Yearly Traffic Safety Analysis

186 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Appanoose County, total traffic crashes decreased by 11.9% from 211 in 2021 to 186 in 2022. This overall reduction was accompanied by a 17.9% decrease in injuries, from 56 to 46, while fatalities remained unchanged with one person killed in each year. The most significant shift in contributing factors was a 43.8% reduction in crashes attributed to a vehicle running off the road straight, which fell from 16 incidents in 2021 to 9 in 2022.

186

▼ -11.8%was 211

Total Crash Events

1

Persons Killed

46

▼ -17.9%was 56

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

Traffic safety trends in Appanoose County showed improvement year-over-year. The total number of crashes fell from 211 in 2021 to 186 in 2022, a decrease of 11.9%. Correspondingly, the number of people injured in these incidents declined from 56 to 46, while the number of fatalities held steady at one in both periods.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

2

Pedestrians Injured

Prior: 3↓-33.3%

2

Cyclists Injured

Prior: 1↑100.0%

42

Motorists Injured

Prior: 52↓-19.2%

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 showed some shifts between the two periods. In 2022, the peak day for crashes was Friday with 31 incidents, a change from 2021 when Thursday was the peak day with 38 crashes. However, the peak hour for collisions remained consistent, with 3 p.m. being the most frequent time for crashes in both 2022 (15 crashes) and 2021 (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

The severity of crashes shifted year-over-year. While the number of fatal crashes remained constant at one, the fatal crash rate increased from 0.47% to 0.54% of all crashes due to the lower overall total. Crashes resulting in serious injuries decreased from 12 in 2021 to 7 in 2022, and possible injury crashes fell from 22 to 17. Conversely, crashes involving minor injuries increased from 16 to 19 during the same period.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
→0.0%prior 1
Serious Injury7serious injury crashes3.8%
↓-41.7%prior 12
Minor Injury19minor injury crashes10.2%
↑18.8%prior 16
Possible Injury17possible injury crashes9.1%
↓-22.7%prior 22
No Injury142no injury crashes76.3%
↓-11.3%prior 160

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

Collisions with animals remained the primary contributing factor in both years, with a slight increase in count from 68 in 2021 to 70 in 2022. The most significant change was a decrease in crashes where a driver 'Ran off road - straight,' which fell from being the second-ranked factor with 16 incidents in 2021 to the fourth-ranked with 9 incidents in 2022. Similarly, incidents of 'Followed too close' and 'FTYROW: From stop sign' both decreased, from 11 crashes each in 2021 to 6 and 2 crashes, respectively, in 2022.

Officer-Reported Primary Contributing Cause

Animal70 (37.6%)↑2.9%prior 68
Other (explain in narrative): Other15 (8.1%)↑87.5%prior 8
Ran off road - left14 (7.5%)↓-6.7%prior 15
Ran off road - straight9 (4.8%)↓-43.8%prior 16
Driver Distraction: Other interior distraction7 (3.8%)↓-12.5%prior 8
Ran Stop Sign7 (3.8%)→0.0%prior 7
Lost Control7 (3.8%)↓-36.4%prior 11
Followed too close6 (3.2%)↓-45.5%prior 11
Driving too fast for conditions4 (2.2%)
Swerving/Evasive Action4 (2.2%)

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

Road & Environmental Conditions

Year-over-year, there was a proportional increase in crashes occurring in adverse conditions, despite a lower total number of crashes. Collisions in dark, unlighted conditions increased from 28 in 2021 to 33 in 2022, representing a rise in share from 13.3% to 17.7% of all crashes. Similarly, the count of crashes on non-dry road surfaces (such as wet, snow, or ice) rose from 25 in 2021 to 29 in 2022.

Weather

Clear100 (78.1%)
↓-19.4%prior 124
Cloudy10 (7.8%)
↓-37.5%prior 16
Rain10 (7.8%)
Snow5 (3.9%)
→0.0%prior 5
Fog, smoke, smog1 (0.8%)
Freezing rain/drizzle1 (0.8%)
Severe Winds1 (0.8%)

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

Lighting

Daylight73 (57.0%)
↓-26.3%prior 99
Dark - roadway not lighted33 (25.8%)
↑17.9%prior 28
Dark - roadway lighted13 (10.2%)
↓-27.8%prior 18
Dawn5 (3.9%)
→0.0%prior 5
Dusk4 (3.1%)

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

Road Surface

Dry95 (74.2%)
↓-20.2%prior 119
Wet17 (13.3%)
↑183.3%prior 6
Snow5 (3.9%)
↓-50.0%prior 10
Ice/frost4 (3.1%)
↓-42.9%prior 7
Gravel4 (3.1%)
↓-55.6%prior 9
Slush2 (1.6%)
Sand1 (0.8%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Chevrolet and Ford being the most common in both 2022 and 2021. While the overall number of people involved in crashes decreased, the representation of younger individuals increased. The count of persons aged 16-20 involved in crashes rose from 58 to 65, and for the 21-25 age group, it increased from 25 to 38 year-over-year.

Top Vehicle Makes (251 vehicles)

1
CHEV50 (19.9%)
↑11.1%prior 45
2
FORD43 (17.1%)
↓-12.2%prior 49
3
CHEVROLET16 (6.4%)
↓-42.9%prior 28
4
DODG15 (6%)
↓-21.1%prior 19
5
JEEP11 (4.4%)
↓-26.7%prior 15
6
BUIC11 (4.4%)
↑83.3%prior 6
7
GMC11 (4.4%)
↓-15.4%prior 13
8
DODGE10 (4%)
→0.0%prior 10
9
TOYT9 (3.6%)
↓-43.8%prior 16
10
TOYOTA7 (2.8%)
↑40.0%prior 5

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

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

Sex Distribution (232 persons with recorded sex)

Male141 (60.8%)
↑6.0%prior 133
Female91 (39.2%)
↓-14.2%prior 106

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: 186
  • Total persons involved: 368
  • Total vehicles involved: 251

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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