Yearly Traffic Safety Analysis

376 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In Cedar County, total traffic crashes increased from 340 in 2020 to 376 in 2021, representing a 10.6% rise. While the number of fatalities decreased from 5 to 4, the number of injuries saw a significant increase of 28.4%, rising from 67 to 86 year-over-year. The overall increase in both crashes and injuries marks the most substantial change between the two periods.

376

▲ 10.6%was 340

Total Crash Events

4

▼ -20.0%was 5

Persons Killed

86

▲ 28.4%was 67

Persons Injured

4

▲ 33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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

Traffic safety trends in Cedar County showed a negative turn from 2020 to 2021, with total crashes increasing by 10.6% from 340 to 376. This rise was accompanied by a 28.4% increase in total injuries, which grew from 67 to 86. Conversely, the number of fatalities saw a slight decrease from 5 in 2020 to 4 in 2021.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 5↓-20.0%

2

Pedestrians Injured

Prior: 3↓-33.3%

84

Motorists Injured

Prior: 63↑33.3%

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 Thursday with 66 incidents, a change from 2020 when Wednesday was the peak day with 59 incidents. Similarly, the peak hour moved from 6 PM in 2020 (27 crashes) to 3 PM in 2021 (28 crashes), indicating a shift in the most frequent crash time from the evening commute to the mid-afternoon.

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 total fatalities decreased, the number of fatal crashes increased from 3 in 2020 to 4 in 2021, pushing the fatal crash rate up from 0.88 to 1.06 per 100 crashes. The overall severity of crashes worsened, with the proportion of crashes involving any injury rising from 16.8% (57 crashes) in 2020 to 19.1% (72 crashes) in 2021. This was driven by a notable increase in minor injury crashes, which grew from 26 to 40 year-over-year.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.1%
↑33.3%prior 3
Serious Injury7serious injury crashes1.9%
↓-22.2%prior 9
Minor Injury40minor injury crashes10.6%
↑53.8%prior 26
Possible Injury25possible injury crashes6.6%
↑13.6%prior 22
No Injury300no injury crashes79.8%
↑7.1%prior 280

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 years, with counts increasing from 93 in 2020 to 97 in 2021. 'Ran off road - straight' incidents also grew, rising from 34 to 43, while 'Lost Control' crashes increased from 29 to 38. Notably, crashes attributed to 'Followed too close' increased significantly, jumping from 14 incidents in 2020 to 27 in 2021, making it the fifth most common factor in the current period.

Officer-Reported Primary Contributing Cause

Animal97 (25.8%)↑4.3%prior 93
Ran off road - straight43 (11.4%)↑26.5%prior 34
Lost Control38 (10.1%)↑31.0%prior 29
Driving too fast for conditions31 (8.2%)↓-6.1%prior 33
Followed too close27 (7.2%)↑92.9%prior 14
Operating vehicle in an reckless, erratic, careless, negligent manner16 (4.3%)↑166.7%prior 6
Ran off road - left14 (3.7%)↓-22.2%prior 18
FTYROW: From stop sign10 (2.7%)↓-9.1%prior 11
Driver Distraction: Other interior distraction10 (2.7%)↑25.0%prior 8
Other (explain in narrative): No improper action8 (2.1%)↑14.3%prior 7

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

Road & Environmental Conditions

Crashes in 2021 were more likely to occur in ideal conditions compared to the prior year. The proportion of crashes on dry roads increased from 43.8% in 2020 to 50.5% in 2021, and incidents in clear weather grew from 43.8% to 50.0% of the total. While the absolute number of crashes in dark conditions was identical at 85 for both years, their share of total crashes decreased from 25.0% in 2020 to 22.6% in 2021.

Weather

Clear188 (65.5%)
↑26.2%prior 149
Cloudy42 (14.6%)
↑40.0%prior 30
Snow19 (6.6%)
↓-26.9%prior 26
Rain15 (5.2%)
↓-6.3%prior 16
Blowing Snow10 (3.5%)
Freezing rain/drizzle5 (1.7%)
↓-68.8%prior 16
Fog, smoke, smog4 (1.4%)
↓-20.0%prior 5
Severe Winds1 (0.3%)
Sleet, hail1 (0.3%)
Other (explain in narrative)1 (0.3%)

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

Lighting

Daylight186 (64.6%)
↑18.5%prior 157
Dark - roadway not lighted61 (21.2%)
↓-12.9%prior 70
Dark - roadway lighted22 (7.6%)
↑120.0%prior 10
Dusk10 (3.5%)
↑100.0%prior 5
Dawn7 (2.4%)
→0.0%prior 7
Dark - unknown roadway lighting2 (0.7%)
↓-60.0%prior 5

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

Road Surface

Dry190 (65.5%)
↑27.5%prior 149
Ice/frost28 (9.7%)
↓-9.7%prior 31
Wet26 (9.0%)
→0.0%prior 26
Snow25 (8.6%)
↓-3.8%prior 26
Gravel18 (6.2%)
↑28.6%prior 14
Slush3 (1.0%)

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

Vehicles & Demographics

The vehicle makes most frequently involved in crashes saw a shift in ranking. Ford became the top make with 85 vehicles in 2021, an increase from 69 in 2020, while Chevrolet's involvement decreased from 85 to 79 vehicles. Demographically, there were significant shifts among age groups of persons involved in crashes. The number of individuals aged 16-20 dropped from 101 to 71, while involvement for the 45-54 age group rose from 78 to 106 and the 65+ group increased from 58 to 77.

Top Vehicle Makes (533 vehicles)

1
FORD85 (15.9%)
↑23.2%prior 69
2
CHEV42 (7.9%)
↓-19.2%prior 52
3
CHEVROLET37 (6.9%)
↑12.1%prior 33
4
FREIGHTLINER19 (3.6%)
↓-26.9%prior 26
5
KENWORTH18 (3.4%)
6
TOYT18 (3.4%)
↑28.6%prior 14
7
VOLVO17 (3.2%)
↑41.7%prior 12
8
TOYOTA16 (3%)
↓-11.1%prior 18
9
JEEP16 (3%)
↑23.1%prior 13
10
RAM15 (2.8%)
↑36.4%prior 11

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

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

Sex Distribution (431 persons with recorded sex)

Male286 (66.4%)
↓-1.7%prior 291
Female145 (33.6%)
↓-5.2%prior 153

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: 376
  • Total persons involved: 673
  • Total vehicles involved: 533

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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