Yearly Traffic Safety Analysis

142 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Kossuth County recorded 142 total crashes, a 15.5% decrease from the 168 crashes reported in 2017. The total number of injuries also decreased from 79 to 64 during this period. Notably, there were no fatal crashes in 2018, an improvement from the one fatal crash and one resulting fatality recorded in the prior year.

142

▼ -15.5%was 168

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

64

▼ -19.0%was 79

Persons Injured

0

▼ -100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Kossuth County indicates a downward trend from 2017 to 2018. Total crashes fell by 15.5%, from 168 to 142. This trend extended to crash outcomes, with total injuries decreasing by 19.0% from 79 to 64, and fatalities dropping from one to zero.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 1↓-100.0%

1

Cyclists Injured

Prior: 0→%

63

Motorists Injured

Prior: 78↓-19.2%

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes in Kossuth County shifted between 2017 and 2018. The peak day for collisions moved from Wednesday, with 30 crashes in 2017, to Monday, with 28 crashes in 2018. The busiest hour for crashes also shifted later in the day, from 3 p.m. in 2017 to 5 p.m. in 2018, though both peak hours recorded 18 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity improved from 2017 to 2018, with fatal crashes decreasing from one to zero. The number of serious injury crashes also fell from 10 to 8. While the absolute count of minor injury crashes was unchanged at 22, their proportion of total crashes increased from 13.1% to 15.5% due to the overall reduction in collisions.

Outcome by Severity (Crash Events)

Serious Injury8serious injury crashes5.6%
↓-20.0%prior 10
Minor Injury22minor injury crashes15.5%
→0.0%prior 22
Possible Injury23possible injury crashes16.2%
↓-20.7%prior 29
No Injury89no injury crashes62.7%
↓-16.0%prior 106

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes showed some shifts between 2017 and 2018. Crashes attributed to "Failure to yield from a stop sign" increased from 10 incidents to 14 incidents. Conversely, crashes involving "Lost Control" decreased from 19 incidents to 12, and those involving "Driving too fast for conditions" fell from 13 to 10 incidents.

Officer-Reported Primary Contributing Cause

Other (explain in narrative): Other15 (10.6%)→0.0%prior 15
FTYROW: From stop sign14 (9.9%)↑40.0%prior 10
Lost Control12 (8.5%)↓-36.8%prior 19
Ran off road - straight10 (7%)→0.0%prior 10
Driving too fast for conditions10 (7%)↓-23.1%prior 13
Followed too close10 (7%)↓-9.1%prior 11
Animal8 (5.6%)↑14.3%prior 7
Ran off road - left8 (5.6%)↑33.3%prior 6
FTYROW: At uncontrolled intersection7 (4.9%)↓-30.0%prior 10
Driver Distraction: Other interior distraction6 (4.2%)↓-33.3%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While most crashes in both periods occurred in daylight on dry roads, there was a notable shift in the prevalence of adverse road surface conditions. In 2018, 31.0% of crashes (44 incidents) occurred on roads with snow, ice, or slush, an increase from the 20.2% share (34 incidents) reported in 2017. The proportion of crashes in daylight decreased slightly from 73.2% in 2017 to 71.1% in 2018.

Weather

Clear94 (68.1%)
↓-10.5%prior 105
Cloudy28 (20.3%)
↓-26.3%prior 38
Snow9 (6.5%)
↑12.5%prior 8
Rain2 (1.4%)
Fog, smoke, smog2 (1.4%)
Freezing rain/drizzle1 (0.7%)
↓-85.7%prior 7
Blowing Snow1 (0.7%)
Sleet, hail1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash

Lighting

Daylight101 (73.2%)
↓-17.9%prior 123
Dark - roadway not lighted21 (15.2%)
→0.0%prior 21
Dark - roadway lighted9 (6.5%)
↓-40.0%prior 15
Dawn4 (2.9%)
Dusk3 (2.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field

Road Surface

Dry80 (58.0%)
↓-26.6%prior 109
Snow21 (15.2%)
↑31.3%prior 16
Ice/frost18 (13.0%)
↑12.5%prior 16
Wet9 (6.5%)
→0.0%prior 9
Slush5 (3.6%)
Gravel4 (2.9%)
↓-60.0%prior 10
Mud, dirt1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Road surface condition field

Vehicles & Demographics

The top three vehicle makes involved in crashes remained consistent, with Chevrolet, Ford, and Dodge leading in both years, although the number of vehicles from each make decreased. The number of Chevrolets involved in crashes fell from 77 to 55, while Fords dropped from 51 to 33. Regarding persons involved, there was a general decrease across most age brackets; for example, the 16-20 age group saw involvement drop from 48 to 38 individuals.

Top Vehicle Makes (234 vehicles)

1
FORD33 (14.1%)
↓-35.3%prior 51
2
CHEV28 (12%)
↓-30.0%prior 40
3
CHEVROLET27 (11.5%)
↓-27.0%prior 37
4
DODG18 (7.7%)
↑20.0%prior 15
5
BUIC16 (6.8%)
↑77.8%prior 9
6
GMC9 (3.8%)
↓-55.0%prior 20
7
DODGE6 (2.6%)
↓-45.5%prior 11
8
TOYT6 (2.6%)
9
CHRY6 (2.6%)
↑20.0%prior 5
10
JEEP6 (2.6%)
↑20.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records

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

Sex Distribution (179 persons with recorded sex)

Male100 (55.9%)
↓-13.8%prior 116
Female79 (44.1%)
↓-14.1%prior 92

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 142
  • Total persons involved: 284
  • Total vehicles involved: 234

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: 2018." Published September 9, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2018-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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