Yearly Traffic Safety Analysis

311 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In 2021, Tama County recorded 311 total crashes, an 11.9% increase from the 278 crashes documented in 2020. While the total number of injuries decreased from 84 to 79, the number of fatalities rose from one in the prior year to three in the current year.

311

▲ 11.9%was 278

Total Crash Events

3

▲ 200.0%was 1

Persons Killed

79

▼ -6.0%was 84

Persons Injured

3

▲ 200.0%was 1

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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

The overall trend in Tama County shows an increase in traffic collisions year-over-year. Total crashes rose by 11.9%, from 278 in 2020 to 311 in 2021. Despite this increase, the number of reported injuries decreased by 6.0%, while fatalities increased from one to three.

Vulnerable Road User Casualties

3

Motorists Killed

Prior: 1↑200.0%

79

Motorists Injured

Prior: 84↓-6.0%

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 Tama County shifted between the two periods. The most frequent day for crashes moved from Wednesday (46 incidents) in 2020 to Monday (54 incidents) in 2021. Similarly, the peak hour for collisions changed from 8 p.m. in the prior year, with 22 crashes, to the 5 p.m. hour in the current year, with 32 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 outcomes worsened in 2021 compared to the previous year. The number of fatal crashes increased from one to three, and serious injury crashes more than doubled from 6 to 13. Consequently, the share of crashes resulting in a fatality or serious injury rose from 2.6% of all crashes in 2020 to 5.2% in 2021. The total count of individuals injured saw a slight decrease from 84 to 79.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1%
↑200.0%prior 1
Serious Injury13serious injury crashes4.2%
↑116.7%prior 6
Minor Injury27minor injury crashes8.7%
↑22.7%prior 22
Possible Injury28possible injury crashes9%
↓-26.3%prior 38
No Injury240no injury crashes77.2%
↑13.7%prior 211

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 both periods, with the count increasing from 112 in 2020 to 129 in 2021. "Lost Control" was the second-most cited factor in both years, with a nearly identical count of 24 in 2020 and 23 in 2021. The number of crashes attributed to "Followed too close" increased by 175%, rising from 4 incidents in 2020 to 11 in 2021, while incidents of "Ran off road - left" were halved, dropping from 14 to 7.

Officer-Reported Primary Contributing Cause

Animal129 (41.5%)↑15.2%prior 112
Lost Control23 (7.4%)↓-4.2%prior 24
Ran off road - straight20 (6.4%)↑11.1%prior 18
FTYROW: From stop sign15 (4.8%)↑7.1%prior 14
Followed too close11 (3.5%)
Other (explain in narrative): Other11 (3.5%)↓-15.4%prior 13
Ran Stop Sign9 (2.9%)↑12.5%prior 8
Driving too fast for conditions8 (2.6%)↓-20.0%prior 10
Ran off road - left7 (2.3%)↓-50.0%prior 14
Operating vehicle in an reckless, erratic, careless, negligent manner7 (2.3%)↑40.0%prior 5

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 2021 and 2020 occurred in clear weather on dry roads. In 2021, the proportion of crashes under these ideal conditions was higher, with 45.7% of crashes happening in clear weather compared to 41.4% in 2020. Correspondingly, crashes during adverse weather conditions like snow or rain accounted for a smaller share of the total in 2021 (4.8%) than in 2020 (9.0%). A similar trend was observed for road surface conditions, with crashes on non-dry surfaces dropping from 17.6% of the total in 2020 to 10.9% in 2021.

Weather

Clear142 (70.0%)
↑23.5%prior 115
Cloudy43 (21.2%)
↑13.2%prior 38
Blowing Snow4 (2.0%)
Snow4 (2.0%)
↓-63.6%prior 11
Fog, smoke, smog3 (1.5%)
Rain3 (1.5%)
↓-50.0%prior 6
Severe Winds3 (1.5%)
Sleet, hail1 (0.5%)

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

Lighting

Daylight127 (63.2%)
↑24.5%prior 102
Dark - roadway not lighted49 (24.4%)
↓-2.0%prior 50
Dark - roadway lighted10 (5.0%)
↓-37.5%prior 16
Dawn9 (4.5%)
↑50.0%prior 6
Dusk5 (2.5%)
↓-28.6%prior 7
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry165 (80.1%)
↑32.0%prior 125
Wet15 (7.3%)
↓-21.1%prior 19
Ice/frost8 (3.9%)
↓-27.3%prior 11
Gravel7 (3.4%)
↓-12.5%prior 8
Snow7 (3.4%)
↓-53.3%prior 15
Slush3 (1.5%)
Mud, dirt1 (0.5%)

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

Vehicles & Demographics

Chevrolet and Ford were the most frequently involved vehicle makes in crashes during both periods. The number of Chevrolet vehicles (combining 'CHEV' and 'CHEVROLET' entries) involved in crashes increased from 87 in 2020 to 112 in 2021, while Fords increased from 60 to 69. An analysis of persons involved shows a shift in age demographics; the 16-20 age group represented a smaller share of individuals in 2021 (11.4%) compared to 2020 (16.2%). Conversely, the proportion of individuals in the 26-34 age group increased from 16.9% in 2020 to 19.9% in 2021.

Top Vehicle Makes (423 vehicles)

1
CHEV79 (18.7%)
↑27.4%prior 62
2
FORD69 (16.3%)
↑15.0%prior 60
3
CHEVROLET33 (7.8%)
↑32.0%prior 25
4
DODG21 (5%)
↑5.0%prior 20
5
TOYT19 (4.5%)
↑35.7%prior 14
6
GMC14 (3.3%)
↑100.0%prior 7
7
JEEP12 (2.8%)
↓-20.0%prior 15
8
HOND10 (2.4%)
↓-28.6%prior 14
9
BUIC9 (2.1%)
→0.0%prior 9
10
HONDA8 (1.9%)
↑14.3%prior 7

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

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

Sex Distribution (315 persons with recorded sex)

Male192 (61.0%)
↓-13.9%prior 223
Female123 (39.0%)
↓-2.4%prior 126

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: 311
  • Total persons involved: 552
  • Total vehicles involved: 423

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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