Yearly Traffic Safety Analysis

202 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Allamakee County, total traffic crashes increased by 11%, rising from 182 in 2019 to 202 in 2020. While fatalities remained unchanged at two, the most notable shift was a 58% increase in the count of crashes attributed to drivers losing control, which grew from 19 to 30 incidents.

202

▲ 11.0%was 182

Total Crash Events

2

Persons Killed

63

▲ 1.6%was 62

Persons Injured

2

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

Overall, Allamakee County experienced an upward trend in traffic incidents, with total crashes increasing by 11% from 182 in 2019 to 202 in 2020. The number of fatalities held steady at two for both years, and total injuries saw a minimal increase from 62 to 63.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

2

Pedestrians Injured

Prior: 2→0.0%

61

Motorists Injured

Prior: 60↑1.7%

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 year-over-year. In 2020, the peak day for crashes was Friday with 40 incidents, a change from 2019 when Wednesday was the peak with 41 crashes. The busiest time of day also moved from midday (12 PM with 16 crashes) in 2019 to the late afternoon commute in 2020, which peaked at 5 PM with 15 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 profile of crashes remained largely consistent between the two periods. The number of fatal crashes was unchanged at two, though the fatal crash rate decreased slightly from 1.1% to 1.0% due to the higher total crash volume. The proportion of crashes involving any type of injury was stable, accounting for 26.8% of all crashes in 2020 compared to 27.5% in 2019.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1%
→0.0%prior 2
Serious Injury6serious injury crashes3%
→0.0%prior 6
Minor Injury19minor injury crashes9.4%
↑5.6%prior 18
Possible Injury29possible injury crashes14.4%
↑11.5%prior 26
No Injury146no injury crashes72.3%
↑12.3%prior 130

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 with animals remained the top contributing factor in both years, with the count rising from 56 in 2019 to 61 in 2020. The most significant change was in crashes attributed to 'Lost Control,' which increased in count by 58% from 19 incidents to 30. Conversely, crashes where 'Driving too fast for conditions' was a factor decreased from 15 to 12, and those involving failure to yield from a stop sign dropped from 13 to 5.

Officer-Reported Primary Contributing Cause

Animal61 (30.2%)↑8.9%prior 56
Lost Control30 (14.9%)↑57.9%prior 19
Driving too fast for conditions12 (5.9%)↓-20.0%prior 15
Ran off road - straight9 (4.5%)↓-18.2%prior 11
Other (explain in narrative): Other7 (3.5%)→0.0%prior 7
Ran off road - left7 (3.5%)↑16.7%prior 6
Followed too close6 (3%)
Driver Distraction: Other interior distraction6 (3%)↑20.0%prior 5
FTYROW: From stop sign5 (2.5%)↓-61.5%prior 13
Improper Backing5 (2.5%)

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

Road & Environmental Conditions

Year-over-year, a larger number of crashes occurred in favorable conditions. Crashes on dry roads increased from 102 to 124, and those in clear weather rose from 106 to 143. In contrast, incidents on roads with snow or ice decreased from a combined 33 in 2019 to 18 in 2020. The proportion of crashes occurring in darkness remained relatively stable, accounting for approximately 29% of crashes in 2020 versus 27% in 2019.

Weather

Clear143 (81.7%)
↑34.9%prior 106
Cloudy15 (8.6%)
↓-44.4%prior 27
Snow8 (4.6%)
↓-11.1%prior 9
Rain7 (4.0%)
↓-36.4%prior 11
Fog, smoke, smog1 (0.6%)
Freezing rain/drizzle1 (0.6%)

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

Lighting

Daylight105 (60.0%)
↑6.1%prior 99
Dark - roadway not lighted39 (22.3%)
↑5.4%prior 37
Dark - roadway lighted17 (9.7%)
↑41.7%prior 12
Dusk8 (4.6%)
↓-20.0%prior 10
Dawn4 (2.3%)
Dark - unknown roadway lighting2 (1.1%)

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

Road Surface

Dry124 (71.7%)
↑21.6%prior 102
Wet15 (8.7%)
↓-16.7%prior 18
Snow13 (7.5%)
↓-31.6%prior 19
Gravel13 (7.5%)
↑116.7%prior 6
Ice/frost5 (2.9%)
↓-64.3%prior 14
Slush2 (1.2%)
Sand1 (0.6%)

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, Chevrolet and Ford, remained consistent across both years. Analysis of persons involved shows that while the 65+ age group was the largest cohort in both periods, there was a substantial increase in the number of younger individuals involved in crashes. The count of persons aged 16-20 rose from 32 to 55, and the count for the 26-34 age group increased from 39 to 55.

Top Vehicle Makes (275 vehicles)

1
FORD48 (17.5%)
↓-5.9%prior 51
2
CHEV43 (15.6%)
↑22.9%prior 35
3
CHEVROLET29 (10.5%)
↑7.4%prior 27
4
JEEP15 (5.5%)
↑50.0%prior 10
5
DODG15 (5.5%)
↑15.4%prior 13
6
NR9 (3.3%)
7
TOYT7 (2.5%)
8
GMC7 (2.5%)
↓-36.4%prior 11
9
PONT6 (2.2%)
10
HONDA6 (2.2%)
↑20.0%prior 5

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

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

Sex Distribution (240 persons with recorded sex)

Male156 (65.0%)
↑12.2%prior 139
Female84 (35.0%)
↓-6.7%prior 90

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: 202
  • Total persons involved: 412
  • Total vehicles involved: 275

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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