Yearly Traffic Safety Analysis

305 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Hamilton County, total crashes decreased by 23.9% from 401 in 2019 to 305 in 2020. This downward trend was accompanied by a reduction in both fatalities, which fell from 3 to 1, and total injuries, which dropped from 120 to 94. The most notable year-over-year shift was this overall reduction in crashes, with significant decreases in incidents attributed to lost control and driving too fast for conditions.

305

▼ -23.9%was 401

Total Crash Events

1

▼ -66.7%was 3

Persons Killed

94

▼ -21.7%was 120

Persons Injured

1

▼ -66.7%was 3

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 crashes in Hamilton County showed a significant downward trend from 2019 to 2020. Total collisions fell by 23.9%, from 401 to 305 incidents. This decline was also reflected in personal harm, with total injuries decreasing by 21.7% (from 120 to 94) and fatalities dropping from 3 to 1.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 3↓-66.7%

1

Cyclists Injured

Prior: 1→0.0%

93

Motorists Injured

Prior: 118↓-21.2%

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 peak day for crashes shifted from Sunday (67 crashes) in 2019 to Friday (61 crashes) in 2020. The peak hour for collisions remained consistent at the 5 p.m. hour in both periods, although the crash count during this hour decreased from 27 to 22. Overall, the distribution of crashes in 2020 was more concentrated on weekdays compared to 2019.

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 number of fatal crashes decreased from 3 in 2019 to 1 in 2020, with the fatal crash rate falling from 0.75% to 0.33% of all incidents. While the total number of injury-related crashes declined, the count of 'Serious Injury' crashes increased from 10 to 12. Consequently, the share of crashes involving any level of injury (possible, minor, or serious) was slightly higher in 2020 at 26.2% compared to 22.4% in 2019.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
↓-66.7%prior 3
Serious Injury12serious injury crashes3.9%
↑20.0%prior 10
Minor Injury25minor injury crashes8.2%
↓-13.8%prior 29
Possible Injury43possible injury crashes14.1%
↓-15.7%prior 51
No Injury224no injury crashes73.4%
↓-27.3%prior 308

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 top contributing factor in both periods was 'Animal', with the count of these incidents decreasing by 22.4% from 85 in 2019 to 66 in 2020. The top four factors remained the same, but their ranking changed. Crashes attributed to 'Lost Control' saw a 37.8% count-based reduction from 45 to 28 incidents, and 'Driving too fast for conditions' fell by 31.0% from 42 to 29 incidents. This resulted in 'Ran off road - straight' becoming the second-most common factor in 2020 with 36 crashes, nearly unchanged from 37 in the prior year.

Officer-Reported Primary Contributing Cause

Animal66 (21.6%)↓-22.4%prior 85
Ran off road - straight36 (11.8%)↓-2.7%prior 37
Driving too fast for conditions29 (9.5%)↓-31.0%prior 42
Lost Control28 (9.2%)↓-37.8%prior 45
FTYROW: From stop sign20 (6.6%)↓-23.1%prior 26
Other (explain in narrative): Other19 (6.2%)↓-38.7%prior 31
Other (explain in narrative): No improper action11 (3.6%)↑57.1%prior 7
Ran Stop Sign11 (3.6%)↑120.0%prior 5
FTYROW: At uncontrolled intersection7 (2.3%)
Followed too close7 (2.3%)↓-46.2%prior 13

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 2020 were more likely to occur on dry roads and in clear weather compared to the previous year. Incidents on dry surfaces accounted for 53.8% of all crashes in 2020, up from a 43.4% share in 2019. Correspondingly, crashes on adverse road surfaces like ice or snow saw a significant drop, with incidents on icy/frosty roads falling from 51 in 2019 to 17 in 2020. Similarly, the proportion of crashes in clear weather increased from 40.6% to 48.2% year-over-year.

Weather

Clear147 (58.3%)
↓-9.8%prior 163
Cloudy51 (20.2%)
↓-23.9%prior 67
Snow24 (9.5%)
→0.0%prior 24
Rain13 (5.2%)
↓-31.6%prior 19
Blowing Snow5 (2.0%)
↓-77.3%prior 22
Freezing rain/drizzle4 (1.6%)
↓-66.7%prior 12
Severe Winds4 (1.6%)
↓-20.0%prior 5
Fog, smoke, smog3 (1.2%)
Sleet, hail1 (0.4%)

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

Lighting

Daylight153 (60.7%)
↓-26.8%prior 209
Dark - roadway not lighted50 (19.8%)
↓-31.5%prior 73
Dark - roadway lighted25 (9.9%)
↑31.6%prior 19
Dawn17 (6.7%)
↑30.8%prior 13
Dusk4 (1.6%)
↓-55.6%prior 9
Dark - unknown roadway lighting3 (1.2%)

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

Road Surface

Dry164 (65.1%)
↓-5.7%prior 174
Wet26 (10.3%)
↓-38.1%prior 42
Snow20 (7.9%)
↓-54.5%prior 44
Ice/frost17 (6.7%)
↓-66.7%prior 51
Gravel11 (4.4%)
↑120.0%prior 5
Slush10 (4.0%)
Other (explain in narrative)2 (0.8%)
Mud, dirt1 (0.4%)
Water (standing or moving)1 (0.4%)

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

Vehicles & Demographics

The top three vehicle makes involved in collisions were consistent across both periods: Chevrolet, Ford, and Dodge, with the count for each decreasing in 2020. An analysis of persons involved in crashes shows a shift in age demographics. The 26-34 age group's share of involved persons increased from 15.2% in 2019 to 18.3% in 2020, while the 16-20 age group's representation decreased from 13.0% to 11.3%.

Top Vehicle Makes (422 vehicles)

1
CHEV77 (18.2%)
↓-15.4%prior 91
2
FORD75 (17.8%)
↓-16.7%prior 90
3
CHEVROLET29 (6.9%)
↓-43.1%prior 51
4
DODGE19 (4.5%)
↑35.7%prior 14
5
TOYT17 (4%)
↓-19.0%prior 21
6
GMC17 (4%)
↓-19.0%prior 21
7
JEEP12 (2.8%)
↓-29.4%prior 17
8
DODG11 (2.6%)
↓-60.7%prior 28
9
VOLVO10 (2.4%)
10
NR9 (2.1%)
↑50.0%prior 6

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

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

Sex Distribution (390 persons with recorded sex)

Male263 (67.4%)
↓-20.1%prior 329
Female127 (32.6%)
↓-33.5%prior 191

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: 305
  • Total persons involved: 601
  • Total vehicles involved: 422

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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