Yearly Traffic Safety Analysis

344 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Boone County recorded 344 total crashes, a 13.1% decrease from the 396 crashes reported in 2019. Despite the overall decline in collisions and injuries, the most notable shift was a significant increase in traffic fatalities, which rose from 2 to 5 year-over-year.

344

▼ -13.1%was 396

Total Crash Events

5

▲ 150.0%was 2

Persons Killed

97

▼ -30.7%was 140

Persons Injured

5

▲ 150.0%was 2

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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in Boone County shows a decrease in traffic collisions from 2019 to 2020. Total crashes fell by 13.1%, from 396 to 344, and the number of people injured decreased by 30.7% from 140 to 97. In contrast to this downward trend, total fatalities more than doubled, increasing from 2 in 2019 to 5 in 2020.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 2↑150.0%

1

Pedestrians Injured

Prior: 4↓-75.0%

1

Cyclists Injured

Prior: 1→0.0%

95

Motorists Injured

Prior: 135↓-29.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal patterns shifted between the two periods, with the peak day for crashes moving from Monday (74 crashes) in 2019 to Friday (58 crashes) in 2020. The peak hour for collisions remained consistent at 5 PM in both years, although the number of crashes during this hour decreased from 35 to 27. The distribution of crashes across weekdays became more uniform in 2020 compared to the previous year, which saw a pronounced spike on Mondays.

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity outcomes worsened in 2020 despite fewer total crashes. The number of fatal crashes increased from 2 to 5, raising the fatal crash share from 0.5% to 1.5% of all collisions. While the proportion of crashes resulting in minor or possible injuries decreased from a combined 21.7% to 16.6%, the share of serious injury crashes saw a slight increase from 3.8% in 2019 to 4.7% in 2020.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.5%
↑150.0%prior 2
Serious Injury16serious injury crashes4.7%
↑6.7%prior 15
Minor Injury23minor injury crashes6.7%
↓-25.8%prior 31
Possible Injury34possible injury crashes9.9%
↓-38.2%prior 55
No Injury266no injury crashes77.3%
↓-9.2%prior 293

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both periods, though the count decreased from 119 in 2019 to 103 in 2020. 'Failure to yield from a stop sign' was the second most common factor in both years, with counts of 37 and 33, respectively. 'Followed too close' incidents increased in count from 21 to 26, moving up to the third-ranked factor in 2020. Conversely, crashes attributed to 'Ran Stop Sign' saw a notable decrease from 22 incidents in 2019 to 12 in 2020.

Officer-Reported Primary Contributing Cause

Animal103 (29.9%)↓-13.4%prior 119
FTYROW: From stop sign33 (9.6%)↓-10.8%prior 37
Followed too close26 (7.6%)↑23.8%prior 21
Lost Control25 (7.3%)↑4.2%prior 24
Driving too fast for conditions14 (4.1%)↑16.7%prior 12
Ran Stop Sign12 (3.5%)↓-45.5%prior 22
Ran off road - straight11 (3.2%)↓-21.4%prior 14
FTYROW: At uncontrolled intersection11 (3.2%)→0.0%prior 11
Other (explain in narrative): Other8 (2.3%)↓-46.7%prior 15
Improper Backing8 (2.3%)↑33.3%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in both periods occurred predominantly in clear weather and daylight conditions on dry roads, with the proportions remaining stable year-over-year. However, there was a notable decrease in crashes occurring on adverse road surfaces, which fell from 88 incidents (22.2% of total) in 2019 to 59 incidents (17.2% of total) in 2020. This was driven by a reduction in crashes on snow and ice-covered roads.

Weather

Clear165 (63.0%)
↓-10.3%prior 184
Cloudy59 (22.5%)
↓-20.3%prior 74
Rain15 (5.7%)
↑36.4%prior 11
Snow12 (4.6%)
→0.0%prior 12
Freezing rain/drizzle7 (2.7%)
↑40.0%prior 5
Fog, smoke, smog2 (0.8%)
Severe Winds1 (0.4%)
Blowing Snow1 (0.4%)
↓-90.9%prior 11

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight185 (70.6%)
↓-12.7%prior 212
Dark - roadway not lighted49 (18.7%)
↓-15.5%prior 58
Dark - roadway lighted13 (5.0%)
↓-7.1%prior 14
Dusk10 (3.8%)
↓-28.6%prior 14
Dawn4 (1.5%)
↓-50.0%prior 8
Dark - unknown roadway lighting1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry190 (72.2%)
↓-9.5%prior 210
Wet32 (12.2%)
→0.0%prior 32
Ice/frost14 (5.3%)
↓-36.4%prior 22
Gravel14 (5.3%)
↑133.3%prior 6
Snow11 (4.2%)
↓-63.3%prior 30
Mud, dirt1 (0.4%)
Slush1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

The ranking of vehicle makes involved in crashes saw some shifts between the two periods. While Chevrolet remained the top make with a nearly identical count (128 in 2019 vs 127 in 2020), Ford and Dodge vehicles saw their involvement decrease from 115 to 86 and 55 to 38, respectively. The age distribution of persons involved in crashes also changed, with a lower proportion of individuals aged 65 and older (12.5% in 2019 vs 8.8% in 2020) and a higher share of those aged 16-20 (11.2% in 2019 vs 12.9% in 2020).

Top Vehicle Makes (527 vehicles)

1
CHEV92 (17.5%)
↓-10.7%prior 103
2
FORD86 (16.3%)
↓-25.2%prior 115
3
CHEVROLET35 (6.6%)
↑40.0%prior 25
4
JEEP23 (4.4%)
↑43.8%prior 16
5
DODG22 (4.2%)
↓-43.6%prior 39
6
GMC22 (4.2%)
↑4.8%prior 21
7
TOYT20 (3.8%)
→0.0%prior 20
8
DODGE16 (3%)
→0.0%prior 16
9
NISS16 (3%)
↑14.3%prior 14
10
HOND14 (2.7%)
↓-12.5%prior 16

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

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

Sex Distribution (489 persons with recorded sex)

Male282 (57.7%)
↓-17.1%prior 340
Female207 (42.3%)
↓-11.9%prior 235

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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: 2020-01-01 through 2020-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 344
  • Total persons involved: 747
  • Total vehicles involved: 527

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: 2020." Published September 9, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2020-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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