Yearly Traffic Safety Analysis

70 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Mitchell County recorded 70 total crashes, a 25.5% decrease from the 94 crashes reported in 2019. While overall collisions and injuries declined, the most notable shift was an increase in crash severity, with fatalities doubling from one in 2019 to two in 2020.

70

▼ -25.5%was 94

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

34

▼ -27.7%was 47

Persons Injured

2

▲ 100.0%was 1

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 crashes in Mitchell County showed a downward trend year-over-year, with total collisions decreasing by 25.5% from 94 in 2019 to 70 in 2020. The number of people injured in these incidents also fell by 27.7%, from 47 to 34. However, fatalities increased, rising from one in 2019 to two in 2020.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 1↑100.0%

0

Other Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 0→%

32

Motorists Injured

Prior: 47↓-31.9%

1

Other Injured

Prior: 0→%

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 Monday with 16 incidents, a change from 2019 when Thursday was the peak day with 24 crashes. Similarly, the peak hour for collisions moved from 12 p.m. in 2019, which saw 13 crashes, to 5 p.m. in 2020, which saw 8 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

Although total crashes decreased, the severity of incidents increased in 2020 compared to 2019. The number of fatal crashes doubled from one to two, raising the fatal crash rate from 1.1% to 2.9% of all incidents. The proportion of crashes resulting in any level of injury also grew, accounting for 41.4% of crashes in 2020, up from 36.2% in the prior year.

Outcome by Severity (Crash Events)

Fatal2fatal crashes2.9%
↑100.0%prior 1
Serious Injury7serious injury crashes10%
↓-22.2%prior 9
Minor Injury10minor injury crashes14.3%
↓-23.1%prior 13
Possible Injury12possible injury crashes17.1%
→0.0%prior 12
No Injury39no injury crashes55.7%
↓-33.9%prior 59

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 leading contributing factors for crashes shifted between 2019 and 2020. In 2020, 'Lost Control' was the most cited factor with 13 incidents, an increase from 11 incidents in 2019. Conversely, crashes attributed to 'Failure to Yield Right of Way at an uncontrolled intersection' saw a count-based decrease of 64.3%, falling from 14 incidents in 2019 to 5 in 2020. The count of crashes involving 'Driving too fast for conditions' more than doubled, increasing from 3 to 7 year-over-year.

Officer-Reported Primary Contributing Cause

Lost Control13 (18.6%)↑18.2%prior 11
Driving too fast for conditions7 (10%)
FTYROW: At uncontrolled intersection5 (7.1%)↓-64.3%prior 14
FTYROW: From stop sign5 (7.1%)
Ran Stop Sign4 (5.7%)
Ran off road - straight4 (5.7%)
Animal3 (4.3%)
Followed too close3 (4.3%)
Other (explain in narrative): Other3 (4.3%)↓-72.7%prior 11
Operating vehicle in an reckless, erratic, careless, negligent manner2 (2.9%)↓-75.0%prior 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

The conditions under which crashes occurred remained largely consistent, with the majority of incidents in both years happening in daylight and clear weather. However, there was a notable shift in road surface conditions, as the number of crashes on adverse surfaces like snow, ice, or wet pavement was cut in half, from 30 in 2019 to 15 in 2020. Consequently, the share of crashes on dry roads increased from 61.7% in 2019 to 67.1% in 2020.

Weather

Clear46 (69.7%)
↓-20.7%prior 58
Cloudy12 (18.2%)
↓-33.3%prior 18
Blowing Snow2 (3.0%)
Other (explain in narrative)2 (3.0%)
Rain2 (3.0%)
Fog, smoke, smog1 (1.5%)
Snow1 (1.5%)
↓-80.0%prior 5

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

Lighting

Daylight50 (74.6%)
↓-25.4%prior 67
Dark - roadway not lighted12 (17.9%)
↓-20.0%prior 15
Dark - roadway lighted4 (6.0%)
↓-33.3%prior 6
Dawn1 (1.5%)

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

Road Surface

Dry47 (70.1%)
↓-19.0%prior 58
Snow6 (9.0%)
↓-62.5%prior 16
Wet5 (7.5%)
↓-28.6%prior 7
Ice/frost5 (7.5%)
Gravel3 (4.5%)
Slush1 (1.5%)

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

Vehicles & Demographics

The types of vehicles involved in crashes were similar year-over-year, with Chevrolet and Ford remaining the two most common makes in both 2019 and 2020, though their total counts decreased in line with the overall reduction in crashes. An analysis of persons involved shows that the 16-20 age group was the most represented demographic in both periods. This group's share of total persons involved increased slightly from 15.3% in 2019 (33 of 215 people) to 17.1% in 2020 (26 of 152 people).

Top Vehicle Makes (108 vehicles)

1
CHEV16 (14.8%)
↓-46.7%prior 30
2
FORD12 (11.1%)
↓-36.8%prior 19
3
CHEVROLET11 (10.2%)
↓-42.1%prior 19
4
GMC7 (6.5%)
5
JEEP6 (5.6%)
6
DODGE5 (4.6%)
7
INTERNATIONA3 (2.8%)
8
MERC3 (2.8%)
9
BUIC3 (2.8%)
10
DODG2 (1.9%)
↓-77.8%prior 9

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

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

Sex Distribution (97 persons with recorded sex)

Male63 (64.9%)
↓-25.0%prior 84
Female34 (35.1%)
↓-37.0%prior 54

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: 70
  • Total persons involved: 152
  • Total vehicles involved: 108

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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