Yearly Traffic Safety Analysis

130 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Guthrie County recorded 130 total vehicle crashes, a 2.3% decrease from the 133 crashes reported in 2017. While the overall number of crashes remained relatively stable, the most significant change was the elimination of traffic fatalities, which dropped from two in 2017 to zero in 2018. Conversely, the number of individuals injured increased from 44 to 52 over the same period.

130

▼ -2.3%was 133

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

52

▲ 18.2%was 44

Persons Injured

0

▼ -100.0%was 2

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

Overall, the total number of crashes in Guthrie County remained stable, showing a slight decrease of 2.3% from 133 in 2017 to 130 in 2018. Despite the small drop in total incidents, the number of reported injuries increased by 18.2%, rising from 44 to 52. However, traffic fatalities were eliminated, dropping from two in the prior year to zero in the current year.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 2↓-100.0%

52

Motorists Injured

Prior: 43↑20.9%

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 temporal patterns of crashes in Guthrie County showed some consistency year-over-year. Friday remained the peak day for crashes in both periods, though the count dropped from 33 in 2017 to 26 in 2018. The peak hour for crashes shifted slightly from 6 p.m. in 2017 (12 crashes) to 5 p.m. in 2018 (12 crashes), with the 7 a.m. hour also showing a high volume of 12 crashes in the current period.

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 saw a positive shift in 2018, with fatal crashes decreasing from two in 2017 to zero. The proportion of crashes resulting in no injuries increased from 69.9% of all crashes in 2017 to 77.7% in 2018. While the number of crashes involving serious injuries decreased from three to two, the total number of people injured rose from 44 to 52, indicating a higher number of injuries per injury-producing incident in 2018.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes1.5%
↓-33.3%prior 3
Minor Injury14minor injury crashes10.8%
↑7.7%prior 13
Possible Injury13possible injury crashes10%
↓-40.9%prior 22
No Injury101no injury crashes77.7%
↑8.6%prior 93

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

Collisions involving an animal remained the leading contributing factor in both periods, with the count of such crashes increasing by 45.2% from 42 in 2017 to 61 in 2018. 'Lost Control' was the second most common factor in both years, with a nearly stable count of 17 in 2017 and 16 in 2018. Crashes attributed to 'Driving too fast for conditions' decreased by 40% in count, from 10 incidents in 2017 to 6 in 2018.

Officer-Reported Primary Contributing Cause

Animal61 (46.9%)↑45.2%prior 42
Lost Control16 (12.3%)↓-5.9%prior 17
FTYROW: From stop sign8 (6.2%)→0.0%prior 8
Ran off road - left6 (4.6%)↓-14.3%prior 7
Driving too fast for conditions6 (4.6%)↓-40.0%prior 10
Improper Backing5 (3.8%)
FTYROW: From yield sign3 (2.3%)
Other (explain in narrative): Other3 (2.3%)↓-50.0%prior 6
FTYROW: Making left turn2 (1.5%)
FTYROW: Other (explain in narrative)2 (1.5%)

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

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with a majority of incidents in both 2018 and 2017 occurring in clear weather on dry roads during daylight hours. However, there was a notable increase in crashes attributed to adverse winter conditions. The number of crashes on roads with ice or frost doubled from 6 in 2017 to 12 in 2018, and crashes during freezing rain or drizzle increased from 2 to 7.

Weather

Clear64 (68.8%)
↓-7.2%prior 69
Cloudy9 (9.7%)
↓-50.0%prior 18
Freezing rain/drizzle7 (7.5%)
Snow5 (5.4%)
Rain3 (3.2%)
Fog, smoke, smog2 (2.2%)
Blowing Snow2 (2.2%)
Other (explain in narrative)1 (1.1%)

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

Lighting

Daylight55 (59.1%)
↓-5.2%prior 58
Dark - roadway not lighted26 (28.0%)
↓-25.7%prior 35
Dark - roadway lighted6 (6.5%)
Dawn3 (3.2%)
Dusk3 (3.2%)

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

Road Surface

Dry53 (56.4%)
↓-18.5%prior 65
Ice/frost12 (12.8%)
↑100.0%prior 6
Gravel10 (10.6%)
→0.0%prior 10
Snow10 (10.6%)
↑11.1%prior 9
Wet8 (8.5%)
↓-20.0%prior 10
Mud, dirt1 (1.1%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes were consistent, with Ford and Chevrolet being the most common in both years, though both saw a decrease in total crash involvement from 2017 to 2018. An analysis of persons involved in crashes shows a shift in age demographics. The number of individuals aged 16-20 involved in crashes decreased from 46 to 32, while involvement increased for the 26-34 age group (from 26 to 37) and the 65+ age group (from 20 to 26).

Top Vehicle Makes (170 vehicles)

1
FORD38 (22.4%)
↓-17.4%prior 46
2
CHEV25 (14.7%)
↓-24.2%prior 33
3
CHEVROLET12 (7.1%)
↓-33.3%prior 18
4
TOYT8 (4.7%)
5
JEEP8 (4.7%)
↑60.0%prior 5
6
TOYO7 (4.1%)
↑40.0%prior 5
7
DODG7 (4.1%)
8
BUIC5 (2.9%)
→0.0%prior 5
9
NISSAN4 (2.4%)
10
NISS4 (2.4%)

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

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

Sex Distribution (126 persons with recorded sex)

Male74 (58.7%)
↓-19.6%prior 92
Female52 (41.3%)
↑2.0%prior 51

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: 130
  • Total persons involved: 229
  • Total vehicles involved: 170

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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