Yearly Traffic Safety Analysis

111 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Calhoun County, total crashes increased by 11% from 100 in 2015 to 111 in 2016. While total fatalities remained stable at one, total injuries saw a slight increase from 34 to 35. One of the most notable shifts was the doubling of DUI-related crashes, which rose from 3 in 2015 to 6 in 2016.

111

▲ 11.0%was 100

Total Crash Events

1

Persons Killed

35

▲ 2.9%was 34

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Calhoun County were generally upward year-over-year. Total collisions rose by 11%, from 100 in 2015 to 111 in 2016. The number of people injured increased slightly from 34 to 35, while the number of fatalities held steady at one for both periods.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 1→0.0%

35

Motorists Injured

Prior: 34↑2.9%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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 years. The peak day for crashes moved from Tuesday (18 crashes) in 2015 to Friday (25 crashes) in 2016. The peak hour for collisions, however, remained consistent at 2 p.m. in both periods, though the number of crashes during that hour decreased from 13 to 11.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The number of fatal crashes remained unchanged at one event in both 2015 and 2016, though the fatal crash rate decreased slightly from 1.0% to 0.9% due to the higher overall crash volume. The proportion of serious injury crashes declined from 5.0% to 3.6% of all crashes. Conversely, the share of both minor injury and possible injury crashes increased, rising from 10% each in 2015 to 11.7% each in 2016.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.9%
→0.0%prior 1
Serious Injury4serious injury crashes3.6%
↓-20.0%prior 5
Minor Injury13minor injury crashes11.7%
↑30.0%prior 10
Possible Injury13possible injury crashes11.7%
↑30.0%prior 10
No Injury80no injury crashes72.1%
↑8.1%prior 74

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in both periods, with the count increasing from 23 in 2015 to 29 in 2016. 'Lost Control' was the second-most cited factor in both years, with its count decreasing slightly from 16 to 15. Notably, crashes attributed to reckless, erratic, or negligent driving increased from a count of 2 to 6, while crashes involving a failure to yield at an uncontrolled intersection decreased from 6 to 3.

Officer-Reported Primary Contributing Cause

Animal29 (26.1%)↑26.1%prior 23
Lost Control15 (13.5%)↓-6.3%prior 16
Operating vehicle in an reckless, erratic, careless, negligent manner6 (5.4%)
Other (explain in narrative): Other5 (4.5%)
Ran Stop Sign5 (4.5%)
Driver Distraction: Other interior distraction3 (2.7%)
Driving too fast for conditions3 (2.7%)↓-40.0%prior 5
Exceeded authorized speed3 (2.7%)
FTYROW: At uncontrolled intersection3 (2.7%)↓-50.0%prior 6
Driver Distraction: Inattentive/lost in thought3 (2.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

There was a notable shift in lighting conditions for crashes year-over-year. The proportion of crashes occurring in daylight decreased from 60% in 2015 to 50.5% in 2016. In contrast, the count of crashes on dark, unlighted roadways more than doubled from 7 to 18. This represented a proportional increase from 7% of all crashes in 2015 to 16.2% in 2016.

Weather

Clear58 (69.9%)
↑18.4%prior 49
Cloudy11 (13.3%)
↓-21.4%prior 14
Rain3 (3.6%)
Severe Winds3 (3.6%)
Snow3 (3.6%)
↓-70.0%prior 10
Blowing Snow3 (3.6%)
Fog, smoke, smog2 (2.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight56 (64.4%)
↓-6.7%prior 60
Dark - roadway not lighted18 (20.7%)
↑157.1%prior 7
Dark - roadway lighted9 (10.3%)
Dusk2 (2.3%)
Dawn1 (1.1%)
↓-83.3%prior 6
Dark - unknown roadway lighting1 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry66 (75.9%)
↑29.4%prior 51
Ice/frost6 (6.9%)
↓-40.0%prior 10
Snow6 (6.9%)
↓-14.3%prior 7
Gravel5 (5.7%)
Wet3 (3.4%)
Slush1 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

The top two vehicle makes involved in collisions swapped rankings between 2015 and 2016. Chevrolet-involved crashes increased from 22 to 31, making it the most common make, while Ford-involved crashes decreased from 29 to 22. Among persons involved in crashes, there was a significant drop in the 65+ age group, from 27 individuals in 2015 to 15 in 2016. Meanwhile, involvement increased for the 26-34 age group, from 23 to 29 persons.

Top Vehicle Makes (155 vehicles)

1
CHEVROLET31 (20%)
↑40.9%prior 22
2
FORD22 (14.2%)
↓-24.1%prior 29
3
CHEV10 (6.5%)
↑11.1%prior 9
4
DODGE9 (5.8%)
↑12.5%prior 8
5
TOYOTA7 (4.5%)
↑40.0%prior 5
6
BUICK6 (3.9%)
↑20.0%prior 5
7
GMC5 (3.2%)
8
PONTIAC5 (3.2%)
↓-28.6%prior 7
9
PTRB4 (2.6%)
10
DODG4 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (115 persons with recorded sex)

Male68 (59.1%)
↓-23.6%prior 89
Female47 (40.9%)
↑17.5%prior 40

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 10, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 111
  • Total persons involved: 171
  • Total vehicles involved: 155

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: 2016." Published September 10, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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