Yearly Traffic Safety Analysis

131 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Kossuth County recorded 131 total crashes, a 10.9% decrease from the 147 crashes documented in 2019. Despite the overall reduction in collisions, the number of fatalities doubled, increasing from 2 in 2019 to 4 in 2020. The total number of injuries, however, decreased by 28.6% from 70 to 50 over the same period.

131

▼ -10.9%was 147

Total Crash Events

4

▲ 100.0%was 2

Persons Killed

50

▼ -28.6%was 70

Persons Injured

4

▲ 100.0%was 2

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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic collisions in Kossuth County showed a downward trend from 2019 to 2020, with total crashes decreasing by 10.9% from 147 to 131. The number of people injured also declined by 28.6%, from 70 to 50. In contrast to these trends, the number of fatalities rose from 2 in 2019 to 4 in 2020.

Vulnerable Road User Casualties

4

Motorists Killed

Prior: 2↑100.0%

50

Motorists Injured

Prior: 70↓-28.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted between the two years. In 2020, the peak day for crashes was Friday with 27 incidents, a change from 2019 when Monday was the peak day with 26 crashes. The peak time for crashes remained consistent in the mid-afternoon, with 3 p.m. being a joint peak hour in both 2020 (13 crashes) and 2019 (14 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes decreased, the rate of fatal crashes increased year-over-year. The number of fatal crashes doubled from 2 in 2019 to 4 in 2020, with the fatal crash rate rising from 1.36% to 3.05%. Conversely, crashes resulting in serious injuries were halved, dropping from 10 in 2019 to 5 in 2020. The proportion of crashes resulting in no injuries increased slightly from 66.0% to 67.9% of all incidents.

Outcome by Severity (Crash Events)

Fatal4fatal crashes3.1%
↑100.0%prior 2
Serious Injury5serious injury crashes3.8%
↓-50.0%prior 10
Minor Injury12minor injury crashes9.2%
↓-25.0%prior 16
Possible Injury21possible injury crashes16%
↓-4.5%prior 22
No Injury89no injury crashes67.9%
↓-8.2%prior 97

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes shifted significantly between periods. In 2020, collisions involving an animal became the top factor, with the count of such incidents increasing by 88.9% from 9 to 17. In contrast, 'Driving too fast for conditions,' which was the top factor in 2019 with 17 crashes, saw its count decrease by 64.7% to 6 crashes in 2020. 'Followed too close' also saw a large drop in count, from 13 incidents in 2019 to 4 in 2020.

Officer-Reported Primary Contributing Cause

Animal17 (13%)↑88.9%prior 9
Lost Control13 (9.9%)↑8.3%prior 12
Other (explain in narrative): Other9 (6.9%)↓-25.0%prior 12
FTYROW: From stop sign9 (6.9%)→0.0%prior 9
Ran off road - left8 (6.1%)↑60.0%prior 5
Driver Distraction: Other interior distraction8 (6.1%)↑60.0%prior 5
Driving too fast for conditions6 (4.6%)↓-64.7%prior 17
FTYROW: From driveway5 (3.8%)
Operating vehicle in an reckless, erratic, careless, negligent manner5 (3.8%)
Exceeded authorized speed5 (3.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in 2020 occurred more frequently on dry roads compared to 2019. In 2020, 63.4% of crashes (83) were on dry surfaces, up from a 44.2% share (65) in 2019. Correspondingly, crashes on adverse road surfaces saw a significant decrease; incidents on icy roads fell from 31 to 10, and crashes on snowy roads fell from 25 to 10. The majority of crashes in both periods occurred in clear weather and during daylight hours.

Weather

Clear80 (66.7%)
↓-2.4%prior 82
Cloudy21 (17.5%)
↓-44.7%prior 38
Snow5 (4.2%)
→0.0%prior 5
Freezing rain/drizzle4 (3.3%)
↓-20.0%prior 5
Rain4 (3.3%)
↓-20.0%prior 5
Blowing Snow3 (2.5%)
Fog, smoke, smog2 (1.7%)
Severe Winds1 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight92 (76.7%)
↓-20.7%prior 116
Dark - roadway not lighted13 (10.8%)
↓-7.1%prior 14
Dark - roadway lighted9 (7.5%)
↑80.0%prior 5
Dawn5 (4.2%)
Dark - unknown roadway lighting1 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry83 (69.2%)
↑27.7%prior 65
Wet11 (9.2%)
↓-8.3%prior 12
Snow10 (8.3%)
↓-60.0%prior 25
Ice/frost10 (8.3%)
↓-67.7%prior 31
Gravel4 (3.3%)
↓-33.3%prior 6
Slush2 (1.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

An analysis of the data shows a shift in the top-ranked vehicle makes involved in crashes. In 2019, Ford was the most common make with 38 vehicles, but in 2020, Chevrolet took the top spot with 30 vehicles involved in crashes. The demographic profile of persons involved also changed, with a notable increase in the proportion of individuals aged 65 and older, who represented 18.5% of persons in 2020 compared to 11.8% in 2019.

Top Vehicle Makes (205 vehicles)

1
CHEVROLET30 (14.6%)
↑25.0%prior 24
2
FORD28 (13.7%)
↓-26.3%prior 38
3
CHEV20 (9.8%)
↓-31.0%prior 29
4
DODG11 (5.4%)
↑57.1%prior 7
5
TOYT11 (5.4%)
↑57.1%prior 7
6
GMC10 (4.9%)
↓-33.3%prior 15
7
JEEP6 (2.9%)
↓-25.0%prior 8
8
TOYO5 (2.4%)
9
PONT5 (2.4%)
10
DODGE5 (2.4%)
↓-37.5%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

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

Sex Distribution (189 persons with recorded sex)

Male118 (62.4%)
↓-20.3%prior 148
Female71 (37.6%)
↓-18.4%prior 87

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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: 2020-01-01 through 2020-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 131
  • Total persons involved: 281
  • Total vehicles involved: 205

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: 2020." Published September 9, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2020-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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