Yearly Traffic Safety Analysis

260 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Mahaska County, total traffic crashes decreased from 299 in 2019 to 260 in 2020, a 13% reduction. Despite this overall decline in collisions, the number of fatalities more than doubled, increasing from 2 in 2019 to 5 in 2020. The number of fatal crashes also saw a significant rise, from 1 to 5 over the same period.

260

▼ -13.0%was 299

Total Crash Events

5

▲ 150.0%was 2

Persons Killed

91

▼ -14.2%was 106

Persons Injured

5

▲ 400.0%was 1

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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

Year-over-year data for Mahaska County indicates a downward trend in the total volume of traffic incidents. Crashes fell by 13.0%, from 299 in 2019 to 260 in 2020, and total injuries decreased by 14.2% from 106 to 91. However, this trend did not extend to the most severe outcomes, as total fatalities increased from 2 to 5 during the same period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 2↑150.0%

2

Pedestrians Injured

Prior: 3↓-33.3%

1

Cyclists Injured

Prior: 0→%

88

Motorists Injured

Prior: 103↓-14.6%

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 in Mahaska County showed some shifts between 2019 and 2020. While the peak hour for collisions remained 3 p.m. in both years, the peak day changed. In 2019, Monday was the day with the most crashes (54), whereas in 2020, Monday and Saturday were tied for the highest volume, each with 45 crashes. This represents a 50% increase in Saturday crashes year-over-year, from 30 to 45.

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

While total crashes declined, the severity of outcomes worsened in 2020. The number of fatal crashes increased from 1 in 2019 to 5 in 2020, raising the fatal crash rate from 0.3% to 1.9% of all incidents. The proportion of crashes resulting in any injury saw a slight decrease from 27.4% to 26.5%. Notably, crashes classified as involving serious injuries decreased from 7 in 2019 to 4 in 2020.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.9%
↑400.0%prior 1
Serious Injury4serious injury crashes1.5%
↓-42.9%prior 7
Minor Injury35minor injury crashes13.5%
↑9.4%prior 32
Possible Injury30possible injury crashes11.5%
↓-30.2%prior 43
No Injury186no injury crashes71.5%
↓-13.9%prior 216

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, 'Failure to yield from a stop sign' became the top factor with 24 incidents, an increase from 21 the prior year. Collisions involving animals nearly doubled, increasing from 12 to 23 incidents, making it the second-most common factor. Meanwhile, factors that were prominent in 2019, such as 'Driving too fast for conditions' (from 27 to 16 crashes) and 'Lost Control' (from 24 to 13 crashes), saw significant decreases in count.

Officer-Reported Primary Contributing Cause

FTYROW: From stop sign24 (9.2%)↑14.3%prior 21
Animal23 (8.8%)↑91.7%prior 12
Followed too close23 (8.8%)↓-17.9%prior 28
Driving too fast for conditions16 (6.2%)↓-40.7%prior 27
Ran off road - left15 (5.8%)↑50.0%prior 10
FTYROW: Making left turn13 (5%)↓-43.5%prior 23
Lost Control13 (5%)↓-45.8%prior 24
Other (explain in narrative): Other12 (4.6%)↓-14.3%prior 14
Driver Distraction: Other interior distraction12 (4.6%)↓-7.7%prior 13
Ran off road - straight10 (3.8%)↓-16.7%prior 12

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 occurred under significantly better road and weather conditions compared to 2019. The number of crashes on non-dry surfaces (wet, snow, or ice) fell from 101 in 2019 to 52 in 2020. Similarly, collisions during rain or snow decreased from 42 to 22. In contrast, incidents in dark, unlighted conditions increased from 37 to 42, and the share of crashes occurring in daylight decreased from 71.9% in 2019 to 61.9% in 2020.

Weather

Clear168 (68.9%)
→0.0%prior 168
Cloudy51 (20.9%)
↓-12.1%prior 58
Rain11 (4.5%)
↓-50.0%prior 22
Snow11 (4.5%)
↓-45.0%prior 20
Fog, smoke, smog1 (0.4%)
Freezing rain/drizzle1 (0.4%)
↓-87.5%prior 8
Blowing sand, soil, dirt1 (0.4%)

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

Lighting

Daylight161 (66.3%)
↓-25.1%prior 215
Dark - roadway not lighted42 (17.3%)
↑13.5%prior 37
Dark - roadway lighted24 (9.9%)
↓-11.1%prior 27
Dawn7 (2.9%)
Dusk7 (2.9%)
Dark - unknown roadway lighting2 (0.8%)

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

Road Surface

Dry191 (78.3%)
↑4.4%prior 183
Wet23 (9.4%)
↓-37.8%prior 37
Gravel12 (4.9%)
↑71.4%prior 7
Snow8 (3.3%)
↓-72.4%prior 29
Ice/frost8 (3.3%)
↓-69.2%prior 26
Slush1 (0.4%)
Other (explain in narrative)1 (0.4%)

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

Vehicles & Demographics

The composition of vehicles and persons involved in crashes showed general stability year-over-year. The top five vehicle makes involved in collisions—Ford, Chevrolet, Dodge, and Jeep—remained the same in both 2019 and 2020, with counts for each decreasing in line with the overall trend. Among persons involved in crashes, the 16-20 age group's representation increased slightly from making up 14.9% of persons in 2019 to 16.1% in 2020. The gender distribution of involved persons also remained consistent, with males comprising approximately 57-59% in both periods.

Top Vehicle Makes (426 vehicles)

1
FORD87 (20.4%)
↓-13.9%prior 101
2
CHEV66 (15.5%)
↓-20.5%prior 83
3
DODG23 (5.4%)
↓-8.0%prior 25
4
CHEVROLET22 (5.2%)
↓-38.9%prior 36
5
JEEP18 (4.2%)
↓-5.3%prior 19
6
GMC16 (3.8%)
↑60.0%prior 10
7
TOYT16 (3.8%)
↓-5.9%prior 17
8
BUIC14 (3.3%)
↑16.7%prior 12
9
HOND11 (2.6%)
↑22.2%prior 9
10
DODGE9 (2.1%)
↓-47.1%prior 17

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

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

Sex Distribution (394 persons with recorded sex)

Male231 (58.6%)
↓-16.9%prior 278
Female163 (41.4%)
↓-21.3%prior 207

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: 260
  • Total persons involved: 564
  • Total vehicles involved: 426

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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