Yearly Traffic Safety Analysis

281 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Buchanan County, total crashes decreased from 343 in 2019 to 281 in 2020, an 18.1% reduction. While this overall decline in collisions was the most significant trend, crashes involving driving under the influence (DUI) increased from 7 to 13 incidents year-over-year.

281

▼ -18.1%was 343

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

67

▼ -30.9%was 97

Persons Injured

2

▼ -33.3%was 3

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

Traffic collisions in Buchanan County showed a clear downward trend from 2019 to 2020. The total number of crashes fell by 18.1%, from 343 to 281. This was accompanied by a decrease in related harms, with total injuries dropping from 97 to 67 and fatalities declining from 3 to 2.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

1

Cyclists Injured

Prior: 1→0.0%

66

Motorists Injured

Prior: 96↓-31.3%

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 timing of crashes shifted between the two periods. In 2020, the peak day for crashes was Friday with 49 incidents, and the peak hour was 1 p.m. with 20 crashes. This contrasts with 2019, when the peak day was Tuesday (60 crashes) and the peak hour was during the evening commute at 5 p.m. (38 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. The number of fatal crashes dropped from 3 to 2, and the fatal crash rate declined from 0.87% to 0.71%. The proportion of crashes resulting in serious injuries also fell, from 2.0% to 1.4% of all incidents. Correspondingly, the share of crashes with no reported injuries increased from 76.4% in 2019 to 79.7% in 2020.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.7%
↓-33.3%prior 3
Serious Injury4serious injury crashes1.4%
↓-42.9%prior 7
Minor Injury22minor injury crashes7.8%
↓-15.4%prior 26
Possible Injury29possible injury crashes10.3%
↓-35.6%prior 45
No Injury224no injury crashes79.7%
↓-14.5%prior 262

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 years, though the count decreased from 108 in 2019 to 101 in 2020. The number of crashes attributed to 'Driving too fast for conditions' fell by 41.4%, from 29 to 17 incidents. In contrast, crashes where a driver was cited for 'Operating vehicle in an reckless, erratic, careless, negligent manner' increased from 4 incidents in 2019 to 9 in 2020.

Officer-Reported Primary Contributing Cause

Animal101 (35.9%)↓-6.5%prior 108
Followed too close20 (7.1%)↑5.3%prior 19
Lost Control18 (6.4%)↓-21.7%prior 23
Driving too fast for conditions17 (6%)↓-41.4%prior 29
Ran off road - left13 (4.6%)↑8.3%prior 12
Operating vehicle in an reckless, erratic, careless, negligent manner9 (3.2%)
FTYROW: From stop sign9 (3.2%)↓-40.0%prior 15
Ran off road - straight9 (3.2%)↓-43.8%prior 16
Other (explain in narrative): Other7 (2.5%)↓-69.6%prior 23
Exceeded authorized speed5 (1.8%)

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

Road & Environmental Conditions

In 2020, a greater share of crashes occurred on dry road surfaces compared to 2019. Incidents on adverse surfaces like snow, ice, or slush saw a notable decrease, dropping from a combined 71 crashes in 2019 to 29 in 2020. The distribution of crashes by lighting conditions remained relatively stable, with daylight crashes accounting for the largest share in both periods.

Weather

Clear134 (70.5%)
↓-12.4%prior 153
Cloudy25 (13.2%)
↓-46.8%prior 47
Snow13 (6.8%)
↑8.3%prior 12
Rain7 (3.7%)
↓-41.7%prior 12
Fog, smoke, smog6 (3.2%)
Freezing rain/drizzle3 (1.6%)
↓-75.0%prior 12
Severe Winds1 (0.5%)
Blowing Snow1 (0.5%)
↓-87.5%prior 8

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

Lighting

Daylight127 (66.1%)
↓-31.4%prior 185
Dark - roadway not lighted49 (25.5%)
↑6.5%prior 46
Dark - roadway lighted8 (4.2%)
↓-27.3%prior 11
Dawn4 (2.1%)
Dusk4 (2.1%)
↓-42.9%prior 7

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

Road Surface

Dry131 (68.6%)
↓-13.8%prior 152
Wet21 (11.0%)
↓-25.0%prior 28
Snow14 (7.3%)
↓-54.8%prior 31
Ice/frost13 (6.8%)
↓-58.1%prior 31
Gravel9 (4.7%)
Slush2 (1.0%)
↓-77.8%prior 9
Mud, dirt1 (0.5%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes, Ford and Chevrolet, remained the same across both years, though their total counts declined in line with the overall trend. Ford-involved vehicles decreased from 88 to 69, while Chevrolet-involved vehicles dropped from a combined 120 to 95. Analysis of person age distribution shows a notable increase in the 55-64 age group, which grew from 71 persons involved in 2019 to 91 in 2020, bucking the general downward trend seen in most other age brackets.

Top Vehicle Makes (389 vehicles)

1
CHEV74 (19%)
↓-3.9%prior 77
2
FORD69 (17.7%)
↓-21.6%prior 88
3
CHEVROLET21 (5.4%)
↓-51.2%prior 43
4
DODG20 (5.1%)
↓-9.1%prior 22
5
PONT14 (3.6%)
→0.0%prior 14
6
DODGE12 (3.1%)
↑71.4%prior 7
7
CHRY10 (2.6%)
↓-33.3%prior 15
8
KIA10 (2.6%)
↑25.0%prior 8
9
JEEP9 (2.3%)
↓-47.1%prior 17
10
FREIGHTLINER8 (2.1%)
↓-27.3%prior 11

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

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

Sex Distribution (363 persons with recorded sex)

Male224 (61.7%)
↓-11.1%prior 252
Female139 (38.3%)
↓-35.3%prior 215

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 10, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 281
  • Total persons involved: 566
  • Total vehicles involved: 389

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 10, 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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