Yearly Traffic Safety Analysis

121 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Monroe County recorded 121 total crashes, a 27.1% decrease from the 166 crashes reported in 2019. This downturn was accompanied by a reduction in both fatalities, which fell from 2 to 1, and total injuries, which dropped from 37 to 23. The most significant year-over-year change was the overall reduction in crash volume.

121

▼ -27.1%was 166

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

23

▼ -37.8%was 37

Persons Injured

1

▼ -50.0%was 2

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 Monroe County showed a downward trend from 2019 to 2020. The total number of crashes decreased by 27.1%, falling from 166 to 121. This decline was also reflected in crash outcomes, with total injuries decreasing by 37.8% and fatalities halving from 2 to 1.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 1→0.0%

23

Motorists Injured

Prior: 36↓-36.1%

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 2019 and 2020. The peak day for crashes moved from Friday (35 incidents) in 2019 to Thursday (29 incidents) in 2020. A more pronounced change occurred in the peak hour, which shifted from the 7 a.m. morning commute hour in 2019 (14 crashes) to the 5 p.m. evening commute hour in 2020 (13 crashes). October was the month with the most crashes in both years.

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

Crash severity generally decreased from 2019 to 2020. The number of fatal crashes fell from 2 to 1, with the fatal crash rate dropping from 1.2% to 0.8% of all incidents. The total number of crashes involving any level of injury decreased from 35 in 2019 to 22 in 2020. Consequently, the share of crashes resulting in no injuries rose from 78.9% in 2019 to 81.8% in 2020.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.8%
↓-50.0%prior 2
Serious Injury3serious injury crashes2.5%
→0.0%prior 3
Minor Injury6minor injury crashes5%
↓-45.5%prior 11
Possible Injury12possible injury crashes9.9%
↓-36.8%prior 19
No Injury99no injury crashes81.8%
↓-24.4%prior 131

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 of such incidents decreased by 33.8% from 77 in 2019 to 51 in 2020. The share of total crashes attributed to animals also fell from 46.4% to 42.1%. In contrast, crashes where failure to yield from a stop sign was a factor increased in count from 4 to 7, and incidents of running a stop sign rose from 5 to 6.

Officer-Reported Primary Contributing Cause

Animal51 (42.1%)↓-33.8%prior 77
Lost Control8 (6.6%)↓-11.1%prior 9
Other (explain in narrative): Other7 (5.8%)↓-36.4%prior 11
FTYROW: From stop sign7 (5.8%)
Ran Stop Sign6 (5%)↑20.0%prior 5
Followed too close6 (5%)↑20.0%prior 5
Ran off road - left5 (4.1%)
Driver Distraction: Other interior distraction4 (3.3%)
Other (explain in narrative): No improper action4 (3.3%)
Driving too fast for conditions3 (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

The distribution of crash conditions saw minor changes between periods. The proportion of crashes occurring in daylight increased from 39.2% in 2019 to 41.3% in 2020, while the count of crashes in dark, unlighted conditions increased slightly from 23 to 26. Crashes on adverse road surfaces like wet, snow, or ice saw a notable drop, falling from 28 incidents in 2019 to 18 in 2020.

Weather

Clear61 (67.8%)
↓-22.8%prior 79
Cloudy14 (15.6%)
→0.0%prior 14
Rain7 (7.8%)
→0.0%prior 7
Freezing rain/drizzle3 (3.3%)
Fog, smoke, smog2 (2.2%)
Snow2 (2.2%)
↓-60.0%prior 5
Sleet, hail1 (1.1%)

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

Lighting

Daylight50 (55.6%)
↓-23.1%prior 65
Dark - roadway not lighted26 (28.9%)
↑13.0%prior 23
Dawn6 (6.7%)
→0.0%prior 6
Dark - roadway lighted4 (4.4%)
↓-55.6%prior 9
Dusk4 (4.4%)

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

Road Surface

Dry66 (73.3%)
↓-8.3%prior 72
Wet12 (13.3%)
↓-25.0%prior 16
Gravel6 (6.7%)
→0.0%prior 6
Ice/frost3 (3.3%)
Snow2 (2.2%)
↓-77.8%prior 9
Slush1 (1.1%)

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 shifted, with Chevrolet (46 total crashes, combining 'CHEV' and 'CHEVROLET') involved in more incidents than Ford (30) in 2020; this is a reversal from 2019 when Ford led with 50 crashes to Chevrolet's 50. The number of persons involved in crashes decreased across most age groups, consistent with the overall drop in incidents. The 26-34 age group was the largest cohort involved in crashes in 2020 with 43 individuals, whereas the 35-44 age group was largest in 2019 with 60 individuals.

Top Vehicle Makes (171 vehicles)

1
CHEV35 (20.5%)
→0.0%prior 35
2
FORD30 (17.5%)
↓-40.0%prior 50
3
DODG14 (8.2%)
↓-6.7%prior 15
4
CHEVROLET11 (6.4%)
↓-26.7%prior 15
5
JEEP10 (5.8%)
↑11.1%prior 9
6
GMC7 (4.1%)
→0.0%prior 7
7
TOYOTA5 (2.9%)
8
NISSAN4 (2.3%)
9
CHRY4 (2.3%)
10
KIA4 (2.3%)

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

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

Sex Distribution (166 persons with recorded sex)

Male91 (54.8%)
↓-24.8%prior 121
Female75 (45.2%)
↓-21.1%prior 95

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: 121
  • Total persons involved: 234
  • Total vehicles involved: 171

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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