Yearly Traffic Safety Analysis

146 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In Wright County, total traffic crashes decreased from 162 in 2020 to 146 in 2021, a 9.9% reduction. This overall decline was accompanied by a decrease in both injuries, which fell from 55 to 38, and fatalities, which dropped from 2 to 1. Notably, crashes involving a driver under the influence (DUI) saw a significant reduction, falling from 11 incidents in the prior year to 6 in the current year.

146

▼ -9.9%was 162

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

38

▼ -30.9%was 55

Persons Injured

1

▼ -50.0%was 2

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 · 2021-01-01 to 2021-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety trends in Wright County showed improvement from 2020 to 2021. Total crashes decreased by 9.9%, from 162 to 146 incidents. This downward trend was also reflected in crash outcomes, with total injuries declining by 30.9% (from 55 to 38) and fatalities halving from 2 to 1.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 2↓-50.0%

38

Motorists Injured

Prior: 54↓-29.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes in Wright County shifted between 2020 and 2021. The most frequent day for crashes moved from Wednesday (33 incidents) in the prior year to Thursday (35 incidents) in the current year. The peak hour for collisions also shifted later, from 5 p.m. in 2020 to a three-way tie at 3 p.m., 4 p.m., and 5 p.m. in 2021, each with 11 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes in Wright County lessened from 2020 to 2021. The number of fatal crashes was halved from 2 to 1, and the proportion of crashes resulting in no injuries increased from 68.5% to 80.1%. Correspondingly, the share of crashes involving minor injuries decreased from 16.0% to 8.9%, and possible injury crashes fell from 12.3% to 8.2% of the total. The proportion of serious injury crashes remained relatively stable at approximately 2% in both years.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
↓-50.0%prior 2
Serious Injury3serious injury crashes2.1%
→0.0%prior 3
Minor Injury13minor injury crashes8.9%
↓-50.0%prior 26
Possible Injury12possible injury crashes8.2%
↓-40.0%prior 20
No Injury117no injury crashes80.1%
↑5.4%prior 111

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in Wright County for both periods, with a slight decrease in count from 37 incidents in 2020 to 33 in 2021. The count of crashes attributed to 'Lost Control' fell significantly by 43.8%, from 16 to 9 incidents, dropping it from the second to the third-ranked factor. Conversely, crashes involving 'Ran Stop Sign' and 'Followed too close' both increased from 2 incidents in the prior year to 7 incidents each in the current year, moving them into the top five contributing factors.

Officer-Reported Primary Contributing Cause

Animal33 (22.6%)↓-10.8%prior 37
Other (explain in narrative): Other15 (10.3%)↑66.7%prior 9
Lost Control9 (6.2%)↓-43.8%prior 16
Driving too fast for conditions8 (5.5%)↓-20.0%prior 10
Ran off road - straight7 (4.8%)↓-30.0%prior 10
Ran Stop Sign7 (4.8%)
Followed too close7 (4.8%)
Improper Backing6 (4.1%)
Other (explain in narrative): No improper action5 (3.4%)→0.0%prior 5
FTYROW: At uncontrolled intersection4 (2.7%)↓-33.3%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The majority of crashes in both 2020 and 2021 occurred in clear weather and on dry roads. However, there was a notable shift in lighting conditions; the proportion of crashes happening during daylight hours increased from 47.5% in 2020 to 55.5% in 2021. Concurrently, crashes in dark, unlighted conditions decreased from 19.8% to 12.3% of the total. The share of crashes on adverse road surfaces like ice or snow also saw a reduction, falling from a combined 15.5% in the prior year to 11.6% in the current year.

Weather

Clear85 (75.9%)
↓-11.5%prior 96
Cloudy15 (13.4%)
↑7.1%prior 14
Freezing rain/drizzle4 (3.6%)
↓-20.0%prior 5
Blowing Snow3 (2.7%)
Snow2 (1.8%)
↓-71.4%prior 7
Fog, smoke, smog1 (0.9%)
Other (explain in narrative)1 (0.9%)
Sleet, hail1 (0.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Weather condition at time of crash

Lighting

Daylight81 (71.7%)
↑5.2%prior 77
Dark - roadway not lighted18 (15.9%)
↓-43.8%prior 32
Dark - roadway lighted8 (7.1%)
↓-27.3%prior 11
Dusk3 (2.7%)
↓-57.1%prior 7
Dawn2 (1.8%)
Dark - unknown roadway lighting1 (0.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Lighting condition field

Road Surface

Dry87 (77.7%)
↑1.2%prior 86
Ice/frost11 (9.8%)
↓-31.3%prior 16
Gravel6 (5.4%)
↓-45.5%prior 11
Snow6 (5.4%)
↓-33.3%prior 9
Wet1 (0.9%)
Other (explain in narrative)1 (0.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Road surface condition field

Vehicles & Demographics

An analysis of vehicles and persons involved in crashes shows shifts in both make and age demographics. Combining variations, Chevrolet (60 vehicles) surpassed Ford (46 vehicles) as the most common make in 2021, a reversal from 2020 when Ford (48) slightly edged out Chevrolet (53). Regarding driver age, the 26-34 and 35-44 age groups were most represented in 2021, each with 41 individuals involved. This represents a decrease for the 26-34 age group, which was the top group in the prior year with 59 individuals.

Top Vehicle Makes (230 vehicles)

1
FORD46 (20%)
↓-4.2%prior 48
2
CHEV30 (13%)
↓-14.3%prior 35
3
CHEVROLET30 (13%)
↑66.7%prior 18
4
DODGE10 (4.3%)
↑66.7%prior 6
5
GMC9 (3.9%)
↓-18.2%prior 11
6
FREIGHTLINER8 (3.5%)
↑60.0%prior 5
7
DODG7 (3%)
↓-12.5%prior 8
8
TOYT6 (2.6%)
↓-25.0%prior 8
9
JEEP6 (2.6%)
↓-25.0%prior 8
10
BUICK5 (2.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Vehicle unit records

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

Sex Distribution (167 persons with recorded sex)

Male95 (56.9%)
↓-21.5%prior 121
Female72 (43.1%)
↓-10.0%prior 80

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · 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: 2021-01-01 through 2021-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2021-01-01 through 2021-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 146
  • Total persons involved: 278
  • Total vehicles involved: 230

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: 2021." Published September 9, 2026. Reporting period: 2021-01-01 to 2021-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2021-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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