Yearly Traffic Safety Analysis

91 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Butler County recorded 91 total crashes, a 12.3% increase from the 81 crashes reported in 2016. Despite the rise in total incidents, the number of fatalities decreased from 4 to 2. The most significant year-over-year change was a 114% increase in the count of crashes attributed to animals, which rose from 14 incidents in 2016 to 30 in 2017.

91

▲ 12.3%was 81

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

39

▼ -17.0%was 47

Persons Injured

2

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash volume in Butler County showed an upward trend, increasing by 12.3% from 81 incidents in 2016 to 91 in 2017. However, the outcomes of these crashes were less severe on average, with total injuries falling by 17% from 47 to 39 and total fatalities dropping by 50% from 4 to 2 over the same period.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 4↓-50.0%

39

Motorists Injured

Prior: 46↓-15.2%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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, with the most frequent day for crashes moving from Monday (14 crashes) in 2016 to Friday (17 crashes) in 2017. The 4 p.m. hour was a peak time in both periods, but 2017 saw an additional peak during the 5 p.m. hour, with both hours recording 10 crashes each, compared to a single peak hour at 4 p.m. in 2016 with 11 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The overall severity of crashes decreased from 2016 to 2017, with the fatal crash rate falling from 2.47% to 2.2%. Although the number of fatal crashes remained constant at two, the proportion of crashes resulting in no injuries increased from 60.5% in 2016 to 68.1% in 2017. Concurrently, the share of crashes involving serious or possible injuries declined year-over-year.

Outcome by Severity (Crash Events)

Fatal2fatal crashes2.2%
→0.0%prior 2
Serious Injury5serious injury crashes5.5%
↓-16.7%prior 6
Minor Injury9minor injury crashes9.9%
↑12.5%prior 8
Possible Injury13possible injury crashes14.3%
↓-18.8%prior 16
No Injury62no injury crashes68.1%
↑26.5%prior 49

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor in 2017 was 'Animal,' cited in 30 crashes, which is more than double the 14 incidents from 2016—a 114% increase in count. This factor's share of all crashes grew from 17.3% to 33%. 'Lost Control' remained a significant factor with 10 crashes reported in both years. Crashes involving 'Failure to Yield Right of Way from a stop sign' decreased by 50%, falling from 8 incidents in 2016 to 4 in 2017.

Officer-Reported Primary Contributing Cause

Animal30 (33%)↑114.3%prior 14
Lost Control10 (11%)→0.0%prior 10
Driving too fast for conditions7 (7.7%)↑40.0%prior 5
FTYROW: From stop sign4 (4.4%)↓-50.0%prior 8
Ran off road - straight4 (4.4%)
Driver Distraction: Inattentive/lost in thought4 (4.4%)
Ran Stop Sign3 (3.3%)
Exceeded authorized speed3 (3.3%)
Followed too close3 (3.3%)
Driver Distraction: Other interior distraction2 (2.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring on dry road surfaces decreased from 66.7% in 2016 to 49.5% in 2017. Crashes on roads with ice or frost increased their share from 2.5% to 5.5% of the total. The share of crashes happening in daylight conditions remained stable at approximately 50.6% for both years, while crashes in clear weather made up a smaller proportion of the total in 2017 (44%) compared to 2016 (60.5%).

Weather

Clear40 (61.5%)
↓-18.4%prior 49
Cloudy10 (15.4%)
↓-16.7%prior 12
Rain5 (7.7%)
→0.0%prior 5
Fog, smoke, smog4 (6.2%)
Snow2 (3.1%)
Other (explain in narrative)1 (1.5%)
Freezing rain/drizzle1 (1.5%)
Severe Winds1 (1.5%)
Blowing Snow1 (1.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight46 (70.8%)
↑12.2%prior 41
Dark - roadway not lighted10 (15.4%)
↓-33.3%prior 15
Dawn3 (4.6%)
Dark - roadway lighted3 (4.6%)
↓-62.5%prior 8
Dusk2 (3.1%)
Dark - unknown roadway lighting1 (1.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry45 (69.2%)
↓-16.7%prior 54
Wet8 (12.3%)
↑14.3%prior 7
Ice/frost5 (7.7%)
Gravel3 (4.6%)
Snow3 (4.6%)
Slush1 (1.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford, Chevrolet, and Dodge leading in both 2016 and 2017. A notable shift occurred in the age demographics of people involved in crashes, with younger age groups becoming more represented in 2017; the 16-20 age group count increased from 23 to 27. Conversely, the involvement of older individuals decreased, with the 55-64 and 65+ age groups seeing their counts fall from 25 and 23 respectively in 2016, to 15 each in 2017.

Top Vehicle Makes (132 vehicles)

1
FORD30 (22.7%)
↑7.1%prior 28
2
CHEVROLET14 (10.6%)
↓-17.6%prior 17
3
CHEV13 (9.8%)
↑85.7%prior 7
4
DODG10 (7.6%)
↑100.0%prior 5
5
DODGE9 (6.8%)
↑50.0%prior 6
6
PONT4 (3%)
7
GMC4 (3%)
↓-33.3%prior 6
8
JEEP4 (3%)
9
BUIC3 (2.3%)
10
PONTIAC3 (2.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (93 persons with recorded sex)

Male59 (63.4%)
↑22.9%prior 48
Female34 (36.6%)
↓-8.1%prior 37

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 91
  • Total persons involved: 162
  • Total vehicles involved: 132

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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