Yearly Traffic Safety Analysis

521 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In Marion County, total traffic crashes increased by 3.8%, from 502 in 2020 to 521 in 2021. While total injuries remained stable, the most significant year-over-year change was a sharp rise in fatalities, which more than doubled from 5 to 11. Consequently, the number of fatal crashes increased from 4 in the prior year to 7 in the current period.

521

▲ 3.8%was 502

Total Crash Events

11

▲ 120.0%was 5

Persons Killed

132

▲ 0.8%was 131

Persons Injured

7

▲ 75.0%was 4

Fatal Crash Events

Note: "Persons Killed" (11) counts individual fatalities across all crash events. "Fatal" in the severity table below (7) 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 traffic safety trends in Marion County showed a slight worsening from 2020 to 2021. The total number of crashes rose by 3.8% from 502 to 521. This increase was accompanied by a more than doubling of fatalities, from 5 to 11, even as the total number of injuries remained nearly unchanged, with 132 in 2021 compared to 131 in 2020.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 1↓-100.0%

11

Motorists Killed

Prior: 4↑175.0%

3

Pedestrians Injured

Prior: 6↓-50.0%

1

Cyclists Injured

Prior: 2↓-50.0%

128

Motorists Injured

Prior: 123↑4.1%

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 timing of crashes shifted between the two periods. In 2021, the peak day for crashes was Wednesday with 85 incidents, a change from 2020 when Friday was the peak day with 87 crashes. The peak hour also moved later in the day, from 3 p.m. in 2020 (45 crashes) to 6 p.m. in 2021 (45 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 year-over-year, with the fatal crash rate increasing from 0.8% of all crashes in 2020 to 1.3% in 2021. The absolute number of fatal crashes grew from 4 to 7. Conversely, the proportion of crashes resulting in any level of injury (serious, minor, or possible) decreased from a combined 22.0% in 2020 to 19.5% in 2021, while the share of no-injury crashes rose from 77.3% to 79.1%.

Severity is per crash event (most severe injury). 7 fatal crash events resulted in 11 persons killed.

Outcome by Severity (Crash Events)

Fatal7fatal crashes1.3%
↑75.0%prior 4
Serious Injury11serious injury crashes2.1%
↓-15.4%prior 13
Minor Injury46minor injury crashes8.8%
↓-8.0%prior 50
Possible Injury45possible injury crashes8.6%
↓-4.3%prior 47
No Injury412no injury crashes79.1%
↑6.2%prior 388

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 counts increasing slightly from 164 in 2020 to 170 in 2021. The ranking of the top factors remained largely consistent, though some counts shifted. Crashes attributed to 'Followed too close' increased significantly in count from 28 to 41, while incidents involving 'Lost Control' decreased from 37 to 32. 'Ran Stop Sign' crashes also saw a notable increase in count from 9 to 16.

Officer-Reported Primary Contributing Cause

Animal170 (32.6%)↑3.7%prior 164
Followed too close41 (7.9%)↑46.4%prior 28
Other (explain in narrative): Other40 (7.7%)↑60.0%prior 25
Lost Control32 (6.1%)↓-13.5%prior 37
Operating vehicle in an reckless, erratic, careless, negligent manner22 (4.2%)↓-4.3%prior 23
Ran off road - straight18 (3.5%)↑12.5%prior 16
Ran off road - left17 (3.3%)↑13.3%prior 15
FTYROW: From stop sign17 (3.3%)↓-15.0%prior 20
Ran Stop Sign16 (3.1%)↑77.8%prior 9
Other (explain in narrative): No improper action16 (3.1%)↑33.3%prior 12

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

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with clear weather and daylight continuing to be the most common circumstances for incidents in both 2020 and 2021. The proportion of crashes occurring in daylight was nearly identical, at 49.9% in 2021 versus 52.0% in 2020. There was a slight increase in crashes on non-dry road surfaces, which accounted for 97 incidents in 2021 compared to 90 in the prior year.

Weather

Clear265 (71.0%)
↑2.3%prior 259
Cloudy72 (19.3%)
↓-4.0%prior 75
Rain17 (4.6%)
↑41.7%prior 12
Snow13 (3.5%)
↓-18.8%prior 16
Blowing Snow2 (0.5%)
Fog, smoke, smog2 (0.5%)
Severe Winds1 (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

Daylight260 (69.5%)
↓-0.4%prior 261
Dark - roadway not lighted67 (17.9%)
↓-4.3%prior 70
Dark - roadway lighted27 (7.2%)
→0.0%prior 27
Dawn11 (2.9%)
↑57.1%prior 7
Dusk9 (2.4%)
↑12.5%prior 8

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

Road Surface

Dry277 (74.1%)
↓-2.5%prior 284
Gravel32 (8.6%)
↑88.2%prior 17
Wet29 (7.8%)
↑7.4%prior 27
Snow23 (6.1%)
↑4.5%prior 22
Ice/frost11 (2.9%)
↓-38.9%prior 18
Sand1 (0.3%)
Slush1 (0.3%)
↓-83.3%prior 6

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, Ford and Chevrolet, maintained their top two rankings in both 2020 and 2021 with nearly identical involvement counts. A notable demographic shift occurred in the age of persons involved in crashes; the 16-20 age group saw its count decrease from 168 to 120. In contrast, the number of persons in the 55-64 age group involved in crashes increased from 103 in 2020 to 124 in 2021.

Top Vehicle Makes (774 vehicles)

1
FORD170 (22%)
↑3.7%prior 164
2
CHEV112 (14.5%)
↑4.7%prior 107
3
CHEVROLET48 (6.2%)
↓-5.9%prior 51
4
JEEP42 (5.4%)
↑35.5%prior 31
5
DODG33 (4.3%)
↓-2.9%prior 34
6
GMC28 (3.6%)
↑16.7%prior 24
7
HOND23 (3%)
→0.0%prior 23
8
NISS21 (2.7%)
↑16.7%prior 18
9
BUIC19 (2.5%)
↑72.7%prior 11
10
DODGE18 (2.3%)
↑12.5%prior 16

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

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

Sex Distribution (609 persons with recorded sex)

Male362 (59.4%)
↓-13.8%prior 420
Female247 (40.6%)
↓-5.7%prior 262

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: 521
  • Total persons involved: 1,004
  • Total vehicles involved: 774

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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