Yearly Traffic Safety Analysis

227 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Emmet County recorded 227 total crashes, a 19.5% increase from the 190 crashes documented in 2018. This period saw a rise in total injuries from 43 to 70. Notably, there was one fatal crash resulting in one fatality in 2019, whereas there were no fatal crashes in the prior year.

227

▲ 19.5%was 190

Total Crash Events

1

Persons Killed

70

▲ 62.8%was 43

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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash volume in Emmet County increased from 2018 to 2019. Total crashes rose by 19.5%, from 190 to 227. This upward trend was also reflected in the number of persons injured, which grew by 62.8% from 43 to 70, and the number of fatalities, which increased from zero to one.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 0→%

1

Pedestrians Injured

Prior: 0→%

69

Motorists Injured

Prior: 42↑64.3%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted between 2018 and 2019. The peak day for crashes moved from Tuesday (38 crashes) in 2018 to Sunday (38 crashes) in 2019. The peak hour for collisions also changed, shifting from 6 p.m. in the prior year (15 crashes) to 3 p.m. in the current year (21 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased in 2019 compared to 2018, with one fatal crash recorded, up from zero in the previous year. The proportion of crashes resulting in any type of injury rose from 18.9% in 2018 to 22.5% in 2019. Correspondingly, the share of crashes with no reported injuries decreased from 81.1% to 77.1% year-over-year.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
Serious Injury3serious injury crashes1.3%
→0.0%prior 3
Minor Injury19minor injury crashes8.4%
↑11.8%prior 17
Possible Injury29possible injury crashes12.8%
↑81.3%prior 16
No Injury175no injury crashes77.1%
↑13.6%prior 154

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor, with an identical count of 53 crashes in both 2019 and 2018. The number of crashes attributed to 'Lost Control' increased by 38.5% in count, from 13 to 18 incidents, making it the third most common factor in 2019. Conversely, crashes due to 'Driving too fast for conditions' saw a decrease in count from 18 to 16.

Officer-Reported Primary Contributing Cause

Animal53 (23.3%)→0.0%prior 53
Other (explain in narrative): Other27 (11.9%)↑125.0%prior 12
Lost Control18 (7.9%)↑38.5%prior 13
Driving too fast for conditions16 (7%)↓-11.1%prior 18
FTYROW: From stop sign9 (4%)↑50.0%prior 6
Operating vehicle in an reckless, erratic, careless, negligent manner9 (4%)↑28.6%prior 7
Ran off road - left9 (4%)↑50.0%prior 6
Other (explain in narrative): No improper action8 (3.5%)
Followed too close7 (3.1%)↓-36.4%prior 11
FTYROW: From driveway6 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

In 2019, a larger proportion of crashes occurred during daylight hours (56.8%) compared to 2018 (50.5%). Crashes on dry road surfaces also saw a proportional increase, accounting for 47.6% of incidents in 2019 versus 40.0% in the prior year. This corresponds with a rise in the share of crashes happening in clear weather, which grew from 42.1% in 2018 to 52.9% in 2019.

Weather

Clear120 (65.9%)
↑50.0%prior 80
Cloudy32 (17.6%)
↓-5.9%prior 34
Snow10 (5.5%)
↓-23.1%prior 13
Rain7 (3.8%)
→0.0%prior 7
Blowing Snow7 (3.8%)
↑16.7%prior 6
Freezing rain/drizzle4 (2.2%)
Fog, smoke, smog2 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight129 (69.7%)
↑34.4%prior 96
Dark - roadway not lighted27 (14.6%)
↓-6.9%prior 29
Dark - roadway lighted20 (10.8%)
↑5.3%prior 19
Dawn5 (2.7%)
Dusk3 (1.6%)
Dark - unknown roadway lighting1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry108 (59.0%)
↑42.1%prior 76
Snow32 (17.5%)
↑33.3%prior 24
Ice/frost23 (12.6%)
↓-8.0%prior 25
Wet13 (7.1%)
↓-18.8%prior 16
Gravel4 (2.2%)
Slush2 (1.1%)
Other (explain in narrative)1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

The number of Chevrolet vehicles (combining 'CHEV' and 'CHEVROLET' entries) involved in crashes increased from 68 in 2018 to 92 in 2019, making it the most frequent vehicle make. Analysis of persons involved shows a significant increase in the 26-34 age group, which grew from 38 individuals in 2018 to 78 in 2019. The 16-20 age group also saw a notable increase in involvement, rising from 48 to 67 persons.

Top Vehicle Makes (348 vehicles)

1
CHEV61 (17.5%)
↑45.2%prior 42
2
FORD51 (14.7%)
↓-1.9%prior 52
3
CHEVROLET31 (8.9%)
↑19.2%prior 26
4
GMC26 (7.5%)
↑30.0%prior 20
5
PONT12 (3.4%)
↑33.3%prior 9
6
DODG11 (3.2%)
↓-38.9%prior 18
7
BUICK11 (3.2%)
8
DODGE11 (3.2%)
↑83.3%prior 6
9
PONTIAC10 (2.9%)
10
BUIC9 (2.6%)
↓-35.7%prior 14

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (295 persons with recorded sex)

Male173 (58.6%)
↑58.7%prior 109
Female122 (41.4%)
↑37.1%prior 89

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 227
  • Total persons involved: 508
  • Total vehicles involved: 348

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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