Yearly Traffic Safety Analysis

180 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Kossuth County, total crashes increased by 12.5%, rising from 160 in 2015 to 180 in 2016. During this period, the number of fatalities remained unchanged at 2, while total injuries saw a slight decrease from 89 to 81. One of the most notable shifts was in contributing factors, where crashes attributed to 'Driving too fast for conditions' increased by 83% from 12 to 22 incidents.

180

▲ 12.5%was 160

Total Crash Events

2

Persons Killed

81

▼ -9.0%was 89

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (2) 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 · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash totals in Kossuth County trended upward in 2016 compared to the prior year, with a 12.5% increase from 160 to 180 incidents. Despite the rise in total crashes, key outcomes were mixed; total injuries decreased by 9% from 89 to 81, and fatalities held steady at 2 for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

80

Motorists Injured

Prior: 84↓-4.8%

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 temporal patterns of crashes shifted between the two periods. In 2016, the peak day for crashes was Friday with 31 incidents, a change from Thursday (37 incidents) in 2015. Similarly, the peak hour for collisions moved from the 5 PM hour in 2015, which saw 15 crashes, to the 12 PM hour in 2016, which recorded 18 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

The number of fatal crashes decreased from 2 in 2015 to 1 in 2016, lowering the fatal crash rate from 1.25% to 0.56%. The count of serious injury crashes increased from 8 to 11, while minor injury crashes fell from 38 to 25. Crashes resulting in no injuries increased from 87 to 122, making up 67.8% of all incidents in 2016 compared to 54.4% in the prior year.

Severity is per crash event (most severe injury). 1 fatal crash events resulted in 2 persons killed.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.6%
↓-50.0%prior 2
Serious Injury11serious injury crashes6.1%
↑37.5%prior 8
Minor Injury25minor injury crashes13.9%
↓-34.2%prior 38
Possible Injury21possible injury crashes11.7%
↓-16.0%prior 25
No Injury122no injury crashes67.8%
↑40.2%prior 87

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

The leading contributing factors for crashes shifted between 2015 and 2016. In 2016, 'Driving too fast for conditions' became the primary factor, with the count of related crashes increasing by 83% from 12 to 22. 'Lost Control,' the top factor in 2015 with 22 crashes, saw its count decrease to 18 in 2016. Incidents involving 'Failure to yield from a stop sign' also rose, with the count increasing from 10 to 17.

Officer-Reported Primary Contributing Cause

Driving too fast for conditions22 (12.2%)↑83.3%prior 12
Lost Control18 (10%)↓-18.2%prior 22
FTYROW: From stop sign17 (9.4%)↑70.0%prior 10
Ran off road - left13 (7.2%)↑85.7%prior 7
Ran off road - straight12 (6.7%)↑71.4%prior 7
FTYROW: At uncontrolled intersection10 (5.6%)↑100.0%prior 5
Followed too close9 (5%)↓-35.7%prior 14
Made improper turn7 (3.9%)↓-12.5%prior 8
Animal6 (3.3%)↑20.0%prior 5
Other (explain in narrative): Other5 (2.8%)↓-28.6%prior 7

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

Road & Environmental Conditions

While the majority of crashes in both periods occurred in clear weather and daylight, there were notable shifts in adverse conditions. The number of crashes on roads with snow increased significantly, rising from 7 incidents in 2015 to 29 in 2016. Additionally, crashes occurring in darkness on unlighted roadways more than doubled, increasing from 13 in 2015 to 28 in 2016.

Weather

Clear116 (65.2%)
↑18.4%prior 98
Cloudy34 (19.1%)
↑17.2%prior 29
Snow14 (7.9%)
↑27.3%prior 11
Rain7 (3.9%)
↓-41.7%prior 12
Blowing Snow3 (1.7%)
Severe Winds2 (1.1%)
Freezing rain/drizzle2 (1.1%)

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

Lighting

Daylight122 (68.2%)
↑1.7%prior 120
Dark - roadway not lighted28 (15.6%)
↑115.4%prior 13
Dark - roadway lighted16 (8.9%)
↑23.1%prior 13
Dawn6 (3.4%)
Dusk6 (3.4%)
Dark - unknown roadway lighting1 (0.6%)

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

Road Surface

Dry107 (59.4%)
↑0.9%prior 106
Snow29 (16.1%)
↑314.3%prior 7
Wet17 (9.4%)
↓-10.5%prior 19
Ice/frost13 (7.2%)
→0.0%prior 13
Gravel8 (4.4%)
→0.0%prior 8
Slush4 (2.2%)
Sand1 (0.6%)
Water (standing or moving)1 (0.6%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained broadly consistent, with Chevrolet and Ford being the most common in both years. The number of persons involved in crashes decreased from 360 to 330. This was reflected in most age groups, with notable drops in the 26-34 age bracket (from 56 to 44 persons) and the 65+ age bracket (from 56 to 46 persons).

Top Vehicle Makes (290 vehicles)

1
FORD52 (17.9%)
↑10.6%prior 47
2
CHEVROLET44 (15.2%)
↑91.3%prior 23
3
CHEV31 (10.7%)
↓-6.1%prior 33
4
GMC16 (5.5%)
↑45.5%prior 11
5
BUIC12 (4.1%)
↑33.3%prior 9
6
DODG11 (3.8%)
↓-8.3%prior 12
7
BUICK11 (3.8%)
↑83.3%prior 6
8
JEEP9 (3.1%)
→0.0%prior 9
9
PONTIAC8 (2.8%)
↑14.3%prior 7
10
PONT7 (2.4%)
↓-12.5%prior 8

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

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

Sex Distribution (218 persons with recorded sex)

Male132 (60.6%)
↓-12.0%prior 150
Female86 (39.4%)
↓-6.5%prior 92

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: 180
  • Total persons involved: 330
  • Total vehicles involved: 290

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

ThatCarHitMe.com · An Injuria.ai Company

Kossuth County, IA Crash Report — 2016 | ThatCarHitMe.com