Yearly Traffic Safety Analysis

312 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Poweshiek County, there were 312 crashes in 2022, a 3.1% decrease from the 322 crashes recorded in 2021. Despite the drop in total collisions, the number of people injured rose by 21.3%, from 94 in 2021 to 114 in 2022. The number of fatalities remained unchanged at two for both years.

312

▼ -3.1%was 322

Total Crash Events

2

Persons Killed

114

▲ 21.3%was 94

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 · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, the total number of crashes in Poweshiek County saw a slight decline from 2021 to 2022. Crashes decreased by 3.1%, from 322 to 312. This indicates a relatively stable but slightly improving trend in terms of crash frequency year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

2

Pedestrians Injured

Prior: 2→0.0%

2

Cyclists Injured

Prior: 1↑100.0%

110

Motorists Injured

Prior: 91↑20.9%

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 temporal patterns of crashes showed some shifts between 2021 and 2022. While Monday remained the peak day for crashes in both years (57 in 2021 and 55 in 2022), the peak hour changed. In 2022, the most crashes occurred at 11 a.m. with 28 incidents, a shift from 2021's peak at 6 p.m. which saw 22 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

The severity of crashes shows a mixed picture year-over-year. The number of fatal crashes remained constant at two in both 2021 and 2022, resulting in two fatalities each year. However, the proportion of crashes involving an injury increased, rising from 23.0% of all crashes in 2021 to 25.6% in 2022. This was driven by increases across all injury categories, including a rise in serious injury crashes from 9 to 10.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.6%
→0.0%prior 2
Serious Injury10serious injury crashes3.2%
↑11.1%prior 9
Minor Injury32minor injury crashes10.3%
↑10.3%prior 29
Possible Injury38possible injury crashes12.2%
↑5.6%prior 36
No Injury230no injury crashes73.7%
↓-6.5%prior 246

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. While collisions involving an "Animal" remained the top factor in both years, their count decreased from 73 in 2021 to 62 in 2022. "Lost Control" incidents increased from 29 to 38, moving this factor from third to second place. Conversely, "Driving too fast for conditions" saw a significant drop in rank, falling from the second most common factor with 31 crashes in 2021 to the fourth with 20 crashes in 2022.

Officer-Reported Primary Contributing Cause

Animal62 (19.9%)↓-15.1%prior 73
Lost Control38 (12.2%)↑31.0%prior 29
Ran off road - straight30 (9.6%)↑15.4%prior 26
Driving too fast for conditions20 (6.4%)↓-35.5%prior 31
Ran off road - left18 (5.8%)↑28.6%prior 14
Other (explain in narrative): Other12 (3.8%)↑20.0%prior 10
Ran Stop Sign10 (3.2%)
FTYROW: From stop sign10 (3.2%)↓-52.4%prior 21
Followed too close9 (2.9%)↓-55.0%prior 20
Operating vehicle in an reckless, erratic, careless, negligent manner8 (2.6%)↑14.3%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

The conditions under which crashes occurred were largely consistent year-over-year. In both 2022 and 2021, the majority of crashes happened in clear weather (169 and 170, respectively) and on dry roads (167 and 187). There was a slight increase in the proportion of crashes occurring during daylight, which rose from 55.6% in 2021 to 60.6% in 2022. Crashes reported during snowy weather increased from 16 to 22 incidents.

Weather

Clear169 (65.5%)
↓-0.6%prior 170
Cloudy38 (14.7%)
↓-17.4%prior 46
Snow22 (8.5%)
↑37.5%prior 16
Rain16 (6.2%)
↑14.3%prior 14
Blowing Snow4 (1.6%)
↓-33.3%prior 6
Freezing rain/drizzle3 (1.2%)
↓-50.0%prior 6
Severe Winds3 (1.2%)
Fog, smoke, smog3 (1.2%)

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

Lighting

Daylight189 (73.0%)
↑5.6%prior 179
Dark - roadway not lighted42 (16.2%)
↓-19.2%prior 52
Dark - roadway lighted16 (6.2%)
↑14.3%prior 14
Dawn6 (2.3%)
↓-33.3%prior 9
Dusk4 (1.5%)
Dark - unknown roadway lighting2 (0.8%)

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

Road Surface

Dry167 (64.7%)
↓-10.7%prior 187
Wet30 (11.6%)
↑15.4%prior 26
Snow27 (10.5%)
↑22.7%prior 22
Ice/frost21 (8.1%)
↑75.0%prior 12
Gravel9 (3.5%)
↑28.6%prior 7
Other (explain in narrative)2 (0.8%)
Slush1 (0.4%)
↓-85.7%prior 7
Mud, dirt1 (0.4%)

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

Vehicles & Demographics

The demographics of vehicles and persons involved in crashes showed notable changes. Ford vehicles became more prominent, with their involvement increasing from 73 in 2021 to 94 in 2022, solidifying their top rank. In contrast, Chevrolet vehicles (combining CHEV and CHEVROLET) saw a decrease from 104 to 90. Regarding persons involved, the 35-44 age group saw a significant increase from 95 to 131 individuals, becoming the largest cohort in 2022. The 65+ age group also grew substantially from 59 to 104 persons involved.

Top Vehicle Makes (456 vehicles)

1
FORD94 (20.6%)
↑28.8%prior 73
2
CHEV66 (14.5%)
↓-7.0%prior 71
3
JEEP25 (5.5%)
↑92.3%prior 13
4
CHEVROLET24 (5.3%)
↓-27.3%prior 33
5
FREIGHTLINER19 (4.2%)
↑90.0%prior 10
6
GMC15 (3.3%)
↑15.4%prior 13
7
RAM13 (2.9%)
↑30.0%prior 10
8
KENWORTH12 (2.6%)
↑100.0%prior 6
9
DODG12 (2.6%)
→0.0%prior 12
10
NR10 (2.2%)
↓-9.1%prior 11

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

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

Sex Distribution (421 persons with recorded sex)

Male267 (63.4%)
↓-0.4%prior 268
Female154 (36.6%)
↑7.7%prior 143

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: 312
  • Total persons involved: 649
  • Total vehicles involved: 456

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

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