Yearly Traffic Safety Analysis

562 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In Wapello County, total traffic crashes increased by 8.1%, from 520 in 2020 to 562 in 2021. During this period, injuries rose by 12.9% from 201 to 227, and fatalities increased from 4 to 5. The most significant year-over-year change was a 90.9% increase in the number of serious injury crashes, which grew from 11 to 21.

562

▲ 8.1%was 520

Total Crash Events

5

▲ 25.0%was 4

Persons Killed

227

▲ 12.9%was 201

Persons Injured

5

▲ 25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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

Overall, Wapello County experienced a rising trend in traffic collisions from 2020 to 2021. The total number of crashes increased by 8.1% from 520 to 562. This was accompanied by a more pronounced increase in negative outcomes, with total injuries rising 12.9% (from 201 to 227) and fatalities increasing from 4 to 5.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 4↑25.0%

4

Pedestrians Injured

Prior: 3↑33.3%

5

Cyclists Injured

Prior: 2↑150.0%

218

Motorists Injured

Prior: 196↑11.2%

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 91 incidents, a change from 2020 when Wednesday was the peak day with 93 incidents. Similarly, the peak hour for crashes moved from 3 p.m. in 2020 (44 crashes) to 5 p.m. in 2021 (45 crashes), indicating a shift in collision timing toward the end of the work week and day.

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 worsened year-over-year. The number of fatal crashes increased from 4 to 5, and the fatal crash rate rose from 0.77 to 0.89 per 100 crashes. The most notable change was in serious injury crashes, which nearly doubled in count from 11 in 2020 to 21 in 2021, increasing their share of all crashes from 2.1% to 3.7%. Conversely, the proportion of crashes resulting in 'possible injury' decreased from 19.6% to 15.7%.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.9%
↑25.0%prior 4
Serious Injury21serious injury crashes3.7%
↑90.9%prior 11
Minor Injury67minor injury crashes11.9%
↑21.8%prior 55
Possible Injury88possible injury crashes15.7%
↓-13.7%prior 102
No Injury381no injury crashes67.8%
↑9.5%prior 348

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 years, with the count increasing from 90 incidents in 2020 to 100 in 2021. Several other key factors saw significant increases in count; crashes attributed to 'Failure to Yield Right of Way from a stop sign' rose by 79.3% from 29 to 52, and 'Lost Control' incidents increased by 68% from 25 to 42. In contrast, crashes involving 'Ran Stop Sign' decreased from 35 to 22.

Officer-Reported Primary Contributing Cause

Animal100 (17.8%)↑11.1%prior 90
FTYROW: From stop sign52 (9.3%)↑79.3%prior 29
Followed too close43 (7.7%)↑38.7%prior 31
Lost Control42 (7.5%)↑68.0%prior 25
Other (explain in narrative): Other33 (5.9%)→0.0%prior 33
Ran off road - left28 (5%)↑21.7%prior 23
Ran off road - straight26 (4.6%)↑4.0%prior 25
Driving too fast for conditions24 (4.3%)↓-11.1%prior 27
FTYROW: Making left turn23 (4.1%)↑43.8%prior 16
Ran Stop Sign22 (3.9%)↓-37.1%prior 35

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 distribution of crashes across different environmental conditions remained largely stable year-over-year. In both 2020 and 2021, approximately 65% of crashes occurred in clear weather and 67% on dry road surfaces. The proportion of crashes happening during daylight versus darkness also saw minimal change. The only minor shift was a small increase in the count of crashes on icy or frosty roads, which rose from 17 in 2020 to 24 in 2021.

Weather

Clear370 (77.1%)
↑8.8%prior 340
Cloudy58 (12.1%)
↑16.0%prior 50
Rain21 (4.4%)
↓-4.5%prior 22
Snow19 (4.0%)
↓-13.6%prior 22
Freezing rain/drizzle5 (1.0%)
↓-16.7%prior 6
Blowing Snow3 (0.6%)
Fog, smoke, smog2 (0.4%)
Severe Winds1 (0.2%)
Other (explain in narrative)1 (0.2%)

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

Lighting

Daylight319 (65.9%)
↑10.4%prior 289
Dark - roadway lighted84 (17.4%)
↑16.7%prior 72
Dark - roadway not lighted56 (11.6%)
↑12.0%prior 50
Dusk12 (2.5%)
↓-40.0%prior 20
Dawn9 (1.9%)
↑12.5%prior 8
Dark - unknown roadway lighting4 (0.8%)

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

Road Surface

Dry380 (78.7%)
↑8.6%prior 350
Wet40 (8.3%)
↓-4.8%prior 42
Snow28 (5.8%)
↑12.0%prior 25
Ice/frost24 (5.0%)
↑41.2%prior 17
Gravel10 (2.1%)
Other (explain in narrative)1 (0.2%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes showed some shifts. The count of Ford vehicles in crashes increased from 126 to 173, widening its lead as the most common make. In contrast, the combined total for Chevrolet vehicles (recorded as 'CHEV' and 'CHEVROLET') slightly decreased from 208 to 199. Regarding driver age, involvement for the 26-34 age group decreased from 220 to 197 persons, while the 45-54 age group saw a notable increase in persons involved, from 120 to 156.

Top Vehicle Makes (923 vehicles)

1
FORD173 (18.7%)
↑37.3%prior 126
2
CHEV111 (12%)
↓-11.2%prior 125
3
CHEVROLET88 (9.5%)
↑6.0%prior 83
4
TOYT43 (4.7%)
↓-14.0%prior 50
5
DODG40 (4.3%)
↓-23.1%prior 52
6
GMC39 (4.2%)
↑39.3%prior 28
7
TOYOTA35 (3.8%)
↑66.7%prior 21
8
DODGE33 (3.6%)
↑10.0%prior 30
9
JEEP25 (2.7%)
↓-21.9%prior 32
10
CHRY22 (2.4%)
↑15.8%prior 19

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

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

Sex Distribution (768 persons with recorded sex)

Male447 (58.2%)
↓-1.8%prior 455
Female321 (41.8%)
→0.0%prior 321

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: 562
  • Total persons involved: 1,187
  • Total vehicles involved: 923

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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