Yearly Traffic Safety Analysis

103 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Hancock County recorded 103 total crashes, an 11% increase from the 93 crashes reported in 2018. While overall collisions rose, the number of fatalities dropped from two in the prior year to zero in the current period. The total number of injuries remained relatively stable, decreasing slightly from 58 to 54.

103

▲ 10.8%was 93

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

54

▼ -6.9%was 58

Persons Injured

0

▼ -100.0%was 2

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

Traffic crashes in Hancock County showed an upward trend, increasing by 10.8% from 93 incidents in 2018 to 103 in 2019. Despite the rise in total collisions, the outcomes were less severe on average. The number of injuries decreased slightly from 58 to 54, and fatalities fell from two to zero year-over-year.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 2↓-100.0%

1

Cyclists Injured

Prior: 0→%

53

Motorists Injured

Prior: 57↓-7.0%

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 shifted between the two periods. In 2019, the peak day for crashes was Thursday with 23 incidents, a significant change from 2018 when Wednesday and Friday were the peak days with 16 crashes each. The afternoon commute hour of 3 p.m. remained the consistent peak time for collisions in both years, with 12 crashes in 2019 and 11 in 2018. A notable monthly shift occurred in February, which saw a surge from 6 crashes in 2018 to 22 in 2019.

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 decreased notably in 2019 compared to the previous year. There were no fatal crashes recorded in 2019, a significant improvement from the two fatal crashes in 2018. The number of serious injury crashes remained constant at four in both periods. However, there was a shift in lower-level injury crashes, with minor injury crashes decreasing from 19 to 15, while possible injury crashes increased from 13 to 24.

Outcome by Severity (Crash Events)

Serious Injury4serious injury crashes3.9%
→0.0%prior 4
Minor Injury15minor injury crashes14.6%
↓-21.1%prior 19
Possible Injury24possible injury crashes23.3%
↑84.6%prior 13
No Injury60no injury crashes58.3%
↑9.1%prior 55

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

In both 2019 and 2018, 'Driving too fast for conditions' was the leading contributing factor, with its count increasing from 16 to 23 crashes year-over-year. Crashes involving failure to yield at an uncontrolled intersection also saw a notable rise, from 5 incidents in 2018 to 9 in 2019. Conversely, crashes attributed to 'Lost Control' decreased from 9 to 6, and incidents involving failure to yield from a stop sign dropped from 6 to 2.

Officer-Reported Primary Contributing Cause

Driving too fast for conditions23 (22.3%)↑43.8%prior 16
FTYROW: At uncontrolled intersection9 (8.7%)↑80.0%prior 5
Lost Control6 (5.8%)↓-33.3%prior 9
Animal5 (4.9%)
Driver Distraction: Other interior distraction5 (4.9%)→0.0%prior 5
Other (explain in narrative): Other4 (3.9%)↓-20.0%prior 5
Followed too close4 (3.9%)
Ran off road - straight4 (3.9%)↓-42.9%prior 7
FTYROW: Making left turn4 (3.9%)
Crossed centerline (undivided)3 (2.9%)

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 adverse road conditions increased significantly in 2019. Collisions on roads with ice or frost surged from 5 incidents in 2018 to 23 in 2019, and crashes in snowy weather conditions doubled from 9 to 18. Despite this, the majority of crashes in both years occurred in clear weather (66 in 2019, 53 in 2018) and during daylight hours (75 in 2019, 60 in 2018).

Weather

Clear66 (64.1%)
↑24.5%prior 53
Cloudy11 (10.7%)
↓-35.3%prior 17
Snow10 (9.7%)
↑100.0%prior 5
Blowing Snow8 (7.8%)
Rain4 (3.9%)
Freezing rain/drizzle2 (1.9%)
Severe Winds2 (1.9%)

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

Lighting

Daylight75 (72.8%)
↑25.0%prior 60
Dark - roadway not lighted22 (21.4%)
↑15.8%prior 19
Dark - roadway lighted2 (1.9%)
↓-71.4%prior 7
Dawn2 (1.9%)
Dusk2 (1.9%)

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

Road Surface

Dry53 (51.5%)
↑3.9%prior 51
Ice/frost23 (22.3%)
↑360.0%prior 5
Snow14 (13.6%)
↑27.3%prior 11
Wet8 (7.8%)
↓-20.0%prior 10
Gravel3 (2.9%)
↓-70.0%prior 10
Mud, dirt1 (1.0%)
Slush1 (1.0%)

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 consistent, with Ford and Chevrolet being the most common in both 2019 and 2018. An analysis of persons involved in crashes shows a shift in age demographics. The proportion of individuals in the 16-20 age group increased from 12.7% of all persons in 2018 to 16.1% in 2019. Conversely, the involvement of the 65+ age group decreased from 15.2% to 12.2% over the same period.

Top Vehicle Makes (164 vehicles)

1
FORD32 (19.5%)
↑23.1%prior 26
2
CHEV23 (14%)
↑4.5%prior 22
3
DODG9 (5.5%)
↓-30.8%prior 13
4
CHEVROLET9 (5.5%)
↓-25.0%prior 12
5
GMC7 (4.3%)
↑16.7%prior 6
6
HOND6 (3.7%)
7
DODGE6 (3.7%)
↓-14.3%prior 7
8
FREIGHTLINER4 (2.4%)
9
RAM3 (1.8%)
10
CHRYSLER3 (1.8%)

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

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

Sex Distribution (152 persons with recorded sex)

Male96 (63.2%)
↑29.7%prior 74
Female56 (36.8%)
↑43.6%prior 39

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: 103
  • Total persons involved: 229
  • Total vehicles involved: 164

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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