Yearly Traffic Safety Analysis

78 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Butler County, the total number of crashes remained unchanged year-over-year, with 78 incidents reported in both 2019 and 2018. While overall crash volume was stable and fatalities remained at zero, the number of people injured increased from 33 to 35. Notably, crashes attributed to driving under the influence doubled, increasing from 2 in 2018 to 4 in 2019.

78

Total Crash Events

0

Persons Killed

35

▲ 6.1%was 33

Persons Injured

0

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall crash trend in Butler County was stable between 2018 and 2019, with a total of 78 crashes recorded in each year. While the number of fatal crashes remained at zero for both periods, the number of people injured in collisions saw a slight increase of 6.1%, rising from 33 in 2018 to 35 in 2019.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

35

Motorists Injured

Prior: 33↑6.1%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed some shifts between the two periods. In 2019, the peak days for crashes were Tuesday and Wednesday, each with 14 incidents, a change from 2018 when Tuesday was the sole peak day with 17 crashes. The peak hour for collisions also shifted later in the day, moving from 3 p.m. in 2018 (10 crashes) to 5 p.m. in 2019 (9 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity levels remained largely consistent year-over-year, with no fatal crashes reported in either 2019 or 2018. The number of serious injury crashes was unchanged at 4 in both periods, as was the count for possible injury crashes at 11. The number of minor injury crashes decreased slightly from 14 in 2018 to 12 in 2019, while crashes resulting in no injury increased from 49 to 51.

Outcome by Severity (Crash Events)

Serious Injury4serious injury crashes5.1%
→0.0%prior 4
Minor Injury12minor injury crashes15.4%
↓-14.3%prior 14
Possible Injury11possible injury crashes14.1%
→0.0%prior 11
No Injury51no injury crashes65.4%
↑4.1%prior 49

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals were the leading contributing factor in both years, with the count increasing by 42.9% from 14 crashes in 2018 to 20 in 2019; this raised its share of all crashes from 17.9% to 25.6%. Conversely, crashes attributed to 'Lost Control' saw a significant decrease in count, falling from 11 incidents in 2018 to 4 in 2019. The number of crashes due to failure to yield from a stop sign doubled, rising from 3 to 6 year-over-year.

Officer-Reported Primary Contributing Cause

Animal20 (25.6%)↑42.9%prior 14
Ran off road - straight7 (9%)
FTYROW: From stop sign6 (7.7%)
Ran off road - left5 (6.4%)
Other (explain in narrative): Other5 (6.4%)
Driving too fast for conditions4 (5.1%)↓-20.0%prior 5
Lost Control4 (5.1%)↓-63.6%prior 11
Followed too close3 (3.8%)
Made improper turn2 (2.6%)
FTYROW: From driveway2 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions decreased from 49 in 2018 to 34 in 2019. Concurrently, incidents during adverse weather saw an increase, with crashes in freezing rain or drizzle rising from 1 to 6. Collisions on non-dry road surfaces, including ice, snow, and wet roads, increased from a combined 20 incidents in 2018 to 24 in 2019. There was also an increase in crashes occurring in darkness on unlighted roadways, which rose from 9 to 13 incidents.

Weather

Clear34 (56.7%)
↓-30.6%prior 49
Cloudy8 (13.3%)
↑60.0%prior 5
Freezing rain/drizzle6 (10.0%)
Rain5 (8.3%)
Snow5 (8.3%)
Blowing Snow2 (3.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight43 (71.7%)
↓-10.4%prior 48
Dark - roadway not lighted13 (21.7%)
↑44.4%prior 9
Dark - roadway lighted3 (5.0%)
Dusk1 (1.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry35 (58.3%)
↓-7.9%prior 38
Ice/frost10 (16.7%)
↑25.0%prior 8
Snow7 (11.7%)
↑40.0%prior 5
Wet7 (11.7%)
↑40.0%prior 5
Gravel1 (1.7%)
↓-85.7%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

Chevrolet and Ford remained the top two vehicle makes involved in crashes in both periods, with their counts staying relatively stable; 28 Chevrolets and 20 Fords were involved in 2019, compared to 28 Chevrolets and 23 Fords in 2018. An analysis of persons involved in crashes reveals a significant increase in the 45-54 age group, which grew from 10 individuals in 2018 to 24 in 2019. The number of people aged 65 and older involved in crashes also increased from 13 to 20.

Top Vehicle Makes (114 vehicles)

1
CHEV28 (24.6%)
↑100.0%prior 14
2
FORD20 (17.5%)
↓-13.0%prior 23
3
DODG6 (5.3%)
4
NISS5 (4.4%)
5
TOYT5 (4.4%)
→0.0%prior 5
6
CHEVROLET4 (3.5%)
↓-71.4%prior 14
7
GMC4 (3.5%)
8
PONT4 (3.5%)
9
BUIC3 (2.6%)
10
JEEP3 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (109 persons with recorded sex)

Male58 (53.2%)
↑11.5%prior 52
Female51 (46.8%)
↑34.2%prior 38

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 78
  • Total persons involved: 169
  • Total vehicles involved: 114

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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