Yearly Traffic Safety Analysis

129 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Union County, total traffic crashes decreased by 13.4% from 149 in 2019 to 129 in 2020. Despite this overall reduction in collisions, the number of fatalities recorded in crashes tripled, increasing from one in 2019 to three in 2020.

129

▼ -13.4%was 149

Total Crash Events

3

▲ 200.0%was 1

Persons Killed

54

▲ 8.0%was 50

Persons Injured

3

▲ 200.0%was 1

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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

Traffic safety trends in Union County presented a mixed picture year-over-year. While the total number of crashes fell from 149 to 129, a 13.4% decrease, negative trends were observed in crash outcomes. Total fatalities increased from one to three, and the number of people injured rose by 8%, from 50 in 2019 to 54 in 2020.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 1↑200.0%

2

Pedestrians Injured

Prior: 0→%

52

Motorists Injured

Prior: 49↑6.1%

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

The temporal patterns of crashes showed some shifts between the two periods. Thursday remained the peak day for crashes in both 2019 (28 crashes) and 2020 (25 crashes). However, the peak hour for collisions moved earlier in the day, shifting from 3 p.m. in 2019 (13 crashes) to 12 p.m. in 2020 (12 crashes).

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

The severity of crashes worsened year-over-year, even as the total number of incidents declined. The number of fatal crashes increased from one to three, raising the fatal crash rate from 0.7% to 2.3% of all crashes. Conversely, serious injury crashes saw a significant drop, from 10 incidents in 2019 to 3 in 2020. Crashes resulting in possible injuries increased, accounting for 22.5% of incidents in 2020 compared to 14.8% in the prior year.

Outcome by Severity (Crash Events)

Fatal3fatal crashes2.3%
↑200.0%prior 1
Serious Injury3serious injury crashes2.3%
↓-70.0%prior 10
Minor Injury14minor injury crashes10.9%
↑16.7%prior 12
Possible Injury29possible injury crashes22.5%
↑31.8%prior 22
No Injury80no injury crashes62%
↓-23.1%prior 104

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

The primary contributing factors for crashes showed some changes between 2019 and 2020. "Failure to yield from a stop sign" became the leading cause in 2020 with 12 incidents, up from 10 the previous year. Incidents of "following too close" decreased slightly from 12 to 11. Notably, crashes attributed to a driver losing control increased from 7 in 2019 to 9 in 2020.

Officer-Reported Primary Contributing Cause

FTYROW: From stop sign12 (9.3%)↑20.0%prior 10
Followed too close11 (8.5%)↓-8.3%prior 12
FTYROW: Making left turn10 (7.8%)↑66.7%prior 6
Ran off road - left10 (7.8%)→0.0%prior 10
Lost Control9 (7%)↑28.6%prior 7
Other (explain in narrative): Other8 (6.2%)↓-33.3%prior 12
Animal7 (5.4%)↓-12.5%prior 8
Ran off road - straight7 (5.4%)↑16.7%prior 6
Driver Distraction: Other interior distraction7 (5.4%)
FTYROW: Other (explain in narrative)7 (5.4%)↓-30.0%prior 10

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 were less frequently associated with adverse conditions in 2020 compared to 2019. The combined number of crashes on snowy or icy roads fell from 22 incidents in 2019 to 10 in 2020. Similarly, crashes during snowy weather decreased from 8 to 4. The number of crashes occurring in clear weather remained identical at 92 incidents in both years.

Weather

Clear92 (74.2%)
→0.0%prior 92
Cloudy20 (16.1%)
↓-31.0%prior 29
Snow4 (3.2%)
↓-50.0%prior 8
Freezing rain/drizzle3 (2.4%)
Fog, smoke, smog2 (1.6%)
Rain2 (1.6%)
↓-66.7%prior 6
Severe Winds1 (0.8%)

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

Lighting

Daylight95 (77.2%)
↓-13.6%prior 110
Dark - roadway not lighted12 (9.8%)
↓-29.4%prior 17
Dark - roadway lighted11 (8.9%)
↑10.0%prior 10
Dawn3 (2.4%)
↓-57.1%prior 7
Dusk2 (1.6%)

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

Road Surface

Dry102 (82.9%)
↓-1.0%prior 103
Wet8 (6.5%)
↓-20.0%prior 10
Snow5 (4.1%)
↓-58.3%prior 12
Ice/frost5 (4.1%)
↓-50.0%prior 10
Slush2 (1.6%)
Water (standing or moving)1 (0.8%)

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

Vehicles & Demographics

Ford and Chevrolet were the top two vehicle makes involved in crashes in both periods, with both seeing a decrease in total involvement from 2019 to 2020. The age distribution of persons involved in crashes shifted notably; involvement for the 65+ age group decreased from 55 to 30 persons, while the 45-54 age group saw an increase from 26 to 46 persons, and the 16-20 age group increased from 44 to 55 persons.

Top Vehicle Makes (227 vehicles)

1
FORD48 (21.1%)
↓-7.7%prior 52
2
CHEV35 (15.4%)
↓-22.2%prior 45
3
CHEVROLET19 (8.4%)
↓-26.9%prior 26
4
GMC13 (5.7%)
↑18.2%prior 11
5
JEEP12 (5.3%)
↑33.3%prior 9
6
DODG11 (4.8%)
↓-8.3%prior 12
7
NISS7 (3.1%)
8
BUICK5 (2.2%)
9
CHRYSLER5 (2.2%)
↓-16.7%prior 6
10
BUIC5 (2.2%)
↓-58.3%prior 12

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

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

Sex Distribution (207 persons with recorded sex)

Male122 (58.9%)
↓-17.6%prior 148
Female85 (41.1%)
↓-11.5%prior 96

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: 129
  • Total persons involved: 304
  • Total vehicles involved: 227

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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