Yearly Traffic Safety Analysis

880 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Webster County recorded 880 total crashes, a 7.1% increase from the 822 crashes reported in 2018. While total crashes and injuries (214, up from 201) rose, the number of fatalities increased slightly from 9 to 10. One of the most significant changes was a 46.9% decrease in crashes involving driving under the influence (DUI), which fell from 32 incidents in 2018 to 17 in 2019.

880

▲ 7.1%was 822

Total Crash Events

10

▲ 11.1%was 9

Persons Killed

214

▲ 6.5%was 201

Persons Injured

9

Fatal Crash Events

Note: "Persons Killed" (10) counts individual fatalities across all crash events. "Fatal" in the severity table below (9) 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 crashes in Webster County trended upward in 2019 compared to the prior year. The total number of crashes increased by 58, from 822 to 880, representing a 7.1% rise. This increase was accompanied by a 6.5% rise in total injuries, from 201 to 214.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

10

Motorists Killed

Prior: 9↑11.1%

2

Pedestrians Injured

Prior: 6↓-66.7%

3

Cyclists Injured

Prior: 5↓-40.0%

209

Motorists Injured

Prior: 189↑10.6%

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 time patterns of crashes shifted slightly between the two periods. In 2019, the peak day for crashes was Wednesday with 149 incidents, changing from Monday (144 incidents) in 2018. The daily peak hour also shifted later, from the 3 p.m. hour in 2018 (63 crashes) to the 5 p.m. hour in 2019 (74 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

While the number of fatal crashes remained constant at 9 for both years, the fatal crash rate per 100 crashes decreased slightly from 1.09 in 2018 to 1.02 in 2019. The proportion of crashes resulting in any injury (fatal, serious, minor, or possible) saw a small decline, accounting for 22.2% of all crashes in 2019 compared to 24.7% in 2018. Notably, crashes resulting in serious injuries were nearly halved, dropping from 13 in 2018 to 6 in 2019.

Severity is per crash event (most severe injury). 9 fatal crash events resulted in 10 persons killed.

Outcome by Severity (Crash Events)

Fatal9fatal crashes1%
→0.0%prior 9
Serious Injury6serious injury crashes0.7%
↓-53.8%prior 13
Minor Injury43minor injury crashes4.9%
↓-23.2%prior 56
Possible Injury137possible injury crashes15.6%
↑9.6%prior 125
No Injury685no injury crashes77.8%
↑10.7%prior 619

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 top contributing factor in both periods was collisions with an animal, with the count increasing from 95 in 2018 to 123 in 2019. 'Driving too fast for conditions' saw a notable decrease in count, falling from 67 incidents to 53. Conversely, 'Failure to yield right of way from a stop sign' incidents increased from 43 to 52. Crashes attributed to 'Followed too close' decreased from 53 in 2018 to 43 in 2019.

Officer-Reported Primary Contributing Cause

Animal123 (14%)↑29.5%prior 95
Other (explain in narrative): Other106 (12%)↑23.3%prior 86
Driving too fast for conditions53 (6%)↓-20.9%prior 67
FTYROW: From stop sign52 (5.9%)↑20.9%prior 43
Lost Control43 (4.9%)↑10.3%prior 39
Followed too close43 (4.9%)↓-18.9%prior 53
Ran off road - left37 (4.2%)↓-7.5%prior 40
FTYROW: Making left turn29 (3.3%)↑26.1%prior 23
Ran off road - straight29 (3.3%)↑52.6%prior 19
Driver Distraction: Other interior distraction28 (3.2%)↑7.7%prior 26

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

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with most incidents in both periods occurring in daylight (61.7% in 2019 vs. 64.5% in 2018) and on dry roads (51.6% vs. 54.6%). There was a slight increase in the proportion of crashes occurring in dark conditions, from 19.0% in 2018 to 20.7% in 2019. The number of crashes on snow-covered roads increased from 80 to 113, while crashes on icy or frosty roads decreased from 82 to 78.

Weather

Clear469 (61.5%)
↑1.7%prior 461
Cloudy184 (24.1%)
↑17.9%prior 156
Rain42 (5.5%)
↑35.5%prior 31
Snow33 (4.3%)
↓-32.7%prior 49
Blowing Snow13 (1.7%)
↑62.5%prior 8
Freezing rain/drizzle13 (1.7%)
↓-23.5%prior 17
Sleet, hail3 (0.4%)
Fog, smoke, smog3 (0.4%)
↓-40.0%prior 5
Severe Winds2 (0.3%)
Other (explain in narrative)1 (0.1%)

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

Lighting

Daylight543 (71.1%)
↑2.5%prior 530
Dark - roadway not lighted101 (13.2%)
↑50.7%prior 67
Dark - roadway lighted81 (10.6%)
↓-9.0%prior 89
Dusk23 (3.0%)
↑21.1%prior 19
Dawn13 (1.7%)
↓-38.1%prior 21
Dark - unknown roadway lighting3 (0.4%)

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

Road Surface

Dry454 (59.4%)
↑1.1%prior 449
Snow113 (14.8%)
↑41.3%prior 80
Wet105 (13.7%)
↑11.7%prior 94
Ice/frost78 (10.2%)
↓-4.9%prior 82
Slush7 (0.9%)
↓-41.7%prior 12
Gravel5 (0.7%)
↓-50.0%prior 10
Mud, dirt1 (0.1%)
Sand1 (0.1%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes were consistent across both years, with Chevrolet and Ford vehicles accounting for the highest volumes in both 2019 and 2018. The age distribution of persons involved in crashes also showed little change; for example, the 16-20 age group represented 12.7% of persons in 2019, compared to 13.6% in 2018. The proportion of male and female persons involved also remained stable.

Top Vehicle Makes (1,503 vehicles)

1
CHEV258 (17.2%)
↑21.7%prior 212
2
FORD248 (16.5%)
↑15.9%prior 214
3
DODG90 (6%)
↑9.8%prior 82
4
CHEVROLET88 (5.9%)
↑14.3%prior 77
5
GMC65 (4.3%)
↓-4.4%prior 68
6
NR62 (4.1%)
↓-4.6%prior 65
7
TOYO60 (4%)
↓-1.6%prior 61
8
JEEP54 (3.6%)
↑17.4%prior 46
9
CHRY51 (3.4%)
↓-21.5%prior 65
10
BUIC48 (3.2%)
↓-33.3%prior 72

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

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

Sex Distribution (1,298 persons with recorded sex)

Male672 (51.8%)
↑15.3%prior 583
Female626 (48.2%)
↑29.1%prior 485

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: 880
  • Total persons involved: 2,013
  • Total vehicles involved: 1,503

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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