Yearly Traffic Safety Analysis

94 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Worth County, total traffic crashes increased by 4.4%, from 90 in 2018 to 94 in 2019. While total fatalities remained unchanged at 2 for both years, the number of reported injuries rose from 27 to 32. The most significant year-over-year change was a 68.8% increase in crashes attributed to animals, which rose from 16 incidents in 2018 to 27 in 2019.

94

▲ 4.4%was 90

Total Crash Events

2

Persons Killed

32

▲ 18.5%was 27

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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic collisions in Worth County saw a slight increase in 2019 compared to the previous year. The total number of crashes rose by 4.4%, from 90 to 94. This increase was accompanied by an 18.5% rise in total injuries, from 27 to 32, while the number of fatalities and fatal crashes held steady at 2 for both years.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 2↓-50.0%

1

Other Killed

Prior: 0→%

32

Motorists Injured

Prior: 27↑18.5%

0

Other Injured

Prior: 0→0.0%

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 timing of crashes in Worth County shifted between 2018 and 2019. The peak day for collisions moved from Friday (20 crashes) in 2018 to Wednesday (20 crashes) in 2019. A more pronounced change occurred in the peak hour of crashes, which shifted from 9 p.m. in the prior year (14 crashes) to 5 a.m. in the current year (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

The severity of crashes showed a mixed trend year-over-year. While the number of fatal crashes remained constant at two, the fatal crash rate decreased slightly from 2.2% in 2018 to 2.1% in 2019. The proportion of crashes resulting in serious injuries fell from 3.3% (3 crashes) to 1.1% (1 crash). Conversely, crashes involving minor injuries increased, rising from 8.9% of all incidents (8 crashes) in 2018 to 13.8% (13 crashes) in 2019.

Outcome by Severity (Crash Events)

Fatal2fatal crashes2.1%
→0.0%prior 2
Serious Injury1serious injury crashes1.1%
↓-66.7%prior 3
Minor Injury13minor injury crashes13.8%
↑62.5%prior 8
Possible Injury9possible injury crashes9.6%
→0.0%prior 9
No Injury69no injury crashes73.4%
↑1.5%prior 68

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

The leading contributing factors for crashes shifted between 2018 and 2019. Collisions involving an animal became the most common cause, with the count increasing by 68.8% from 16 to 27 incidents. "Driving too fast for conditions" also saw a notable rise, increasing from 14 to 18 crashes. In contrast, incidents where a driver "Lost Control" decreased from 15 to 13, and crashes from "Ran off road - straight" dropped from 10 in 2018 to just 2 in 2019.

Officer-Reported Primary Contributing Cause

Animal27 (28.7%)↑68.8%prior 16
Driving too fast for conditions18 (19.1%)↑28.6%prior 14
Lost Control13 (13.8%)↓-13.3%prior 15
Driver Distraction: Other interior distraction4 (4.3%)
Followed too close3 (3.2%)
Other (explain in narrative): No improper action3 (3.2%)
FTYROW: At uncontrolled intersection3 (3.2%)
Swerving/Evasive Action2 (2.1%)
Operating vehicle in an reckless, erratic, careless, negligent manner2 (2.1%)
Other (explain in narrative): Other2 (2.1%)

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

Road & Environmental Conditions

The environmental conditions during crashes showed some year-over-year changes. The proportion of collisions occurring in daylight increased from 45.6% in 2018 to 48.9% in 2019, while crashes in unlit dark conditions decreased from 36.7% to 26.6% of the total. Although the overall share of crashes on non-dry road surfaces remained stable at approximately 46%, incidents on snow-covered roads rose from 10 to 16. Crashes during "Blowing Snow" conditions more than doubled, from 5 in 2018 to 11 in 2019.

Weather

Clear31 (40.8%)
↓-20.5%prior 39
Cloudy17 (22.4%)
↑6.3%prior 16
Blowing Snow11 (14.5%)
↑120.0%prior 5
Rain6 (7.9%)
Freezing rain/drizzle3 (3.9%)
↓-40.0%prior 5
Snow3 (3.9%)
↓-72.7%prior 11
Severe Winds2 (2.6%)
Fog, smoke, smog1 (1.3%)
Other (explain in narrative)1 (1.3%)
Sleet, hail1 (1.3%)

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

Lighting

Daylight46 (60.5%)
↑12.2%prior 41
Dark - roadway not lighted25 (32.9%)
↓-24.2%prior 33
Dark - roadway lighted3 (3.9%)
Dawn1 (1.3%)
Dusk1 (1.3%)

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

Road Surface

Dry33 (43.4%)
↓-15.4%prior 39
Snow16 (21.1%)
↑60.0%prior 10
Ice/frost13 (17.1%)
↓-23.5%prior 17
Wet10 (13.2%)
↑42.9%prior 7
Gravel2 (2.6%)
↓-60.0%prior 5
Slush2 (2.6%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained relatively consistent year-over-year, with Ford (21 vehicles in 2019 vs. 22 in 2018) and Chevrolet variants (18 vs. 22) being the most common. A more significant shift was observed in the age distribution of persons involved in collisions. The 35-44 age group's involvement increased from 19 individuals in 2018 to 36 in 2019. In contrast, the number of people in the 55-64 age group involved in crashes was halved, dropping from 24 to 12.

Top Vehicle Makes (125 vehicles)

1
FORD21 (16.8%)
↓-4.5%prior 22
2
CHEV10 (8%)
↓-23.1%prior 13
3
VOLVO8 (6.4%)
↑14.3%prior 7
4
CHEVROLET8 (6.4%)
↓-11.1%prior 9
5
DODGE8 (6.4%)
6
FREIGHTLINER7 (5.6%)
→0.0%prior 7
7
HONDA6 (4.8%)
8
TOYOTA5 (4%)
9
GMC5 (4%)
↓-28.6%prior 7
10
KENWORTH5 (4%)

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

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

Sex Distribution (118 persons with recorded sex)

Male78 (66.1%)
↑6.8%prior 73
Female40 (33.9%)
↑37.9%prior 29

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: 94
  • Total persons involved: 181
  • Total vehicles involved: 125

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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