Yearly Traffic Safety Analysis

153 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Emmet County recorded 153 crashes, a 32.6% decrease from the 227 crashes documented in 2019. While the number of fatalities remained constant at one, the total number of injuries was halved, falling from 70 to 35. The most notable year-over-year change was the significant overall reduction in both crash volume and the number of people injured.

153

▼ -32.6%was 227

Total Crash Events

1

Persons Killed

35

▼ -50.0%was 70

Persons Injured

1

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 Emmet County showed a significant downward trend year-over-year. The total number of crashes fell by 32.6%, from 227 in 2019 to 153 in 2020. This decline was mirrored in non-fatal outcomes, as total injuries dropped by 50% from 70 to 35, while fatalities held steady at one for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Cyclists Killed

Prior: 0→%

0

Motorists Killed

Prior: 1↓-100.0%

1

Pedestrians Injured

Prior: 1→0.0%

1

Cyclists Injured

Prior: 0→%

33

Motorists Injured

Prior: 69↓-52.2%

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 Thursday with 31 incidents, a change from Sunday in 2019 which saw 38 crashes. The busiest time of day also moved later, with the peak hour shifting from 3 p.m. in 2019 (21 crashes) to 5 p.m. in 2020 (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

Although the number of fatal crashes was unchanged at one, the fatal crash rate per 100 crashes increased from 0.44 in 2019 to 0.65 in 2020 due to the lower total number of incidents. The proportion of crashes resulting in no injury was identical at 77.1% for both periods. The total count of crashes involving any type of injury decreased from 51 in 2019 to 34 in 2020.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
→0.0%prior 1
Minor Injury17minor injury crashes11.1%
↓-10.5%prior 19
Possible Injury17possible injury crashes11.1%
↓-41.4%prior 29
No Injury118no injury crashes77.1%
↓-32.6%prior 175

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 top contributing factor in both periods, with the count decreasing from 53 crashes in 2019 to 48 in 2020. The number of crashes attributed to 'Lost Control' fell from 18 to 13, and 'Driving too fast for conditions' incidents dropped from 16 to 9. Conversely, crashes where a driver 'Followed too close' increased from 7 in 2019 to 10 in 2020.

Officer-Reported Primary Contributing Cause

Animal48 (31.4%)↓-9.4%prior 53
Other (explain in narrative): Other14 (9.2%)↓-48.1%prior 27
Lost Control13 (8.5%)↓-27.8%prior 18
Followed too close10 (6.5%)↑42.9%prior 7
Driving too fast for conditions9 (5.9%)↓-43.8%prior 16
Other (explain in narrative): No improper action7 (4.6%)↓-12.5%prior 8
FTYROW: At uncontrolled intersection6 (3.9%)
Driver Distraction: Other interior distraction5 (3.3%)↓-16.7%prior 6
FTYROW: From stop sign5 (3.3%)↓-44.4%prior 9
Ran off road - left4 (2.6%)↓-55.6%prior 9

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 proportion of crashes occurring in clear weather was stable at 52.9% in both 2019 and 2020, while daylight crashes accounted for 51.6% of incidents in 2020, down from 56.8% the prior year. There was a notable shift in road conditions, as the share of crashes on adverse surfaces like snow, ice, or wet roads decreased from 30.0% of all crashes in 2019 to 21.6% in 2020.

Weather

Clear81 (70.4%)
↓-32.5%prior 120
Cloudy16 (13.9%)
↓-50.0%prior 32
Rain6 (5.2%)
↓-14.3%prior 7
Blowing Snow5 (4.3%)
↓-28.6%prior 7
Other (explain in narrative)3 (2.6%)
Freezing rain/drizzle2 (1.7%)
Fog, smoke, smog1 (0.9%)
Snow1 (0.9%)
↓-90.0%prior 10

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

Lighting

Daylight79 (68.1%)
↓-38.8%prior 129
Dark - roadway not lighted19 (16.4%)
↓-29.6%prior 27
Dark - roadway lighted12 (10.3%)
↓-40.0%prior 20
Dark - unknown roadway lighting3 (2.6%)
Dawn2 (1.7%)
↓-60.0%prior 5
Dusk1 (0.9%)

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

Road Surface

Dry78 (68.4%)
↓-27.8%prior 108
Ice/frost15 (13.2%)
↓-34.8%prior 23
Wet11 (9.6%)
↓-15.4%prior 13
Snow7 (6.1%)
↓-78.1%prior 32
Gravel2 (1.8%)
Slush1 (0.9%)

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

Vehicles & Demographics

The ranking of the top three vehicle makes involved in crashes—Chevrolet, Ford, and GMC—was consistent across both years, with counts for each make decreasing in 2020. The age distribution of persons involved in crashes remained broadly similar. The 26-34 age group was the most represented cohort in both years, accounting for 15.4% of persons in 2019 and 14.7% in 2020.

Top Vehicle Makes (223 vehicles)

1
CHEV42 (18.8%)
↓-31.1%prior 61
2
FORD40 (17.9%)
↓-21.6%prior 51
3
CHEVROLET27 (12.1%)
↓-12.9%prior 31
4
GMC14 (6.3%)
↓-46.2%prior 26
5
DODG9 (4%)
↓-18.2%prior 11
6
CHRYSLER6 (2.7%)
7
PONT6 (2.7%)
↓-50.0%prior 12
8
BUIC6 (2.7%)
↓-33.3%prior 9
9
DODGE6 (2.7%)
↓-45.5%prior 11
10
BUICK5 (2.2%)
↓-54.5%prior 11

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

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

Sex Distribution (196 persons with recorded sex)

Male111 (56.6%)
↓-35.8%prior 173
Female85 (43.4%)
↓-30.3%prior 122

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: 153
  • Total persons involved: 319
  • Total vehicles involved: 223

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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