Yearly Traffic Safety Analysis

57 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Winnebago County recorded 57 total crashes, a 29.5% increase from the 44 crashes in 2018. While total injuries rose from 29 to 39, the county saw a decrease in fatalities from one in the prior year to zero. The most significant shift was a 200% increase in the count of crashes attributed to 'Driving too fast for conditions,' which rose from 3 to 9 incidents.

57

▲ 29.5%was 44

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

39

▲ 34.5%was 29

Persons Injured

0

▼ -100.0%was 1

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

Crash data for Winnebago County indicates a rising trend year-over-year. Total collisions increased by 29.5%, from 44 in 2018 to 57 in 2019. This was accompanied by a 34.5% increase in persons injured, from 29 to 39, although the single fatality from 2018 was not repeated in 2019.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 1↓-100.0%

1

Pedestrians Injured

Prior: 0→%

38

Motorists Injured

Prior: 29↑31.0%

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 temporal patterns of crashes showed some changes between the two periods. While Wednesday remained the peak day for crashes in both 2018 (9 crashes) and 2019 (13 crashes), the peak hour shifted. In 2018, the most crashes occurred at 8 a.m. with 4 incidents, whereas in 2019 the peak moved to 2 p.m. with 6 incidents.

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

While total crashes increased, their severity profile shifted. The county recorded zero fatal crashes in 2019, down from one fatal crash in 2018, which had accounted for 2.3% of that year's total. The proportion of crashes resulting in any level of injury remained stable at 38.6% in both years, though the absolute number of injury-causing crashes rose from 17 to 22. Crashes resulting in serious injuries increased from 2 in 2018 to 3 in 2019.

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes5.3%
↑50.0%prior 2
Minor Injury9minor injury crashes15.8%
↑12.5%prior 8
Possible Injury10possible injury crashes17.5%
↑42.9%prior 7
No Injury35no injury crashes61.4%
↑34.6%prior 26

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 changed significantly year-over-year. In 2019, 'Driving too fast for conditions' was the primary factor, cited in 9 crashes, a 200% increase in count from just 3 incidents in 2018. This factor rose from a tie for third to the top-ranked cause. Conversely, 'Ran off road - straight,' the top factor in 2018 with 5 crashes, decreased to 2 crashes in 2019. Crashes involving an animal also doubled from 3 to 6 incidents.

Officer-Reported Primary Contributing Cause

Driving too fast for conditions9 (15.8%)
Lost Control6 (10.5%)
Animal6 (10.5%)
FTYROW: From stop sign4 (7%)
Made improper turn4 (7%)
Swerving/Evasive Action3 (5.3%)
Followed too close2 (3.5%)
FTYROW: At uncontrolled intersection2 (3.5%)
Other (explain in narrative): Other2 (3.5%)
Ran off road - straight2 (3.5%)↓-60.0%prior 5

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

Road & Environmental Conditions

There was a notable shift in the road surface conditions reported during crashes. The share of collisions on dry roads decreased from 45.5% in 2018 to 33.3% in 2019. Concurrently, crashes occurring on adverse surfaces like snow, ice, or wet pavement increased, accounting for 52.6% of all incidents in 2019, up from 43.2% in the prior year. The distribution of crashes by lighting conditions remained largely consistent, with daylight hours seeing the majority of incidents in both years.

Weather

Clear28 (52.8%)
↑27.3%prior 22
Cloudy11 (20.8%)
↑22.2%prior 9
Snow6 (11.3%)
Rain3 (5.7%)
Fog, smoke, smog2 (3.8%)
Freezing rain/drizzle1 (1.9%)
Blowing Snow1 (1.9%)
Severe Winds1 (1.9%)

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

Lighting

Daylight35 (66.0%)
↑45.8%prior 24
Dark - roadway not lighted9 (17.0%)
→0.0%prior 9
Dark - roadway lighted6 (11.3%)
Dawn2 (3.8%)
Dusk1 (1.9%)

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

Road Surface

Dry19 (35.8%)
↓-5.0%prior 20
Snow11 (20.8%)
↑37.5%prior 8
Wet9 (17.0%)
Ice/frost9 (17.0%)
↑28.6%prior 7
Gravel4 (7.5%)
Slush1 (1.9%)

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

Vehicles & Demographics

Ford was the most frequent vehicle make involved in crashes in both years, accounting for 20 vehicles in 2019 and 21 in 2018. The involvement of Chevrolet vehicles increased from 11 in 2018 to 15 in 2019. A significant demographic shift occurred among persons involved in crashes, as the number of individuals in the 65-and-older age group grew from 9 in 2018 to 23 in 2019, an increase of over 155%.

Top Vehicle Makes (91 vehicles)

1
FORD20 (22%)
↓-4.8%prior 21
2
CHEV11 (12.1%)
↑83.3%prior 6
3
PONT6 (6.6%)
4
BUIC4 (4.4%)
5
INTERNATIONA4 (4.4%)
6
JEEP4 (4.4%)
7
CHEVROLET4 (4.4%)
↓-20.0%prior 5
8
GMC4 (4.4%)
9
DODG3 (3.3%)
10
TOYT2 (2.2%)

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

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

Sex Distribution (85 persons with recorded sex)

Male47 (55.3%)
↑46.9%prior 32
Female38 (44.7%)
↑58.3%prior 24

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: 57
  • Total persons involved: 139
  • Total vehicles involved: 91

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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