Yearly Traffic Safety Analysis

168 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Kossuth County, traffic crashes decreased by 6.7% from 180 in 2016 to 168 in 2017. During this period, total fatalities were halved from two to one, and total injuries saw a minor reduction. The most notable year-over-year shift was a 40.9% decrease in crashes attributed to 'Driving too fast for conditions,' which fell from the top contributing factor in 2016 to the third-ranked factor in 2017.

168

▼ -6.7%was 180

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

79

▼ -2.5%was 81

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

Trend Summary

The overall trend in Kossuth County shows a modest decline in traffic incidents year-over-year. Total crashes fell from 180 to 168, a 6.7% decrease. This downward trend was also reflected in crash outcomes, with fatalities decreasing from two to one and total injuries declining slightly from 81 to 79.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 2↓-50.0%

1

Pedestrians Injured

Prior: 1→0.0%

78

Motorists Injured

Prior: 80↓-2.5%

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 shifted between the two periods. The peak day for crashes moved from Friday (31 crashes) in 2016 to Wednesday (30 crashes) in 2017. Similarly, the peak hour for incidents shifted from 12 p.m. in 2016 to 3 p.m. in 2017, with both hours recording 18 crashes in their respective years.

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

While the number of fatal crashes remained unchanged at one, the number of fatalities decreased from two in 2016 to one in 2017. The proportion of crashes resulting in 'Possible Injury' increased from 11.7% of all crashes in 2016 to 17.3% in 2017. Conversely, the share of 'No Injury' crashes decreased from 67.8% in 2016 to 63.1% in 2017.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.6%
→0.0%prior 1
Serious Injury10serious injury crashes6%
↓-9.1%prior 11
Minor Injury22minor injury crashes13.1%
↓-12.0%prior 25
Possible Injury29possible injury crashes17.3%
↑38.1%prior 21
No Injury106no injury crashes63.1%
↓-13.1%prior 122

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

The leading contributing factors for crashes changed year-over-year. In 2016, 'Driving too fast for conditions' was the top factor with 22 incidents, but this number fell by 40.9% to 13 incidents in 2017, making it the third most common factor. 'Lost Control' became the top-ranked cause in 2017 with 19 crashes, a slight increase from 18 crashes in the prior year. Incidents involving 'FTYROW: From stop sign' also saw a notable decrease from 17 to 10.

Officer-Reported Primary Contributing Cause

Lost Control19 (11.3%)↑5.6%prior 18
Other (explain in narrative): Other15 (8.9%)↑200.0%prior 5
Driving too fast for conditions13 (7.7%)↓-40.9%prior 22
Followed too close11 (6.5%)↑22.2%prior 9
Improper Backing10 (6%)
Ran off road - straight10 (6%)↓-16.7%prior 12
FTYROW: At uncontrolled intersection10 (6%)→0.0%prior 10
FTYROW: From stop sign10 (6%)↓-41.2%prior 17
Driver Distraction: Other interior distraction9 (5.4%)
Animal7 (4.2%)↑16.7%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 occurring in adverse road conditions saw a decrease. The number of incidents on roads with snow or ice dropped from a combined 42 in 2016 to 32 in 2017. Despite this, crashes on dry roads and in clear weather remained the most frequent scenarios in both years, with dry road crashes increasing slightly from 107 to 109.

Weather

Clear105 (64.0%)
↓-9.5%prior 116
Cloudy38 (23.2%)
↑11.8%prior 34
Snow8 (4.9%)
↓-42.9%prior 14
Freezing rain/drizzle7 (4.3%)
Rain3 (1.8%)
↓-57.1%prior 7
Blowing Snow2 (1.2%)
Fog, smoke, smog1 (0.6%)

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

Lighting

Daylight123 (74.5%)
↑0.8%prior 122
Dark - roadway not lighted21 (12.7%)
↓-25.0%prior 28
Dark - roadway lighted15 (9.1%)
↓-6.3%prior 16
Dawn4 (2.4%)
↓-33.3%prior 6
Dusk2 (1.2%)
↓-66.7%prior 6

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

Road Surface

Dry109 (66.5%)
↑1.9%prior 107
Snow16 (9.8%)
↓-44.8%prior 29
Ice/frost16 (9.8%)
↑23.1%prior 13
Gravel10 (6.1%)
↑25.0%prior 8
Wet9 (5.5%)
↓-47.1%prior 17
Slush2 (1.2%)
Sand2 (1.2%)

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

Vehicles & Demographics

The primary vehicle makes involved in crashes remained consistent, with Chevrolet and Ford being the top two in both years. The number of Chevrolets involved increased slightly from 75 to 77, while Fords decreased from 52 to 51. Analysis of persons involved shows an increase in the 16-20 age group, which grew from 44 individuals in 2016 to 48 in 2017.

Top Vehicle Makes (276 vehicles)

1
FORD51 (18.5%)
↓-1.9%prior 52
2
CHEV40 (14.5%)
↑29.0%prior 31
3
CHEVROLET37 (13.4%)
↓-15.9%prior 44
4
GMC20 (7.2%)
↑25.0%prior 16
5
DODG15 (5.4%)
↑36.4%prior 11
6
DODGE11 (4%)
↑57.1%prior 7
7
BUIC9 (3.3%)
↓-25.0%prior 12
8
HOND8 (2.9%)
9
PONT7 (2.5%)
→0.0%prior 7
10
CHRY5 (1.8%)
→0.0%prior 5

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

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

Sex Distribution (208 persons with recorded sex)

Male116 (55.8%)
↓-12.1%prior 132
Female92 (44.2%)
↑7.0%prior 86

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: 168
  • Total persons involved: 326
  • Total vehicles involved: 276

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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