Yearly Traffic Safety Analysis

140 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In Calhoun County, total crashes increased by 33.3% from 105 in 2020 to 140 in 2021. While the number of fatalities dropped from one to zero, the most significant year-over-year change was a 121% increase in the number of people injured, which rose from 19 to 42.

140

▲ 33.3%was 105

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

42

▲ 121.1%was 19

Persons Injured

0

▼ -100.0%was 1

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

Trend Summary

Crash trends in Calhoun County showed a notable increase between 2020 and 2021. The total number of incidents rose by 33.3%, from 105 to 140. This upward trend was also reflected in crash outcomes, with total injuries more than doubling from 19 to 42, even as fatalities fell from one to zero.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 1↓-100.0%

1

Cyclists Injured

Prior: 0→%

41

Motorists Injured

Prior: 19↑115.8%

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

Temporal patterns shifted year-over-year, with crashes becoming more concentrated on weekdays. In 2021, Monday, Wednesday, and Friday tied as the peak day with 25 crashes each, a substantial increase from 2020's single peak day of Friday with 17 crashes. The peak hour for crashes also broadened; while the 3 PM hour was a high-frequency time in both years, 2021 saw three additional peak hours at 8 AM, 12 PM, and 6 PM, each with 10 incidents.

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

While Calhoun County recorded zero fatal crashes in 2021, a decrease from one in 2020, the overall severity of crashes increased. The proportion of crashes resulting in an injury rose from 17.1% of all crashes in 2020 to 26.4% in 2021. The count of serious injury crashes increased from 2 to 7, and the total number of persons injured grew from 19 to 42.

Outcome by Severity (Crash Events)

Serious Injury7serious injury crashes5%
↑250.0%prior 2
Minor Injury13minor injury crashes9.3%
↑116.7%prior 6
Possible Injury17possible injury crashes12.1%
↑70.0%prior 10
No Injury103no injury crashes73.6%
↑19.8%prior 86

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 with animals remained the leading contributing factor in both periods, with the count of such incidents increasing from 32 in 2020 to 41 in 2021. Crashes attributed to a driver losing control nearly doubled in count, rising from 8 to 15 incidents. Another notable change was the increase in crashes involving failure to yield at an uncontrolled intersection, which tripled from 2 incidents in 2020 to 6 in 2021.

Officer-Reported Primary Contributing Cause

Animal41 (29.3%)↑28.1%prior 32
Lost Control15 (10.7%)↑87.5%prior 8
Other (explain in narrative): Other10 (7.1%)
Ran off road - straight7 (5%)↓-12.5%prior 8
FTYROW: At uncontrolled intersection6 (4.3%)
Driving too fast for conditions5 (3.6%)
Operating vehicle in an reckless, erratic, careless, negligent manner5 (3.6%)
Ran off road - left4 (2.9%)↓-42.9%prior 7
Followed too close4 (2.9%)
Ran Stop Sign4 (2.9%)

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 proportion of crashes occurring in daylight decreased from 53.3% in 2020 to 47.1% in 2021, while the share of crashes on dark, unlit roadways grew from 15.2% to 20.7%. Regarding road surface, the percentage of crashes on dry roads was stable at approximately 55% for both years. However, crashes on icy or frosty surfaces represented a smaller share of the total in 2021 (5.7%) compared to 2020 (13.3%).

Weather

Clear71 (64.0%)
↑26.8%prior 56
Cloudy20 (18.0%)
↑100.0%prior 10
Rain7 (6.3%)
Severe Winds5 (4.5%)
Snow3 (2.7%)
Freezing rain/drizzle2 (1.8%)
Fog, smoke, smog1 (0.9%)
Sleet, hail1 (0.9%)
Blowing sand, soil, dirt1 (0.9%)

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

Lighting

Daylight66 (59.5%)
↑17.9%prior 56
Dark - roadway not lighted29 (26.1%)
↑81.3%prior 16
Dark - roadway lighted8 (7.2%)
Dawn5 (4.5%)
→0.0%prior 5
Dusk3 (2.7%)

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

Road Surface

Dry77 (69.4%)
↑32.8%prior 58
Gravel11 (9.9%)
Wet8 (7.2%)
Ice/frost8 (7.2%)
↓-42.9%prior 14
Snow5 (4.5%)
Slush2 (1.8%)

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

Vehicles & Demographics

In 2021, Chevrolet became the most frequent vehicle make involved in crashes with 44 vehicles (combining 'CHEV' and 'CHEVROLET' records), surpassing Ford, which was the top make in 2020 with 25 vehicles. The age distribution of persons involved in crashes also shifted, with the 16-20 and 26-34 age groups becoming the most represented in 2021, each with 42 individuals. This marks a significant increase from their 2020 counts of 19 and 31, respectively.

Top Vehicle Makes (189 vehicles)

1
CHEV30 (15.9%)
↑25.0%prior 24
2
FORD22 (11.6%)
↓-12.0%prior 25
3
CHEVROLET14 (7.4%)
→0.0%prior 14
4
GMC8 (4.2%)
↑60.0%prior 5
5
DODGE7 (3.7%)
↑40.0%prior 5
6
DODG7 (3.7%)
↓-12.5%prior 8
7
JEEP6 (3.2%)
8
CHRY6 (3.2%)
↑20.0%prior 5
9
BUICK6 (3.2%)
10
BUIC5 (2.6%)
→0.0%prior 5

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

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

Sex Distribution (147 persons with recorded sex)

Male84 (57.1%)
↑6.3%prior 79
Female63 (42.9%)
↑10.5%prior 57

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 10, 2026

Data Coverage

  • Reporting period: 2021-01-01 through 2021-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 140
  • Total persons involved: 244
  • Total vehicles involved: 189

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 10, 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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