Friday remained the peak day for crashes in both 2016 (27 crashes) and 2015 (34 crashes). However, the peak hour for collisions shifted significantly, moving from the 3 p.m. hour in 2015 (14 crashes) to the 8 a.m. hour in 2016 (10 crashes), indicating a change from an afternoon to a morning peak.

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

While total crashes decreased, their severity profile worsened year-over-year. The county recorded two fatal crashes in 2016, representing 1.7% of all crashes, whereas there were no fatal crashes in 2015. The number of serious injury crashes also increased slightly from 4 to 5, and minor injury crashes rose from 18 to 21. Conversely, crashes resulting in possible injury or no injury saw a decline in both count and their share of the total.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.7%
Serious Injury5serious injury crashes4.2%
↑25.0%prior 4
Minor Injury21minor injury crashes17.5%
↑16.7%prior 18
Possible Injury18possible injury crashes15%
↓-37.9%prior 29
No Injury74no injury crashes61.7%
↓-21.3%prior 94

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

The leading contributing factors for crashes showed some shifts between 2015 and 2016. Collisions involving an "Animal" remained the top factor, with the count holding steady at 25 crashes in both years. Crashes attributed to "Lost Control" decreased significantly in count from 23 to 13. In contrast, crashes due to "Driving too fast for conditions" increased in count from 8 in 2015 to 13 in 2016, becoming the second-most common factor alongside "Lost Control".

Officer-Reported Primary Contributing Cause

Animal25 (20.8%)→0.0%prior 25
Lost Control13 (10.8%)↓-43.5%prior 23
Driving too fast for conditions13 (10.8%)↑62.5%prior 8
Ran off road - straight12 (10%)↑20.0%prior 10
FTYROW: From stop sign6 (5%)↓-14.3%prior 7
Followed too close5 (4.2%)↓-28.6%prior 7
FTYROW: From driveway4 (3.3%)
Driver Distraction: Exterior distraction3 (2.5%)
FTYROW: Making left turn3 (2.5%)
Made improper turn3 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

An analysis of crash conditions reveals a shift toward incidents in darkness and on icy surfaces. Crashes in daylight decreased from 92 to 60, while those on unlit dark roadways increased from 32 to 34. Year-over-year, the number of crashes on roads with ice or frost doubled, rising from 6 to 12. Conversely, crashes on snowy surfaces were less frequent, dropping from 21 incidents in 2015 to 9 in 2016.

Weather

Clear81 (74.3%)
↓-13.8%prior 94
Cloudy8 (7.3%)
↓-60.0%prior 20
Snow7 (6.4%)
↓-41.7%prior 12
Fog, smoke, smog6 (5.5%)
Rain3 (2.8%)
Severe Winds2 (1.8%)
Freezing rain/drizzle1 (0.9%)
Blowing Snow1 (0.9%)

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

Lighting

Daylight60 (55.0%)
↓-34.8%prior 92
Dark - roadway not lighted34 (31.2%)
↑6.3%prior 32
Dark - roadway lighted7 (6.4%)
Dusk6 (5.5%)
↓-25.0%prior 8
Dawn2 (1.8%)
↓-60.0%prior 5

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

Road Surface

Dry74 (67.9%)
↓-12.9%prior 85
Ice/frost12 (11.0%)
↑100.0%prior 6
Gravel11 (10.1%)
↓-35.3%prior 17
Snow9 (8.3%)
↓-57.1%prior 21
Wet3 (2.8%)
↓-70.0%prior 10

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

Vehicles & Demographics

The types of vehicles involved in crashes remained largely consistent year-over-year. Ford and Chevrolet were the top two makes involved in collisions in both 2016 and 2015, with counts for both decreasing in line with the overall reduction in crashes. An analysis of persons involved shows a decrease in total participants from 288 to 207. However, the proportion of individuals in the 16-20 and 65+ age groups increased slightly as a share of all persons involved in crashes.

Top Vehicle Makes (177 vehicles)

1
FORD32 (18.1%)
↓-13.5%prior 37
2
CHEVROLET31 (17.5%)
↑34.8%prior 23
3
CHEV13 (7.3%)
↓-50.0%prior 26
4
GMC9 (5.1%)
↑12.5%prior 8
5
FREIGHTLINER8 (4.5%)
6
DODG6 (3.4%)
↓-45.5%prior 11
7
BUICK5 (2.8%)
↓-16.7%prior 6
8
HONDA5 (2.8%)
9
PONTIAC5 (2.8%)
10
PETERBILT5 (2.8%)
↓-16.7%prior 6

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

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

Sex Distribution (123 persons with recorded sex)

Male88 (71.5%)
↓-22.8%prior 114
Female35 (28.5%)
↓-50.0%prior 70

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: 120
  • Total persons involved: 207
  • Total vehicles involved: 177

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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