Yearly Traffic Safety Analysis

151 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In Palo Alto County, total crashes increased from 113 in 2020 to 151 in 2021, a rise of approximately 33.6%. Despite the increase in total collisions, the most notable year-over-year shift was a positive one: fatalities dropped from two in the prior period to zero in the current period.

151

▲ 33.6%was 113

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

38

▼ -7.3%was 41

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 Palo Alto County showed a significant increase year-over-year, with total collisions rising by 33.6% from 113 in 2020 to 151 in 2021. However, this increase in crash volume did not correspond to a rise in harm; total injuries saw a slight decrease from 41 to 38, and fatalities fell from two to zero.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 2↓-100.0%

38

Motorists Injured

Prior: 41↓-7.3%

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 shifted between the two periods. In 2021, the peak day for crashes was Friday with 31 incidents, a change from 2020 when Wednesday and Thursday were the peak days with 22 crashes each. The peak hour for collisions also moved earlier, from 8 p.m. in 2020 (13 crashes) to 6 p.m. in 2021 (15 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

Crash severity improved year-over-year, with fatal crashes decreasing from one in 2020 to zero in 2021. The number of serious injury crashes also declined from 4 to 3. While the total number of crashes involving minor or possible injuries increased from a combined 21 to 32, crashes resulting in no injury made up a consistent share of the total, accounting for 76.8% in 2021 compared to 77.0% in 2020.

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes2%
↓-25.0%prior 4
Minor Injury13minor injury crashes8.6%
↑62.5%prior 8
Possible Injury19possible injury crashes12.6%
↑46.2%prior 13
No Injury116no injury crashes76.8%
↑33.3%prior 87

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 crashes increasing from 44 in 2020 to 62 in 2021. 'Lost Control' became the second-most cited factor in 2021 with 11 incidents, more than doubling its count of 5 from the previous year. The count for crashes attributed to 'Driving too fast for conditions' held steady at 9 incidents, while incidents involving 'Failure to Yield Right of Way from a stop sign' also rose from 4 to 7 crashes.

Officer-Reported Primary Contributing Cause

Animal62 (41.1%)↑40.9%prior 44
Lost Control11 (7.3%)↑120.0%prior 5
Driving too fast for conditions9 (6%)→0.0%prior 9
FTYROW: From stop sign7 (4.6%)
Driver Distraction: Other interior distraction5 (3.3%)
Ran Stop Sign4 (2.6%)
Followed too close4 (2.6%)
Other (explain in narrative): Other4 (2.6%)
FTYROW: From driveway3 (2%)
Ran off road - straight3 (2%)↓-50.0%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 periods occurred in daylight on dry roads. In 2021, crashes during daylight hours increased from 42 to 66, and those on dry surfaces rose from 46 to 68. The proportion of crashes occurring on adverse road surfaces like ice, snow, or wet pavement remained relatively stable, accounting for 17.9% of all crashes in 2021 compared to 15.9% in 2020. Crashes in dark, unlighted conditions were nearly unchanged, with 20 incidents in 2021 versus 21 in the prior year.

Weather

Clear67 (69.1%)
↑67.5%prior 40
Cloudy25 (25.8%)
↑127.3%prior 11
Rain3 (3.1%)
↓-40.0%prior 5
Fog, smoke, smog2 (2.1%)

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

Lighting

Daylight66 (68.0%)
↑57.1%prior 42
Dark - roadway not lighted20 (20.6%)
↓-4.8%prior 21
Dark - roadway lighted8 (8.2%)
Dusk2 (2.1%)
Dark - unknown roadway lighting1 (1.0%)

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

Road Surface

Dry68 (69.4%)
↑47.8%prior 46
Ice/frost12 (12.2%)
↑20.0%prior 10
Wet8 (8.2%)
↑60.0%prior 5
Snow7 (7.1%)
Gravel3 (3.1%)
↓-40.0%prior 5

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford and Chevrolet models leading in both years. The number of Fords involved increased from 30 to 42, while combined Chevrolet models ('CHEV' and 'CHEVROLET') rose from 37 to 43. A significant demographic shift occurred among persons involved in crashes, with the 16-20 age group seeing its numbers more than double from 21 individuals in 2020 to 49 in 2021. This group's share of all persons involved in crashes increased from 8.6% to 18.4%.

Top Vehicle Makes (206 vehicles)

1
FORD42 (20.4%)
↑40.0%prior 30
2
CHEV29 (14.1%)
↑26.1%prior 23
3
GMC16 (7.8%)
↑128.6%prior 7
4
CHEVROLET14 (6.8%)
→0.0%prior 14
5
DODGE9 (4.4%)
↑80.0%prior 5
6
CHRY9 (4.4%)
↑28.6%prior 7
7
TOYT8 (3.9%)
↓-27.3%prior 11
8
DODG8 (3.9%)
9
JEEP6 (2.9%)
↓-14.3%prior 7
10
BUIC6 (2.9%)
↑20.0%prior 5

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

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

Sex Distribution (162 persons with recorded sex)

Male94 (58.0%)
↑4.4%prior 90
Female68 (42.0%)
↑28.3%prior 53

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: 151
  • Total persons involved: 266
  • Total vehicles involved: 206

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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