Yearly Traffic Safety Analysis

135 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Guthrie County, total vehicle crashes increased from 127 in 2019 to 135 in 2020, a rise of 6.3%. The most significant year-over-year change was the increase in fatalities, with three deaths recorded in 2020 compared to none in the prior year. This increase in crash volume and severity indicates a negative trend in traffic safety for the period.

135

▲ 6.3%was 127

Total Crash Events

3

Persons Killed

40

▲ 21.2%was 33

Persons Injured

3

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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

Traffic crashes in Guthrie County showed an upward trend from 2019 to 2020. The total number of incidents increased by 6.3%, from 127 to 135. This was accompanied by a rise in negative outcomes, as total injuries increased by 21.2% from 33 to 40, and fatalities rose from zero to three.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

39

Motorists Injured

Prior: 32↑21.9%

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 timing of crashes shifted between the two periods. In 2020, the peak day for crashes was Wednesday with 29 incidents, moving from Tuesday (22 incidents) in 2019. The peak hour also changed significantly, shifting from the 6 AM morning commute in 2019 (13 crashes) to the 5 PM evening commute in 2020 (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

Crash severity worsened in 2020 compared to the previous year. Three fatal crashes occurred, accounting for 2.2% of all incidents, whereas there were no fatal crashes in 2019. The proportion of serious injury crashes also increased from 3.1% to 4.4% of total crashes. Concurrently, the share of crashes resulting in possible injury rose from 9.4% to 13.3%.

Outcome by Severity (Crash Events)

Fatal3fatal crashes2.2%
Serious Injury6serious injury crashes4.4%
↑50.0%prior 4
Minor Injury10minor injury crashes7.4%
↓-33.3%prior 15
Possible Injury18possible injury crashes13.3%
↑50.0%prior 12
No Injury98no injury crashes72.6%
↑2.1%prior 96

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

Collisions involving an animal remained the top contributing factor in both years, with the count of such incidents increasing from 42 in 2019 to 51 in 2020. "Lost Control" was the second most common factor in both periods, with a nearly stable count of 15 in 2019 and 14 in 2020. Notably, crashes attributed to a vehicle running off the road to the left increased from 3 to 8, while incidents involving "Driving too fast for conditions" decreased from 7 to 2.

Officer-Reported Primary Contributing Cause

Animal51 (37.8%)↑21.4%prior 42
Lost Control14 (10.4%)↓-6.7%prior 15
Ran off road - straight9 (6.7%)↑80.0%prior 5
Ran off road - left8 (5.9%)
Other (explain in narrative): Other7 (5.2%)→0.0%prior 7
Ran off road - right5 (3.7%)
Operating vehicle in an reckless, erratic, careless, negligent manner5 (3.7%)
Ran Stop Sign5 (3.7%)
FTYROW: From stop sign4 (3%)
Exceeded authorized speed4 (3%)

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

Road & Environmental Conditions

The proportion of crashes occurring in clear weather was higher in 2020 (79 crashes) than in 2019 (64 crashes), while incidents during rainy conditions dropped from 10 to zero. Crashes on wet road surfaces also decreased significantly from 19 in 2019 to 6 in 2020. In contrast, crashes on snowy surfaces increased from 7 to 11. The number of crashes in daylight (58) and dark, unlighted conditions (38) in 2020 remained relatively stable compared to the prior year's counts of 53 and 40, respectively.

Weather

Clear79 (76.7%)
↑23.4%prior 64
Cloudy13 (12.6%)
↓-35.0%prior 20
Snow7 (6.8%)
↑40.0%prior 5
Fog, smoke, smog2 (1.9%)
Freezing rain/drizzle1 (1.0%)
Severe Winds1 (1.0%)

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

Lighting

Daylight58 (55.8%)
↑9.4%prior 53
Dark - roadway not lighted38 (36.5%)
↓-5.0%prior 40
Dark - roadway lighted4 (3.8%)
↓-42.9%prior 7
Dawn2 (1.9%)
Dusk2 (1.9%)

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

Road Surface

Dry75 (71.4%)
↑10.3%prior 68
Snow11 (10.5%)
↑57.1%prior 7
Gravel10 (9.5%)
Wet6 (5.7%)
↓-68.4%prior 19
Ice/frost3 (2.9%)
↓-70.0%prior 10

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

Vehicles & Demographics

Chevrolet and Ford were the top two vehicle makes involved in crashes in both years, though both saw their total counts decrease from 2019 to 2020. There were notable shifts in the age distribution of persons involved in crashes. The 26-34 age group saw a significant increase in involvement, from 43 individuals in 2019 to 59 in 2020. Similarly, the 55-64 age group increased from 20 to 32 individuals, and the 16-20 age group grew from 36 to 43.

Top Vehicle Makes (173 vehicles)

1
FORD30 (17.3%)
↓-11.8%prior 34
2
CHEVROLET21 (12.1%)
↓-12.5%prior 24
3
CHEV18 (10.4%)
↓-47.1%prior 34
4
JEEP10 (5.8%)
5
GMC8 (4.6%)
→0.0%prior 8
6
DODGE6 (3.5%)
↓-14.3%prior 7
7
CHRY6 (3.5%)
8
NR5 (2.9%)
9
NISS5 (2.9%)
10
PONT4 (2.3%)

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

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

Sex Distribution (160 persons with recorded sex)

Male100 (62.5%)
↑12.4%prior 89
Female60 (37.5%)
↓-11.8%prior 68

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: 135
  • Total persons involved: 259
  • Total vehicles involved: 173

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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