Yearly Traffic Safety Analysis

170 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Emmet County recorded 170 total crashes, a 4.3% increase from the 163 crashes in 2015. Despite the rise in total incidents, severe outcomes improved, with total injuries decreasing from 44 to 42 and fatalities dropping from one in the prior year to zero in the current year.

170

▲ 4.3%was 163

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

42

▼ -4.5%was 44

Persons Injured

0

▼ -100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends in Emmet County show a slight increase in volume but a decrease in severity. Total crashes rose by 4.3% from 163 in 2015 to 170 in 2016. Concurrently, the number of people injured fell by 4.5% from 44 to 42, and the single fatality recorded in 2015 was not repeated in 2016.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 1↓-100.0%

1

Pedestrians Injured

Prior: 1→0.0%

41

Motorists Injured

Prior: 43↓-4.7%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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 2016, the peak day for crashes was Thursday with 33 incidents, a change from 2015 when Monday and Friday tied for the most crashes with 28 each. The peak hour also moved later in the day, from 3 PM (15 crashes) in 2015 to the 5 PM and 7 PM hours in 2016, which both recorded 14 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity saw a notable improvement year-over-year. The number of fatal crashes dropped from one in 2015 to zero in 2016. The count of serious injury crashes also decreased from three to two. While minor injury crashes saw a slight increase from 17 to 19, the overall number of crashes involving any level of injury fell from 38 in 2015 to 34 in 2016.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes1.2%
↓-33.3%prior 3
Minor Injury19minor injury crashes11.2%
↑11.8%prior 17
Possible Injury13possible injury crashes7.6%
↓-27.8%prior 18
No Injury136no injury crashes80%
↑9.7%prior 124

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with an animal remained the top contributing factor in both years, with the count of such crashes increasing from 37 in 2015 to 44 in 2016. Other notable shifts include a significant decrease in crashes where "Driving too fast for conditions" was a factor, falling from 15 incidents in 2015 to 6 in 2016. Crashes attributed to "Followed too close" also dropped substantially, from 11 in the prior year to 3 in the current year.

Officer-Reported Primary Contributing Cause

Animal44 (25.9%)↑18.9%prior 37
Lost Control14 (8.2%)↑16.7%prior 12
Ran off road - left11 (6.5%)
Driver Distraction: Other interior distraction9 (5.3%)↑28.6%prior 7
Operating vehicle in an reckless, erratic, careless, negligent manner7 (4.1%)↓-36.4%prior 11
Improper Backing7 (4.1%)
Other (explain in narrative): Other7 (4.1%)↓-41.7%prior 12
FTYROW: From stop sign6 (3.5%)↓-25.0%prior 8
Ran off road - straight6 (3.5%)↑20.0%prior 5
Driving too fast for conditions6 (3.5%)↓-60.0%prior 15

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes across different environmental conditions remained largely consistent year-over-year. Crashes on dry road surfaces were most common in both periods, with 91 incidents in 2016 compared to 97 in 2015. Daylight conditions accounted for the majority of crashes in both years (84 in 2016 vs. 89 in 2015). A notable change was a decrease in crashes occurring in snowy weather, which fell from 11 in 2015 to 5 in 2016.

Weather

Clear86 (62.3%)
↑4.9%prior 82
Cloudy33 (23.9%)
↑13.8%prior 29
Snow5 (3.6%)
↓-54.5%prior 11
Blowing Snow4 (2.9%)
Freezing rain/drizzle3 (2.2%)
Rain3 (2.2%)
↓-50.0%prior 6
Fog, smoke, smog3 (2.2%)
Other (explain in narrative)1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight84 (59.6%)
↓-5.6%prior 89
Dark - roadway not lighted31 (22.0%)
↓-3.1%prior 32
Dark - roadway lighted18 (12.8%)
↑5.9%prior 17
Dusk5 (3.5%)
Dawn2 (1.4%)
Dark - unknown roadway lighting1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry91 (65.5%)
↓-6.2%prior 97
Snow18 (12.9%)
↑12.5%prior 16
Ice/frost14 (10.1%)
↑27.3%prior 11
Wet8 (5.8%)
↓-11.1%prior 9
Gravel6 (4.3%)
↑20.0%prior 5
Slush2 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

Vehicle and person demographics showed some shifts between periods. Ford and Chevrolet models were the most frequently involved makes in both years. Analysis of person data shows a significant decrease in the number of individuals aged 16-20 involved in crashes, dropping from 57 in 2015 to 42 in 2016. A similar drop was seen for the 65+ age group, which went from 39 persons involved in 2015 to 20 in 2016.

Top Vehicle Makes (256 vehicles)

1
FORD41 (16%)
↑20.6%prior 34
2
CHEVROLET36 (14.1%)
↑33.3%prior 27
3
CHEV29 (11.3%)
↓-6.5%prior 31
4
DODGE14 (5.5%)
↑16.7%prior 12
5
GMC14 (5.5%)
↓-22.2%prior 18
6
DODG13 (5.1%)
↑44.4%prior 9
7
NR11 (4.3%)
↑83.3%prior 6
8
PONTIAC11 (4.3%)
→0.0%prior 11
9
TOYT9 (3.5%)
↑12.5%prior 8
10
BUIC7 (2.7%)
↓-30.0%prior 10

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (174 persons with recorded sex)

Male120 (69.0%)
↑11.1%prior 108
Female54 (31.0%)
↓-47.1%prior 102

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 170
  • Total persons involved: 283
  • Total vehicles involved: 256

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: 2016." Published September 9, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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