Yearly Traffic Safety Analysis

112 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Hancock County, total vehicle crashes increased by 14.3% from 98 in 2021 to 112 in 2022. Despite this rise in collisions, the number of people injured decreased by 29.6%, falling from 54 to 38. The number of fatalities remained unchanged, with one person killed in a crash in both periods. A notable shift occurred in the conditions under which crashes happened, with a higher proportion of incidents in 2022 taking place on snowy or icy roads.

112

▲ 14.3%was 98

Total Crash Events

1

Persons Killed

38

▼ -29.6%was 54

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crashes in Hancock County trended upward in 2022, increasing to 112 from 98 in the prior year. However, the severity of these incidents decreased, as total injuries dropped from 54 to 38. The number of fatal crashes and resulting fatalities held steady at one for both years.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 1→0.0%

38

Motorists Injured

Prior: 54↓-29.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted significantly between the two periods. In 2022, Monday was the most frequent day for crashes with 26 incidents, a change from 2021 when Thursday saw the most crashes (19). The peak hour for collisions also moved earlier in the day, from 5 p.m. in 2021 (8 crashes) to 3 p.m. in 2022, which saw a spike of 17 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes increased, their severity generally lessened year-over-year. The fatal crash rate per 100 collisions declined from 1.02 to 0.89. The proportion of crashes resulting in serious injuries also fell, from 5.1% (5 crashes) in 2021 to 3.6% (4 crashes) in 2022. Conversely, the share of crashes with no injuries increased from 62.2% to 66.1% of all incidents.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.9%
→0.0%prior 1
Serious Injury4serious injury crashes3.6%
↓-20.0%prior 5
Minor Injury13minor injury crashes11.6%
↑62.5%prior 8
Possible Injury20possible injury crashes17.9%
↓-13.0%prior 23
No Injury74no injury crashes66.1%
↑21.3%prior 61

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes shifted between 2021 and 2022. In 2022, the top cause was 'Lost Control' with 11 crashes, an increase from 7 crashes the previous year. 'Driving too fast for conditions' also saw a notable increase, rising from 7 to 10 incidents. Conversely, crashes attributed to 'Followed too close' decreased from 7 in 2021 to 5 in 2022.

Officer-Reported Primary Contributing Cause

Lost Control11 (9.8%)↑57.1%prior 7
Driving too fast for conditions10 (8.9%)↑42.9%prior 7
Ran off road - left9 (8%)↑80.0%prior 5
Driver Distraction: Other interior distraction8 (7.1%)↑33.3%prior 6
Other (explain in narrative): Other8 (7.1%)↓-27.3%prior 11
Animal8 (7.1%)↑14.3%prior 7
FTYROW: From stop sign6 (5.4%)
FTYROW: At uncontrolled intersection5 (4.5%)
Ran Stop Sign5 (4.5%)
Followed too close5 (4.5%)↓-28.6%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

There was a distinct shift in the conditions present during crashes. In 2022, a smaller proportion of crashes occurred on dry roads (60.7% vs. 73.5% in 2021), while incidents on snowy or icy surfaces increased. Crashes on snow-covered roads rose from 8 to 14, and those on ice or frost increased from 7 to 13. Similarly, crashes in cloudy conditions more than tripled, from 5 in 2021 to 16 in 2022.

Weather

Clear73 (70.2%)
↓-9.9%prior 81
Cloudy16 (15.4%)
↑220.0%prior 5
Snow5 (4.8%)
Freezing rain/drizzle4 (3.8%)
Rain2 (1.9%)
Blowing Snow2 (1.9%)
Other (explain in narrative)1 (1.0%)
Fog, smoke, smog1 (1.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Daylight76 (73.1%)
↑20.6%prior 63
Dark - roadway not lighted14 (13.5%)
→0.0%prior 14
Dusk6 (5.8%)
↑20.0%prior 5
Dark - roadway lighted5 (4.8%)
↓-28.6%prior 7
Dawn3 (2.9%)
↓-62.5%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field

Road Surface

Dry68 (65.4%)
↓-5.6%prior 72
Snow14 (13.5%)
↑75.0%prior 8
Ice/frost13 (12.5%)
↑85.7%prior 7
Wet6 (5.8%)
↑20.0%prior 5
Gravel3 (2.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field

Vehicles & Demographics

The demographics of persons involved in crashes changed, with a significant increase in the 65+ age group, which grew from 23 individuals in 2021 to 41 in 2022. The top vehicle makes involved in crashes remained Chevrolet and Ford, but their order reversed; Chevrolet-branded vehicles were involved in 49 crashes in 2022 (up from 37), while Ford-branded vehicles were involved in 38 (up from 34).

Top Vehicle Makes (182 vehicles)

1
CHEV40 (22%)
↑122.2%prior 18
2
FORD38 (20.9%)
↑11.8%prior 34
3
DODG9 (4.9%)
↓-10.0%prior 10
4
CHEVROLET9 (4.9%)
↓-52.6%prior 19
5
GMC7 (3.8%)
↓-12.5%prior 8
6
TOYT6 (3.3%)
7
LINC4 (2.2%)
8
HOND4 (2.2%)
↓-20.0%prior 5
9
CHRY4 (2.2%)
10
NR4 (2.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records

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

Sex Distribution (162 persons with recorded sex)

Male95 (58.6%)
↑35.7%prior 70
Female67 (41.4%)
↑34.0%prior 50

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 112
  • Total persons involved: 243
  • Total vehicles involved: 182

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: 2022." Published September 9, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2022-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

ThatCarHitMe.com · An Injuria.ai Company

Hancock County, IA Crash Report — 2022 | ThatCarHitMe.com