Yearly Traffic Safety Analysis

182 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Emmet County, total vehicle crashes increased by 7.1%, from 170 incidents in 2016 to 182 in 2017. Despite the rise in collisions, the number of reported injuries fell from 42 to 37, and no fatalities were recorded in either year. The most significant year-over-year change was a 43% increase in crashes attributed to animals, which rose from 44 to 63 incidents.

182

▲ 7.1%was 170

Total Crash Events

0

Persons Killed

37

▼ -11.9%was 42

Persons Injured

0

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend shows a rise in traffic collisions in Emmet County, with total crashes increasing by 7.1% from 170 in 2016 to 182 in 2017. However, this increase in crash volume was accompanied by a 11.9% decrease in the number of people injured, which fell from 42 to 37. Fatalities remained at zero for both years.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

37

Motorists Injured

Prior: 41↓-9.8%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes showed minor shifts between the two periods. The peak day for collisions moved from Thursday in 2016 (33 crashes) to Friday in 2017 (33 crashes). The peak hour also shifted from a tie between 5 PM and 7 PM in 2016 (14 crashes each) to a three-way tie across the 4 PM, 5 PM, and 6 PM hours in 2017, each with 14 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes recorded in either 2016 or 2017. The total number of crashes involving an injury was unchanged at 34 for both years, though their proportion of all crashes fell from 20.0% in 2016 to 18.7% in 2017. The severity distribution shifted slightly, with minor injury crashes decreasing from 19 to 15, while possible injury crashes increased from 13 to 17. The count of serious injury crashes held steady at two.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes1.1%
→0.0%prior 2
Minor Injury15minor injury crashes8.2%
↓-21.1%prior 19
Possible Injury17possible injury crashes9.3%
↑30.8%prior 13
No Injury148no injury crashes81.3%
↑8.8%prior 136

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal were the leading contributing factor in both years, with the count increasing by 43% from 44 incidents in 2016 to 63 in 2017. Consequently, this factor's share of all crashes grew from 25.9% to 34.6%. Another notable change was a more than threefold increase in crashes due to "Followed too close," which rose from 3 in 2016 to 10 in 2017. In contrast, crashes attributed to "Ran off road - left" decreased from 11 to 4 over the same period.

Officer-Reported Primary Contributing Cause

Animal63 (34.6%)↑43.2%prior 44
Other (explain in narrative): Other15 (8.2%)↑114.3%prior 7
Lost Control15 (8.2%)↑7.1%prior 14
Followed too close10 (5.5%)
Driver Distraction: Other interior distraction6 (3.3%)↓-33.3%prior 9
Ran off road - straight6 (3.3%)→0.0%prior 6
Operating vehicle in an reckless, erratic, careless, negligent manner6 (3.3%)↓-14.3%prior 7
FTYROW: From stop sign5 (2.7%)↓-16.7%prior 6
FTYROW: From parked position5 (2.7%)
Driving too fast for conditions4 (2.2%)↓-33.3%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes on dry roads increased from 91 in 2016 to 110 in 2017, while collisions on adverse surfaces like snow, ice, or wet pavement decreased from 42 to 26. The number of crashes occurring in dark conditions remained relatively stable at 50 in 2016 and 52 in 2017. Similarly, incidents during adverse weather conditions were nearly unchanged, with 15 in 2016 and 14 in 2017.

Weather

Clear90 (64.3%)
↑4.7%prior 86
Cloudy34 (24.3%)
↑3.0%prior 33
Rain6 (4.3%)
Blowing Snow4 (2.9%)
Fog, smoke, smog2 (1.4%)
Other (explain in narrative)1 (0.7%)
Severe Winds1 (0.7%)
Snow1 (0.7%)
↓-80.0%prior 5
Freezing rain/drizzle1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight86 (61.0%)
↑2.4%prior 84
Dark - roadway not lighted40 (28.4%)
↑29.0%prior 31
Dark - roadway lighted12 (8.5%)
↓-33.3%prior 18
Dusk3 (2.1%)
↓-40.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry110 (78.0%)
↑20.9%prior 91
Wet13 (9.2%)
↑62.5%prior 8
Snow8 (5.7%)
↓-55.6%prior 18
Ice/frost5 (3.5%)
↓-64.3%prior 14
Gravel5 (3.5%)
↓-16.7%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes were consistent year-over-year, with Chevrolet and Ford vehicles being the most frequent in both periods. The number of Ford vehicles involved decreased from 41 to 33, while Chevrolet vehicles were involved in 64 crashes in 2017, compared to 65 in 2016. A notable demographic shift was observed among persons involved in crashes, as the number of individuals aged 65 and older more than doubled from 20 in 2016 to 45 in 2017. Conversely, involvement for the 16-20 age group decreased from 42 to 29 persons.

Top Vehicle Makes (267 vehicles)

1
CHEV40 (15%)
↑37.9%prior 29
2
FORD33 (12.4%)
↓-19.5%prior 41
3
CHEVROLET24 (9%)
↓-33.3%prior 36
4
GMC17 (6.4%)
↑21.4%prior 14
5
DODGE14 (5.2%)
→0.0%prior 14
6
DODG14 (5.2%)
↑7.7%prior 13
7
TOYT11 (4.1%)
↑22.2%prior 9
8
CHRYSLER9 (3.4%)
9
BUIC9 (3.4%)
↑28.6%prior 7
10
JEEP7 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (187 persons with recorded sex)

Male108 (57.8%)
↓-10.0%prior 120
Female79 (42.2%)
↑46.3%prior 54

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 182
  • Total persons involved: 315
  • Total vehicles involved: 267

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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