Yearly Traffic Safety Analysis

92 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In Keokuk County, total traffic crashes increased by 10.8% from 83 in 2020 to 92 in 2021. Despite the rise in overall collisions, the most significant year-over-year change was a reduction in fatalities, which dropped from 3 in the prior period to 0 in the current period. Concurrently, the number of people injured in crashes rose from 26 to 37.

92

▲ 10.8%was 83

Total Crash Events

0

▼ -100.0%was 3

Persons Killed

37

▲ 42.3%was 26

Persons Injured

0

▼ -100.0%was 3

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 Keokuk County show a mixed pattern year-over-year. The total number of crashes increased by 10.8%, from 83 to 92, and total injuries rose by 42.3% from 26 to 37. However, this was accompanied by a positive trend in crash severity, as fatal crashes were eliminated, falling from 3 in 2020 to 0 in 2021.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 3↓-100.0%

37

Motorists Injured

Prior: 26↑42.3%

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 showed some shifts between the two periods. Friday remained the most frequent day for crashes, with 19 incidents in 2021 compared to 17 in 2020. However, the peak hour for collisions shifted four hours earlier, from 10 p.m. in 2020 (9 crashes) to 6 p.m. in 2021 (9 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

While overall crashes increased, the severity of outcomes improved significantly. There were no fatal crashes in 2021, a decrease from 3 fatal incidents in 2020. The number of serious injury crashes remained relatively stable, increasing from 4 to 5. However, the total number of injuries reported rose from 26 to 37, driven by increases in minor and possible injury categories.

Outcome by Severity (Crash Events)

Serious Injury5serious injury crashes5.4%
↑25.0%prior 4
Minor Injury8minor injury crashes8.7%
↑14.3%prior 7
Possible Injury14possible injury crashes15.2%
↑55.6%prior 9
No Injury65no injury crashes70.7%
↑8.3%prior 60

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 animals remained the leading contributing factor in both periods, though the count of such incidents decreased by 19.4% from 36 in 2020 to 29 in 2021. Other notable changes include a significant increase in crashes where a driver ran a stop sign, which rose from 3 to 7 incidents. Conversely, crashes attributed to 'Lost Control' and 'Followed too close' saw decreases, falling from 9 to 5 and 8 to 3, respectively.

Officer-Reported Primary Contributing Cause

Animal29 (31.5%)↓-19.4%prior 36
Ran Stop Sign7 (7.6%)
Driving too fast for conditions6 (6.5%)
Lost Control5 (5.4%)↓-44.4%prior 9
Driver Distraction: Other interior distraction5 (5.4%)
Ran off road - straight5 (5.4%)→0.0%prior 5
Operating vehicle in an reckless, erratic, careless, negligent manner5 (5.4%)
Ran off road - left3 (3.3%)
Followed too close3 (3.3%)↓-62.5%prior 8
FTYROW: Other (explain in narrative)2 (2.2%)

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

Road & Environmental Conditions

Comparatively, driving conditions saw some changes between the two periods. Crashes in daylight increased from 32 to 44, while collisions on dry roads were relatively stable, increasing from 37 to 39. There was a notable increase in crashes occurring on adverse road surfaces; incidents on wet roads more than doubled from 4 to 9, and crashes on snowy or icy surfaces increased from 6 to 10.

Weather

Clear43 (68.3%)
↑22.9%prior 35
Cloudy14 (22.2%)
→0.0%prior 14
Rain3 (4.8%)
Fog, smoke, smog1 (1.6%)
Blowing Snow1 (1.6%)
Snow1 (1.6%)

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

Lighting

Daylight44 (69.8%)
↑37.5%prior 32
Dark - roadway not lighted13 (20.6%)
↓-13.3%prior 15
Dark - roadway lighted3 (4.8%)
Dawn2 (3.2%)
Dusk1 (1.6%)

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

Road Surface

Dry39 (61.9%)
↑5.4%prior 37
Wet9 (14.3%)
Snow7 (11.1%)
Gravel3 (4.8%)
↓-40.0%prior 5
Slush2 (3.2%)
Other (explain in narrative)1 (1.6%)
Mud, dirt1 (1.6%)
Ice/frost1 (1.6%)

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 remained consistent, with Chevrolet (31 vehicles) and Ford (23 vehicles) being the most common in 2021, similar to the prior year's totals of 32 and 24, respectively. The age distribution of persons involved in crashes showed some changes, with a notable increase in the 0-15 age group (from 3 to 8 individuals) and the 35-44 age group (from 21 to 28 individuals). Conversely, the number of persons aged 65 and older involved in crashes decreased from 27 to 24.

Top Vehicle Makes (136 vehicles)

1
FORD23 (16.9%)
↓-4.2%prior 24
2
CHEV16 (11.8%)
↓-36.0%prior 25
3
CHEVROLET15 (11%)
↑114.3%prior 7
4
GMC7 (5.1%)
5
BUIC7 (5.1%)
6
DODG7 (5.1%)
↓-12.5%prior 8
7
DODGE6 (4.4%)
8
TOYT3 (2.2%)
↓-50.0%prior 6
9
JEP3 (2.2%)
10
NR3 (2.2%)

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

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

Sex Distribution (105 persons with recorded sex)

Male66 (62.9%)
↓-7.0%prior 71
Female39 (37.1%)
↓-2.5%prior 40

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: 92
  • Total persons involved: 182
  • Total vehicles involved: 136

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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