Yearly Traffic Safety Analysis

854 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Cerro Gordo County recorded 854 total crashes, a 19.1% decrease from the 1,056 crashes reported in 2019. This overall reduction in collisions was accompanied by a significant drop in crash-related fatalities, which fell from 7 in the prior year to 2 in the current year. The most notable shift was a substantial decrease in crashes occurring on adverse road surfaces like snow and ice.

854

▼ -19.1%was 1,056

Total Crash Events

2

▼ -71.4%was 7

Persons Killed

241

▼ -19.4%was 299

Persons Injured

2

▼ -66.7%was 6

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

Crash trends in Cerro Gordo County showed a significant downward movement year-over-year. Total collisions fell by 202 incidents, representing a 19.1% decline from 2019 to 2020. This trend extended to crash severity, as the number of people injured decreased from 299 to 241, and fatalities dropped from 7 to 2.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 7↓-85.7%

4

Pedestrians Injured

Prior: 7↓-42.9%

5

Cyclists Injured

Prior: 4↑25.0%

232

Motorists Injured

Prior: 287↓-19.2%

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 2019 and 2020. While Friday remained the peak day for crashes in both years, the count on that day decreased from 190 to 147. The peak hour for collisions shifted later in the day, moving from the 3 p.m. hour in 2019 (96 crashes) to the 6 p.m. hour in 2020 (73 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 decreased from 2019 to 2020, with the number of fatal crashes dropping from 6 to 2. Consequently, the fatal crash rate fell from 0.6% to 0.2% of all crashes. While the count of serious injury crashes declined from 14 to 11, their share of total incidents remained stable at 1.3%. Crashes involving possible injuries made up a larger share of the total in 2020 (14.5%) compared to 2019 (12.5%).

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.2%
↓-66.7%prior 6
Serious Injury11serious injury crashes1.3%
↓-21.4%prior 14
Minor Injury62minor injury crashes7.3%
↓-27.9%prior 86
Possible Injury124possible injury crashes14.5%
↓-6.1%prior 132
No Injury655no injury crashes76.7%
↓-19.9%prior 818

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 leading contributing factor in both periods, though the count decreased slightly from 164 incidents in 2019 to 154 in 2020. Other top factors saw more significant year-over-year reductions in count; crashes attributed to 'Driving too fast for conditions' fell from 93 to 59, and incidents involving 'Followed too close' dropped from 85 to 58. 'Failure to yield from a stop sign' also decreased as a factor, from 81 crashes in 2019 to 57 in 2020.

Officer-Reported Primary Contributing Cause

Animal154 (18%)↓-6.1%prior 164
Other (explain in narrative): Other61 (7.1%)↓-33.0%prior 91
Driving too fast for conditions59 (6.9%)↓-36.6%prior 93
Followed too close58 (6.8%)↓-31.8%prior 85
FTYROW: From stop sign57 (6.7%)↓-29.6%prior 81
Ran off road - left37 (4.3%)↓-27.5%prior 51
FTYROW: At uncontrolled intersection30 (3.5%)↑7.1%prior 28
Driver Distraction: Other interior distraction28 (3.3%)→0.0%prior 28
FTYROW: Making left turn27 (3.2%)↓-25.0%prior 36
Lost Control26 (3%)↓-21.2%prior 33

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

Road & Environmental Conditions

While crashes in both years predominantly occurred in clear weather and daylight, there was a significant shift in road surface conditions. In 2020, a larger share of crashes happened on dry roads (63.0%) compared to 2019 (50.6%). The absolute number of crashes on snow, ice, or slush-covered roads decreased substantially, from 249 incidents in 2019 to 118 in 2020, suggesting different weather patterns between the two years.

Weather

Clear481 (66.5%)
↓-13.3%prior 555
Cloudy149 (20.6%)
↓-26.6%prior 203
Snow43 (5.9%)
↓-15.7%prior 51
Rain20 (2.8%)
↓-61.5%prior 52
Freezing rain/drizzle13 (1.8%)
↓-35.0%prior 20
Blowing Snow9 (1.2%)
↓-70.0%prior 30
Fog, smoke, smog3 (0.4%)
↓-40.0%prior 5
Severe Winds2 (0.3%)
Other (explain in narrative)2 (0.3%)
Sleet, hail1 (0.1%)

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

Lighting

Daylight503 (69.6%)
↓-25.7%prior 677
Dark - roadway lighted105 (14.5%)
↓-9.5%prior 116
Dark - roadway not lighted78 (10.8%)
↓-16.1%prior 93
Dusk27 (3.7%)
↑42.1%prior 19
Dawn8 (1.1%)
↓-38.5%prior 13
Dark - unknown roadway lighting2 (0.3%)
↓-66.7%prior 6

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

Road Surface

Dry538 (74.1%)
↑0.7%prior 534
Snow63 (8.7%)
↓-54.0%prior 137
Wet62 (8.5%)
↓-54.1%prior 135
Ice/frost49 (6.7%)
↓-49.0%prior 96
Slush6 (0.8%)
↓-62.5%prior 16
Gravel6 (0.8%)
↑20.0%prior 5
Mud, dirt2 (0.3%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes were consistent year-over-year, with Chevrolet and Ford vehicles accounting for the majority in both periods, though their total counts declined. An analysis of persons involved in crashes shows a shift in the most represented age group. In 2019, the 55-64 age group had the highest involvement with 371 individuals, while in 2020, the 35-44 age group was most prevalent with 268 individuals.

Top Vehicle Makes (1,427 vehicles)

1
FORD254 (17.8%)
↓-25.7%prior 342
2
CHEV213 (14.9%)
↓-19.3%prior 264
3
CHEVROLET94 (6.6%)
↓-23.0%prior 122
4
TOYT67 (4.7%)
↓-25.6%prior 90
5
DODG53 (3.7%)
↓-31.2%prior 77
6
NISS49 (3.4%)
↑22.5%prior 40
7
GMC47 (3.3%)
↓-32.9%prior 70
8
BUIC39 (2.7%)
↑5.4%prior 37
9
NR39 (2.7%)
↑8.3%prior 36
10
HOND38 (2.7%)
↓-33.3%prior 57

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

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

Sex Distribution (1,271 persons with recorded sex)

Male707 (55.6%)
↓-22.6%prior 913
Female564 (44.4%)
↓-24.1%prior 743

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: 854
  • Total persons involved: 1,927
  • Total vehicles involved: 1,427

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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