Yearly Traffic Safety Analysis

127 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Guthrie County recorded 127 total vehicle crashes, a 2.3% decrease from the 130 crashes reported in 2018. While total crashes remained relatively stable, the number of reported injuries saw a notable year-over-year decline of 36.5%, falling from 52 to 33. There were no fatalities recorded in either period.

127

▼ -2.3%was 130

Total Crash Events

0

Persons Killed

33

▼ -36.5%was 52

Persons Injured

0

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

Trend Summary

Overall traffic crash volume in Guthrie County remained stable, with a slight decrease from 130 incidents in 2018 to 127 in 2019. The number of people injured in these crashes decreased by 36.5%, from 52 in 2018 to 33 in 2019. There were no fatalities recorded in either period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 0→%

32

Motorists Injured

Prior: 52↓-38.5%

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 shifted between the two periods. In 2019, the peak day for crashes was Tuesday with 22 incidents, changing from Friday (26 incidents) in the prior year. The peak hour for crashes also shifted significantly from the evening commute at 5 p.m. in 2018 (12 crashes) to the morning at 6 a.m. in 2019 (13 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

There were no fatal crashes recorded in either 2018 or 2019. The number of crashes resulting in serious injuries doubled from 2 in 2018 to 4 in 2019, representing 3.1% of all crashes in the current period compared to 1.5% in the prior period. The count of minor injury crashes remained stable at 15 in 2019 versus 14 in 2018, while possible injury crashes decreased slightly from 13 to 12.

Outcome by Severity (Crash Events)

Serious Injury4serious injury crashes3.1%
↑100.0%prior 2
Minor Injury15minor injury crashes11.8%
↑7.1%prior 14
Possible Injury12possible injury crashes9.4%
↓-7.7%prior 13
No Injury96no injury crashes75.6%
↓-5.0%prior 101

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 animals remained the top contributing factor in both periods but saw a significant decrease in count from 61 crashes in 2018 to 42 in 2019. 'Lost Control' was the second most common factor in both years, with a stable count of 16 in 2018 and 15 in 2019. Crashes attributed to 'Failure to Yield from a Stop Sign' decreased by half, from 8 incidents in 2018 to 4 in 2019.

Officer-Reported Primary Contributing Cause

Animal42 (33.1%)↓-31.1%prior 61
Lost Control15 (11.8%)↓-6.3%prior 16
Other (explain in narrative): Other7 (5.5%)
Driving too fast for conditions7 (5.5%)↑16.7%prior 6
Driver Distraction: Other interior distraction5 (3.9%)
Ran off road - straight5 (3.9%)
Ran Stop Sign4 (3.1%)
FTYROW: From stop sign4 (3.1%)↓-50.0%prior 8
Operating vehicle in an reckless, erratic, careless, negligent manner4 (3.1%)
FTYROW: Other (explain in narrative)3 (2.4%)

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

Road & Environmental Conditions

The number of crashes occurring in clear weather was identical in both periods, with 64 incidents each year. A significant shift was observed in lighting conditions, with crashes on dark, unlit roadways increasing from 26 in 2018 to 40 in 2019. Crashes on wet road surfaces also more than doubled, from 8 in 2018 to 19 in 2019.

Weather

Clear64 (59.8%)
→0.0%prior 64
Cloudy20 (18.7%)
↑122.2%prior 9
Rain10 (9.3%)
Snow5 (4.7%)
→0.0%prior 5
Freezing rain/drizzle3 (2.8%)
↓-57.1%prior 7
Blowing Snow2 (1.9%)
Fog, smoke, smog2 (1.9%)
Other (explain in narrative)1 (0.9%)

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

Lighting

Daylight53 (49.1%)
↓-3.6%prior 55
Dark - roadway not lighted40 (37.0%)
↑53.8%prior 26
Dark - roadway lighted7 (6.5%)
↑16.7%prior 6
Dusk4 (3.7%)
Dawn3 (2.8%)
Dark - unknown roadway lighting1 (0.9%)

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

Road Surface

Dry68 (63.6%)
↑28.3%prior 53
Wet19 (17.8%)
↑137.5%prior 8
Ice/frost10 (9.3%)
↓-16.7%prior 12
Snow7 (6.5%)
↓-30.0%prior 10
Slush2 (1.9%)
Gravel1 (0.9%)
↓-90.0%prior 10

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

Vehicles & Demographics

Analysis of vehicles involved shows a shift in the top makes; in 2019, Chevrolet-branded vehicles were most common (58), overtaking Ford (34), which was the top make in 2018 with 38 vehicles. The demographic data of persons involved in crashes shows an increase in the 26-34 age group, from 37 individuals in 2018 to 43 in 2019. Conversely, the number of persons in the 0-15 age group involved in crashes decreased from 11 to 2.

Top Vehicle Makes (171 vehicles)

1
FORD34 (19.9%)
↓-10.5%prior 38
2
CHEV34 (19.9%)
↑36.0%prior 25
3
CHEVROLET24 (14%)
↑100.0%prior 12
4
GMC8 (4.7%)
5
DODGE7 (4.1%)
6
DODG6 (3.5%)
↓-14.3%prior 7
7
KIA5 (2.9%)
8
JEEP4 (2.3%)
↓-50.0%prior 8
9
BUIC4 (2.3%)
↓-20.0%prior 5
10
NISS4 (2.3%)

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

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

Sex Distribution (157 persons with recorded sex)

Male89 (56.7%)
↑20.3%prior 74
Female68 (43.3%)
↑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: 127
  • Total persons involved: 239
  • Total vehicles involved: 171

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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