Yearly Traffic Safety Analysis

146 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Lyon County, total traffic crashes increased by 14.1% from 128 in 2018 to 146 in 2019. The most significant year-over-year change was the increase in traffic fatalities, which rose from one in the prior period to four in the current period. Correspondingly, the number of fatal crashes increased from one to four.

146

▲ 14.1%was 128

Total Crash Events

4

▲ 300.0%was 1

Persons Killed

52

▼ -1.9%was 53

Persons Injured

4

▲ 300.0%was 1

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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in Lyon County shows an increase in crash frequency and severity year-over-year. Total collisions rose from 128 to 146, a 14.1% increase. While the total number of injuries remained nearly the same, decreasing from 53 to 52, the number of fatalities quadrupled from one to four.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

3

Motorists Killed

Prior: 1↑200.0%

0

Pedestrians Injured

Prior: 0→0.0%

52

Motorists Injured

Prior: 53↓-1.9%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal crash patterns shifted between the two periods. In 2019, the peak day for crashes was Tuesday with 26 incidents, and the peak hour was 7 a.m. with 13 incidents. This contrasts with 2018, when the peak day was Wednesday (25 crashes) and the peak hour was 3 p.m. (12 crashes), indicating a shift from an afternoon peak to a morning peak.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened year-over-year. The number of fatal crashes increased from one in 2018 to four in 2019, with the fatal crash rate rising from 0.8% to 2.7% of all crashes. The number of serious injury crashes was unchanged at three in both periods. Crashes resulting in minor injuries decreased from 21 to 19, while property-damage-only crashes increased from 84 to 100.

Outcome by Severity (Crash Events)

Fatal4fatal crashes2.7%
↑300.0%prior 1
Serious Injury3serious injury crashes2.1%
→0.0%prior 3
Minor Injury19minor injury crashes13%
↓-9.5%prior 21
Possible Injury20possible injury crashes13.7%
↑5.3%prior 19
No Injury100no injury crashes68.5%
↑19.0%prior 84

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both periods, increasing slightly from 32 crashes in 2018 to 34 in 2019. The counts for 'Driving too fast for conditions' and 'Lost Control' both saw a significant increase, rising from 11 crashes each in 2018 to 19 crashes each in 2019, a 72.7% increase in count for both factors. Conversely, crashes attributed to 'Ran Stop Sign' decreased from 8 to 3.

Officer-Reported Primary Contributing Cause

Animal34 (23.3%)↑6.3%prior 32
Driving too fast for conditions19 (13%)↑72.7%prior 11
Lost Control19 (13%)↑72.7%prior 11
Followed too close13 (8.9%)↑62.5%prior 8
Ran off road - straight11 (7.5%)↑120.0%prior 5
FTYROW: From stop sign7 (4.8%)→0.0%prior 7
Driver Distraction: Other interior distraction4 (2.7%)
Operating vehicle in an reckless, erratic, careless, negligent manner4 (2.7%)
FTYROW: Making left turn3 (2.1%)
Ran off road - left3 (2.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes under clear weather and on dry roads were most common in both years, and their counts increased from 68 to 86 and 59 to 78, respectively. There was a notable shift in adverse conditions; crashes on icy or frosty roads increased from 11 in 2018 to 19 in 2019. Similarly, collisions in dark, unlit roadway conditions rose from 22 to 30 incidents year-over-year.

Weather

Clear86 (68.3%)
↑26.5%prior 68
Cloudy17 (13.5%)
↑21.4%prior 14
Freezing rain/drizzle7 (5.6%)
→0.0%prior 7
Snow6 (4.8%)
↓-25.0%prior 8
Rain5 (4.0%)
→0.0%prior 5
Blowing Snow3 (2.4%)
Severe Winds1 (0.8%)
Fog, smoke, smog1 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight75 (59.1%)
↑7.1%prior 70
Dark - roadway not lighted30 (23.6%)
↑36.4%prior 22
Dark - roadway lighted9 (7.1%)
→0.0%prior 9
Dawn6 (4.7%)
Dusk6 (4.7%)
Dark - unknown roadway lighting1 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry78 (61.4%)
↑32.2%prior 59
Ice/frost19 (15.0%)
↑72.7%prior 11
Wet12 (9.4%)
↑33.3%prior 9
Snow11 (8.7%)
↓-38.9%prior 18
Gravel4 (3.1%)
↓-42.9%prior 7
Slush3 (2.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

Chevrolet and Ford vehicles were the most frequently involved makes in both periods. The number of Chevrolet-branded vehicles in crashes rose from 41 to 59, while Ford involvement remained stable at 33, down from 34. Analysis of persons involved shows a significant increase in the 16-20 age group (from 28 to 43 individuals), the 55-64 age group (from 23 to 41), and the 65+ age group (from 30 to 47).

Top Vehicle Makes (204 vehicles)

1
CHEV36 (17.6%)
↑63.6%prior 22
2
FORD33 (16.2%)
↓-2.9%prior 34
3
CHEVROLET23 (11.3%)
↑21.1%prior 19
4
BUIC7 (3.4%)
↑40.0%prior 5
5
GMC6 (2.9%)
↓-25.0%prior 8
6
HOND6 (2.9%)
7
DODGE5 (2.5%)
↓-50.0%prior 10
8
HONDA5 (2.5%)
9
CHRYSLER5 (2.5%)
10
JEEP5 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (193 persons with recorded sex)

Male125 (64.8%)
↑60.3%prior 78
Female68 (35.2%)
↑30.8%prior 52

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 146
  • Total persons involved: 290
  • Total vehicles involved: 204

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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