Yearly Traffic Safety Analysis

177 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Cherokee County recorded 177 traffic crashes, a 13.5% increase from the 156 crashes reported in 2017. This period also saw total fatalities rise from one to two. A notable change was the increase in crashes attributed to animals, which rose in count from 39 in 2017 to 49 in 2018.

177

▲ 13.5%was 156

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

55

▲ 22.2%was 45

Persons Injured

2

▲ 100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

Traffic crashes in Cherokee County showed an upward trend from 2017 to 2018. The total number of crashes increased by 13.5%, from 156 to 177. Correspondingly, the number of people injured rose by 22.2% from 45 to 55, and fatalities doubled from one to two.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 1↑100.0%

1

Pedestrians Injured

Prior: 1→0.0%

54

Motorists Injured

Prior: 44↑22.7%

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 shifted between the two periods. In 2018, the peak day for crashes was Tuesday with 31 incidents, a change from Friday (29 incidents) in the prior year. The peak hour for collisions also moved from 7 a.m. in 2017 (16 crashes) to 3 p.m. in 2018 (16 crashes), indicating a shift from morning commute times to the afternoon.

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

The number of fatal crashes doubled from one in 2017 to two in 2018, with the fatal crash rate increasing from 0.64 to 1.13 per 100 crashes. The overall proportion of crashes resulting in any level of injury (serious, minor, or possible) remained stable at approximately 25% across both years. While serious injury crashes decreased from 4 to 3, minor injury crashes increased from 14 to 19.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.1%
↑100.0%prior 1
Serious Injury3serious injury crashes1.7%
↓-25.0%prior 4
Minor Injury19minor injury crashes10.7%
↑35.7%prior 14
Possible Injury22possible injury crashes12.4%
↑4.8%prior 21
No Injury131no injury crashes74%
↑12.9%prior 116

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 top contributing factor in both years, with the count increasing from 39 to 49. Crashes attributed to 'Driving too fast for conditions' more than doubled, rising from 7 to 17 incidents, and 'Followed too close' increased from 5 to 14 incidents. Conversely, crashes due to 'Failure to yield from a stop sign' decreased from 15 to 10, and 'Lost Control' incidents fell from 14 to 9.

Officer-Reported Primary Contributing Cause

Animal49 (27.7%)↑25.6%prior 39
Driving too fast for conditions17 (9.6%)↑142.9%prior 7
Followed too close14 (7.9%)↑180.0%prior 5
Other (explain in narrative): Other12 (6.8%)↑100.0%prior 6
FTYROW: From stop sign10 (5.6%)↓-33.3%prior 15
Lost Control9 (5.1%)↓-35.7%prior 14
Ran off road - straight9 (5.1%)↑80.0%prior 5
FTYROW: Making left turn7 (4%)
Ran off road - left5 (2.8%)↓-16.7%prior 6
Ran Stop Sign4 (2.3%)↓-20.0%prior 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

While most crashes in both years occurred in clear weather, there was a notable increase in incidents under adverse road conditions. Crashes on roads with ice or frost more than tripled, rising from 5 in 2017 to 16 in 2018. The proportion of crashes occurring during daylight hours increased from 50.6% in 2017 to 58.7% in 2018, while the share of crashes on dry surfaces decreased from 58.3% to 53.1%.

Weather

Clear100 (70.4%)
↓-2.0%prior 102
Cloudy19 (13.4%)
↑90.0%prior 10
Rain8 (5.6%)
↑60.0%prior 5
Snow6 (4.2%)
Freezing rain/drizzle4 (2.8%)
Blowing Snow2 (1.4%)
Sleet, hail2 (1.4%)
Fog, smoke, smog1 (0.7%)

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

Lighting

Daylight104 (72.7%)
↑31.6%prior 79
Dark - roadway not lighted23 (16.1%)
↓-8.0%prior 25
Dark - roadway lighted9 (6.3%)
↓-35.7%prior 14
Dawn3 (2.1%)
↓-40.0%prior 5
Dusk3 (2.1%)
Dark - unknown roadway lighting1 (0.7%)

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

Road Surface

Dry94 (66.2%)
↑3.3%prior 91
Ice/frost16 (11.3%)
↑220.0%prior 5
Wet15 (10.6%)
↑15.4%prior 13
Snow8 (5.6%)
↓-20.0%prior 10
Gravel6 (4.2%)
↓-14.3%prior 7
Slush3 (2.1%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent year-over-year, with Chevrolet and Ford leading in both periods; Chevrolet vehicles increased from 49 to 64, and Fords from 39 to 42. Analysis of persons involved in crashes reveals a significant demographic shift, with a substantial increase in the number of individuals from older age groups. The count of persons aged 45-54 involved in crashes more than doubled from 22 to 50, and the 55-64 age group increased from 30 to 51.

Top Vehicle Makes (265 vehicles)

1
CHEV46 (17.4%)
↑43.8%prior 32
2
FORD42 (15.8%)
↑7.7%prior 39
3
CHEVROLET18 (6.8%)
↑5.9%prior 17
4
DODG17 (6.4%)
↑70.0%prior 10
5
GMC16 (6%)
↑128.6%prior 7
6
BUIC11 (4.2%)
↑37.5%prior 8
7
CHRY10 (3.8%)
8
PONT10 (3.8%)
↑66.7%prior 6
9
JEEP9 (3.4%)
↑80.0%prior 5
10
TOYT7 (2.6%)

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

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

Sex Distribution (210 persons with recorded sex)

Male116 (55.2%)
↑10.5%prior 105
Female94 (44.8%)
↑49.2%prior 63

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: 177
  • Total persons involved: 328
  • Total vehicles involved: 265

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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