Yearly Traffic Safety Analysis

81 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Butler County, total traffic crashes increased by 17.4%, from 69 in 2015 to 81 in 2016. The most significant year-over-year change was the increase in crash severity, with total fatalities rising from 1 to 4 and total injuries increasing from 27 to 47.

81

▲ 17.4%was 69

Total Crash Events

4

▲ 300.0%was 1

Persons Killed

47

▲ 74.1%was 27

Persons Injured

2

▲ 100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (4) 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 · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data indicates a rising trend in Butler County from 2015 to 2016. The total number of crashes grew by 17.4% from 69 to 81 incidents. This increase was accompanied by a more pronounced rise in severity, as total injuries surged by 74.1% and fatalities quadrupled from 1 to 4.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 1↑300.0%

1

Cyclists Injured

Prior: 0→%

46

Motorists Injured

Prior: 27↑70.4%

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 years. In 2016, Monday was the peak day with 14 crashes, a change from 2015 when Friday was the most frequent day with 16 crashes. The peak hour for collisions also moved slightly later, from the 3 PM hour in 2015 (14 crashes) to the 4 PM hour in 2016 (11 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

Crash severity worsened in 2016 compared to the prior year. The number of fatal crashes doubled from 1 to 2, and total fatalities increased from 1 to 4. The number of serious injury crashes also saw a substantial increase, rising from 1 in 2015 to 6 in 2016. Consequently, the share of crashes resulting in no injuries decreased from 68.1% in 2015 to 60.5% in 2016.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes2.5%
↑100.0%prior 1
Serious Injury6serious injury crashes7.4%
↑500.0%prior 1
Minor Injury8minor injury crashes9.9%
↑14.3%prior 7
Possible Injury16possible injury crashes19.8%
↑23.1%prior 13
No Injury49no injury crashes60.5%
↑4.3%prior 47

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

"Animal" became the leading contributing factor in 2016 with 14 crashes, replacing 2015's top factor, "Driving too fast for conditions," which had 9 crashes. The count of crashes involving an animal increased by 180%, from 5 in 2015 to 14 in 2016. Conversely, crashes attributed to "Driving too fast for conditions" decreased by 44.4% in count, from 9 to 5. Notably, incidents of "Failure to yield from a stop sign" increased from 1 to 8 year-over-year.

Officer-Reported Primary Contributing Cause

Animal14 (17.3%)↑180.0%prior 5
Lost Control10 (12.3%)↑66.7%prior 6
FTYROW: From stop sign8 (9.9%)
Driving too fast for conditions5 (6.2%)↓-44.4%prior 9
Followed too close4 (4.9%)
Driver Distraction: Other interior distraction4 (4.9%)
Operating vehicle in an reckless, erratic, careless, negligent manner4 (4.9%)↓-20.0%prior 5
Ran off road - straight3 (3.7%)↓-50.0%prior 6
Ran Stop Sign3 (3.7%)
FTYROW: From parked position2 (2.5%)

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

Road & Environmental Conditions

In both 2015 and 2016, the vast majority of crashes occurred in clear weather and on dry road surfaces. Crashes under clear conditions were nearly identical, with 48 in 2015 and 49 in 2016. There was a notable decrease in crashes on snow or ice-covered roads, which fell from a combined 8 incidents in 2015 to 6 in 2016. Crashes during daylight hours were most frequent in both periods, with 43 in 2015 and 41 in 2016.

Weather

Clear49 (70.0%)
↑2.1%prior 48
Cloudy12 (17.1%)
↑100.0%prior 6
Rain5 (7.1%)
Fog, smoke, smog2 (2.9%)
Freezing rain/drizzle1 (1.4%)
Blowing Snow1 (1.4%)

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

Lighting

Daylight41 (57.7%)
↓-4.7%prior 43
Dark - roadway not lighted15 (21.1%)
↓-6.3%prior 16
Dark - roadway lighted8 (11.3%)
↑60.0%prior 5
Dusk4 (5.6%)
Dawn2 (2.8%)
Dark - unknown roadway lighting1 (1.4%)

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

Road Surface

Dry54 (76.1%)
↑20.0%prior 45
Wet7 (9.9%)
↑40.0%prior 5
Gravel4 (5.6%)
↓-42.9%prior 7
Snow4 (5.6%)
Ice/frost2 (2.8%)

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

Vehicles & Demographics

A shift was observed in the most common vehicle makes involved in crashes; Ford became the top make with 28 vehicles in 2016, up from 18 in 2015, while Chevrolet decreased from 32 to 24. The age demographics of persons involved in crashes also changed, with a significant increase in the 55-64 age group from 9 individuals in 2015 to 25 in 2016. The 16-20 age group remained one of the most represented demographics, with 24 individuals in 2015 and 23 in 2016.

Top Vehicle Makes (123 vehicles)

1
FORD28 (22.8%)
↑55.6%prior 18
2
CHEVROLET17 (13.8%)
↓-37.0%prior 27
3
CHEV7 (5.7%)
↑40.0%prior 5
4
TOYOTA6 (4.9%)
5
DODGE6 (4.9%)
↓-50.0%prior 12
6
GMC6 (4.9%)
7
DODG5 (4.1%)
8
BUICK4 (3.3%)
9
CHRY4 (3.3%)
10
FREIGHTLINER3 (2.4%)

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

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

Sex Distribution (85 persons with recorded sex)

Male48 (56.5%)
↓-33.3%prior 72
Female37 (43.5%)
↑23.3%prior 30

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: 81
  • Total persons involved: 146
  • Total vehicles involved: 123

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

ThatCarHitMe.com · An Injuria.ai Company