Yearly Traffic Safety Analysis

147 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Kossuth County, total vehicle crashes increased slightly from 142 in 2018 to 147 in 2019, a change of 3.5%. The number of injuries also rose from 64 to 70. The most significant year-over-year shift was the occurrence of two traffic fatalities in 2019, whereas none were recorded in the prior year.

147

▲ 3.5%was 142

Total Crash Events

2

Persons Killed

70

▲ 9.4%was 64

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

Trend Summary

Overall traffic crash trends in Kossuth County showed a slight increase year-over-year. Total crashes rose from 142 to 147, and the number of people injured increased by 9.4% from 64 to 70. Notably, after a year with zero fatalities, 2019 saw two individuals killed in traffic collisions.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 0→%

70

Motorists Injured

Prior: 63↑11.1%

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

The temporal patterns of crashes showed some consistency and some shifts between the two periods. Monday remained the peak day for crashes in both 2018 (28 crashes) and 2019 (26 crashes). However, the peak hour for collisions shifted two hours earlier, from 5 PM in 2018 (18 crashes) to 3 PM in 2019 (14 crashes).

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

The severity of crashes worsened in 2019 compared to the previous year. Two fatal crashes occurred, representing 1.4% of all incidents, up from zero fatal crashes in 2018. The proportion of serious injury crashes also increased, rising from 5.6% (8 crashes) in 2018 to 6.8% (10 crashes) in 2019. Crashes resulting in no injuries increased from 89 to 97.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.4%
Serious Injury10serious injury crashes6.8%
↑25.0%prior 8
Minor Injury16minor injury crashes10.9%
↓-27.3%prior 22
Possible Injury22possible injury crashes15%
↓-4.3%prior 23
No Injury97no injury crashes66%
↑9.0%prior 89

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

The leading contributing factors for crashes shifted between 2018 and 2019. "Driving too fast for conditions" became the top factor in 2019, with its count increasing by 70% from 10 to 17 crashes. Conversely, "Failure to yield from a stop sign," the second-leading cause in 2018 with 14 crashes, saw its count decrease to 9. The number of crashes attributed to "Ran Stop Sign" increased from 3 to 8 year-over-year.

Officer-Reported Primary Contributing Cause

Driving too fast for conditions17 (11.6%)↑70.0%prior 10
Followed too close13 (8.8%)↑30.0%prior 10
Lost Control12 (8.2%)→0.0%prior 12
Other (explain in narrative): Other12 (8.2%)↓-20.0%prior 15
FTYROW: From stop sign9 (6.1%)↓-35.7%prior 14
Animal9 (6.1%)↑12.5%prior 8
FTYROW: At uncontrolled intersection8 (5.4%)↑14.3%prior 7
Ran Stop Sign8 (5.4%)
Ran off road - straight6 (4.1%)↓-40.0%prior 10
Made improper turn6 (4.1%)↑20.0%prior 5

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

Road & Environmental Conditions

There was a notable shift toward crashes occurring in adverse conditions. The proportion of collisions on road surfaces affected by ice, snow, or water increased from 37.3% in 2018 to 47.6% in 2019. Crashes in cloudy conditions also rose from 28 to 38. Despite this, the share of crashes happening in daylight increased from 71.1% to 78.9% of all incidents.

Weather

Clear82 (57.7%)
↓-12.8%prior 94
Cloudy38 (26.8%)
↑35.7%prior 28
Snow5 (3.5%)
↓-44.4%prior 9
Freezing rain/drizzle5 (3.5%)
Rain5 (3.5%)
Blowing Snow4 (2.8%)
Other (explain in narrative)1 (0.7%)
Fog, smoke, smog1 (0.7%)
Severe Winds1 (0.7%)

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

Lighting

Daylight116 (81.7%)
↑14.9%prior 101
Dark - roadway not lighted14 (9.9%)
↓-33.3%prior 21
Dark - roadway lighted5 (3.5%)
↓-44.4%prior 9
Dawn4 (2.8%)
Dusk2 (1.4%)
Dark - unknown roadway lighting1 (0.7%)

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

Road Surface

Dry65 (45.8%)
↓-18.8%prior 80
Ice/frost31 (21.8%)
↑72.2%prior 18
Snow25 (17.6%)
↑19.0%prior 21
Wet12 (8.5%)
↑33.3%prior 9
Gravel6 (4.2%)
Slush2 (1.4%)
↓-60.0%prior 5
Mud, dirt1 (0.7%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, led by Ford and Chevrolet. The count of Ford vehicles in crashes rose from 33 to 38, while the combined count for vehicles listed as 'CHEV' or 'CHEVROLET' was 53 in 2019, a slight decrease from 55 in 2018. An analysis of persons involved shows a significant increase in the 26-34, 45-54, and 55-64 age groups, with each group's count rising to 53 individuals in 2019 from 34, 35, and 39, respectively.

Top Vehicle Makes (245 vehicles)

1
FORD38 (15.5%)
↑15.2%prior 33
2
CHEV29 (11.8%)
↑3.6%prior 28
3
CHEVROLET24 (9.8%)
↓-11.1%prior 27
4
GMC15 (6.1%)
↑66.7%prior 9
5
PETERBILT8 (3.3%)
6
JEEP8 (3.3%)
↑33.3%prior 6
7
FREIGHTLINER8 (3.3%)
8
DODGE8 (3.3%)
↑33.3%prior 6
9
DODG7 (2.9%)
↓-61.1%prior 18
10
TOYT7 (2.9%)
↑16.7%prior 6

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 (235 persons with recorded sex)

Male148 (63.0%)
↑48.0%prior 100
Female87 (37.0%)
↑10.1%prior 79

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: 147
  • Total persons involved: 330
  • Total vehicles involved: 245

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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