Yearly Traffic Safety Analysis

354 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Hamilton County recorded 354 total crashes, a slight increase of 0.9% from the 351 crashes in 2015. While total crashes and fatalities remained stable, the number of crashes involving a driver under the influence of alcohol (DUI) increased by 85.7%, from 7 incidents in 2015 to 13 in 2016.

354

▲ 0.9%was 351

Total Crash Events

5

Persons Killed

133

▼ -8.9%was 146

Persons Injured

5

▲ 25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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

Overall crash trends in Hamilton County remained relatively stable year-over-year, with a minor increase of just 3 incidents from 351 in 2015 to 354 in 2016. Despite the stable crash volume, the number of people injured in these incidents decreased by 8.9% from 146 to 133. The number of fatalities held steady at 5 for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

1

Cyclists Killed

Prior: 0→%

4

Motorists Killed

Prior: 4→0.0%

2

Pedestrians Injured

Prior: 0→%

0

Cyclists Injured

Prior: 0→0.0%

131

Motorists Injured

Prior: 146↓-10.3%

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 timing of crashes shifted between the two periods. In 2016, the peak day for crashes was Friday with 65 incidents, a change from Thursday (65 incidents) in the prior year. The peak hour for crashes moved significantly, from the 4 p.m. hour in 2015 (38 crashes) to the 10 a.m. hour in 2016 (27 crashes).

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 severity of crashes saw some changes year-over-year. The number of fatal crashes increased from 4 to 5, raising the fatal crash rate from 1.14% to 1.41% of all crashes. The proportion of crashes resulting in serious injuries decreased slightly from 2.6% to 2.3%, while crashes with no injuries increased as a share of the total, rising from 68.7% in 2015 to 70.9% in 2016.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.4%
↑25.0%prior 4
Serious Injury8serious injury crashes2.3%
↓-11.1%prior 9
Minor Injury37minor injury crashes10.5%
↑12.1%prior 33
Possible Injury53possible injury crashes15%
↓-17.2%prior 64
No Injury251no injury crashes70.9%
↑4.1%prior 241

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 years, though the count of such incidents decreased from 82 in 2015 to 70 in 2016. The most significant change was in crashes attributed to "Driving too fast for conditions," which saw a 61.3% increase in count from 31 to 50 incidents, moving it from the third to the second most common factor. The count of crashes due to "Followed too close" also doubled, increasing from 11 to 22 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal70 (19.8%)↓-14.6%prior 82
Driving too fast for conditions50 (14.1%)↑61.3%prior 31
Lost Control38 (10.7%)↓-5.0%prior 40
Ran off road - straight32 (9%)↑14.3%prior 28
Followed too close22 (6.2%)↑100.0%prior 11
FTYROW: From stop sign15 (4.2%)↓-21.1%prior 19
Other (explain in narrative): Other14 (4%)↑7.7%prior 13
FTYROW: At uncontrolled intersection10 (2.8%)→0.0%prior 10
Ran off road - left10 (2.8%)↓-41.2%prior 17
Driver Distraction: Other interior distraction10 (2.8%)↑11.1%prior 9

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

Road & Environmental Conditions

Crashes occurring during adverse conditions increased in 2016 compared to 2015. The number of crashes on roads with ice or frost rose from 27 to 45, and collisions in snowy weather conditions (including blowing snow) increased from 41 to 53. Crashes in dark, unlighted conditions also saw an increase, rising from 38 incidents in 2015 to 53 in 2016.

Weather

Clear149 (51.2%)
↓-8.0%prior 162
Cloudy57 (19.6%)
↑1.8%prior 56
Snow31 (10.7%)
↓-20.5%prior 39
Blowing Snow22 (7.6%)
Rain12 (4.1%)
↓-7.7%prior 13
Severe Winds8 (2.7%)
Fog, smoke, smog6 (2.1%)
Freezing rain/drizzle5 (1.7%)
↓-44.4%prior 9
Sleet, hail1 (0.3%)

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

Lighting

Daylight193 (66.3%)
↓-2.5%prior 198
Dark - roadway not lighted53 (18.2%)
↑39.5%prior 38
Dark - roadway lighted25 (8.6%)
↑4.2%prior 24
Dusk10 (3.4%)
↓-9.1%prior 11
Dawn9 (3.1%)
↑50.0%prior 6
Dark - unknown roadway lighting1 (0.3%)
↓-80.0%prior 5

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

Road Surface

Dry172 (58.9%)
↓-8.5%prior 188
Ice/frost45 (15.4%)
↑66.7%prior 27
Snow37 (12.7%)
↑27.6%prior 29
Wet24 (8.2%)
↓-22.6%prior 31
Gravel7 (2.4%)
Slush5 (1.7%)
Other (explain in narrative)1 (0.3%)
Mud, dirt1 (0.3%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford and Chevrolet vehicles accounting for the highest volumes in both 2015 and 2016. Ford vehicles were involved in 79 crashes in 2016, down from 83 the previous year. An analysis of persons involved in crashes shows a decrease in the representation of the 65+ age group, which fell from involving 74 individuals in 2015 to 50 in 2016.

Top Vehicle Makes (523 vehicles)

1
FORD79 (15.1%)
↓-4.8%prior 83
2
CHEVROLET75 (14.3%)
↑27.1%prior 59
3
CHEV49 (9.4%)
↓-18.3%prior 60
4
TOYOTA23 (4.4%)
↑15.0%prior 20
5
DODGE22 (4.2%)
↑57.1%prior 14
6
GMC22 (4.2%)
↑29.4%prior 17
7
DODG16 (3.1%)
↓-33.3%prior 24
8
PETERBILT14 (2.7%)
9
FREIGHTLINER12 (2.3%)
↑50.0%prior 8
10
PONTIAC11 (2.1%)
↑37.5%prior 8

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

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

Sex Distribution (375 persons with recorded sex)

Male249 (66.4%)
↓-8.1%prior 271
Female126 (33.6%)
↓-28.0%prior 175

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 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 354
  • Total persons involved: 610
  • Total vehicles involved: 523

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 9, 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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