Yearly Traffic Safety Analysis

120 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Calhoun County, total traffic crashes increased from 106 in 2017 to 120 in 2018, representing a 13.2% rise. Despite the increase in total collisions, the number of fatalities decreased from 4 to 3 year-over-year. The most notable shift was a decrease in single-vehicle, non-collision crashes, which dropped from 58 to 42, while broadside collisions increased from 17 to 25.

120

▲ 13.2%was 106

Total Crash Events

3

▼ -25.0%was 4

Persons Killed

39

▲ 2.6%was 38

Persons Injured

3

▼ -25.0%was 4

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 · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic incidents in Calhoun County trended upward in 2018, with total crashes increasing by 13.2% from 106 to 120. However, the severity of these incidents showed a mixed trend; total fatalities decreased from 4 to 3, while total injuries remained nearly unchanged, rising from 38 to 39.

Vulnerable Road User Casualties

3

Motorists Killed

Prior: 4↓-25.0%

39

Motorists Injured

Prior: 38↑2.6%

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, Monday was the peak day for crashes with 27 incidents, a significant change from 2017 when Tuesday and Friday were the peak days with 20 crashes each. The peak hour for crashes also moved later in the day, from 1 p.m. (9 crashes) in 2017 to 3 p.m. (10 crashes) in 2018.

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 severity of crashes showed a decrease year-over-year. The fatal crash rate fell from 3.77 per 100 crashes in 2017 to 2.5 in 2018, with fatal crashes decreasing from 4 to 3. The proportion of crashes resulting in any level of injury also declined, from 35.8% of all crashes in 2017 (38 crashes) to 25.8% in 2018 (31 crashes), driven by a drop in 'Possible Injury' crashes from 19 to 13.

Outcome by Severity (Crash Events)

Fatal3fatal crashes2.5%
↓-25.0%prior 4
Serious Injury3serious injury crashes2.5%
↓-25.0%prior 4
Minor Injury12minor injury crashes10%
↑9.1%prior 11
Possible Injury13possible injury crashes10.8%
↓-31.6%prior 19
No Injury89no injury crashes74.2%
↑30.9%prior 68

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 with animals remained the leading contributing factor in both years, with the count increasing from 30 in 2017 to 33 in 2018. The number of crashes attributed to a driver losing control saw a significant 50% decrease, falling from 16 incidents in 2017 to 8 in 2018. Conversely, crashes involving 'Driving too fast for conditions' increased in count from 6 to 9.

Officer-Reported Primary Contributing Cause

Animal33 (27.5%)↑10.0%prior 30
Driving too fast for conditions9 (7.5%)↑50.0%prior 6
Lost Control8 (6.7%)↓-50.0%prior 16
Other (explain in narrative): Other6 (5%)
Ran off road - straight5 (4.2%)↓-44.4%prior 9
Ran Stop Sign5 (4.2%)↓-28.6%prior 7
FTYROW: At uncontrolled intersection5 (4.2%)→0.0%prior 5
FTYROW: From stop sign5 (4.2%)
Driver Distraction: Other interior distraction4 (3.3%)
FTYROW: From yield sign4 (3.3%)

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 shifted year-over-year, with a lower proportion of incidents occurring on dry roads and in clear weather. Crashes on dry roads fell from 57 to 40, while those on wet roads increased from 4 to 15. Similarly, crashes in clear weather decreased from 52 to 42, while those in cloudy conditions rose from 16 to 24. Incidents in daylight increased from 51 to 60, while crashes in unlit dark conditions decreased from 30 to 17.

Weather

Clear42 (46.2%)
↓-19.2%prior 52
Cloudy24 (26.4%)
↑50.0%prior 16
Blowing Snow9 (9.9%)
↑50.0%prior 6
Snow6 (6.6%)
Freezing rain/drizzle4 (4.4%)
↓-20.0%prior 5
Rain2 (2.2%)
Other (explain in narrative)1 (1.1%)
Severe Winds1 (1.1%)
Sleet, hail1 (1.1%)
Fog, smoke, smog1 (1.1%)

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

Lighting

Daylight60 (65.9%)
↑17.6%prior 51
Dark - roadway not lighted17 (18.7%)
↓-43.3%prior 30
Dark - roadway lighted6 (6.6%)
Dusk5 (5.5%)
Dawn2 (2.2%)
Dark - unknown roadway lighting1 (1.1%)

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

Road Surface

Dry40 (44.4%)
↓-29.8%prior 57
Wet15 (16.7%)
Ice/frost13 (14.4%)
↓-23.5%prior 17
Snow12 (13.3%)
Gravel6 (6.7%)
→0.0%prior 6
Slush4 (4.4%)

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

Vehicles & Demographics

Ford and Chevrolet vehicles were the most frequently involved in crashes during both periods, with the counts for both makes increasing in 2018. An analysis of persons involved in crashes shows an increase in the 16-20 age group, which grew from 25 individuals in 2017 to 32 in 2018. The 65+ age group also saw an increase in involvement, from 28 persons in 2017 to 34 in 2018.

Top Vehicle Makes (176 vehicles)

1
FORD30 (17%)
↑66.7%prior 18
2
CHEV26 (14.8%)
↑8.3%prior 24
3
CHEVROLET18 (10.2%)
↑20.0%prior 15
4
DODG12 (6.8%)
↑71.4%prior 7
5
DODGE8 (4.5%)
↓-20.0%prior 10
6
CHRY6 (3.4%)
7
GMC6 (3.4%)
8
BUIC6 (3.4%)
9
JEEP5 (2.8%)
10
PONTIAC5 (2.8%)

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

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

Sex Distribution (126 persons with recorded sex)

Male80 (63.5%)
↑17.6%prior 68
Female46 (36.5%)
↑7.0%prior 43

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 10, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 120
  • Total persons involved: 217
  • Total vehicles involved: 176

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 10, 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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