Yearly Traffic Safety Analysis

312 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Clayton County recorded 312 total crashes, a 1.6% decrease from the 317 crashes reported in 2015. Despite the slight drop in overall collisions, the number of crashes resulting in serious injuries doubled from 6 in the prior year to 12 in the current year. Total fatalities also increased from 3 to 4 year-over-year.

312

▼ -1.6%was 317

Total Crash Events

4

▲ 33.3%was 3

Persons Killed

92

▼ -5.2%was 97

Persons Injured

4

▲ 33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 Clayton County remained relatively stable, showing a slight decrease from 317 incidents in 2015 to 312 in 2016, a change of -1.6%. While total injuries also saw a minor reduction of 5.2% from 97 to 92, the number of people killed in crashes rose from 3 to 4 during the same period.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

3

Motorists Killed

Prior: 3→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

91

Motorists Injured

Prior: 95↓-4.2%

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 53 incidents, whereas in 2015 it was Saturday with 58 incidents. Similarly, the peak hour for collisions moved an hour earlier, from 6 p.m. (31 crashes) in the prior year to 5 p.m. (33 crashes) in the current year.

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 worsened in 2016 compared to the previous year. The fatal crash rate increased from 0.95% to 1.28%, with fatal incidents rising from 3 to 4. Most notably, the count of serious injury crashes doubled from 6 to 12, increasing their share of all crashes from 1.9% to 3.8%. Consequently, the proportion of no-injury crashes decreased from 77.9% in 2015 to 75.0% in 2016.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.3%
↑33.3%prior 3
Serious Injury12serious injury crashes3.8%
↑100.0%prior 6
Minor Injury30minor injury crashes9.6%
↑3.4%prior 29
Possible Injury32possible injury crashes10.3%
→0.0%prior 32
No Injury234no injury crashes75%
↓-5.3%prior 247

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 animals remained the leading contributing factor in both periods, though the count of such incidents decreased by 8.3% from 144 in 2015 to 132 in 2016. The second most common factor, 'Lost Control', also saw a reduction in count, falling by 21.2% from 33 to 26 crashes. In contrast, crashes attributed to 'Driving too fast for conditions' increased by 29.4%, rising from 17 incidents in the prior year to 22 in the current year.

Officer-Reported Primary Contributing Cause

Animal132 (42.3%)↓-8.3%prior 144
Lost Control26 (8.3%)↓-21.2%prior 33
Driving too fast for conditions22 (7.1%)↑29.4%prior 17
Ran off road - straight20 (6.4%)↑11.1%prior 18
Other (explain in narrative): Other15 (4.8%)↑50.0%prior 10
Ran off road - left13 (4.2%)↑44.4%prior 9
FTYROW: From stop sign7 (2.2%)→0.0%prior 7
Other (explain in narrative): No improper action6 (1.9%)↑20.0%prior 5
Followed too close6 (1.9%)
Driver Distraction: Other interior distraction6 (1.9%)↓-25.0%prior 8

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 majority of crashes in both years occurred in clear weather and on dry roads. In 2016, the proportion of crashes in darkness (including not lighted, lighted, and dusk) saw a slight increase, accounting for 41.5% of incidents with known lighting conditions compared to 38.2% in the previous year. Crashes on adverse road surfaces like snow, ice, or wet pavement decreased slightly as a share of the total, from 37.2% in 2015 to 33.2% in 2016.

Weather

Clear131 (64.2%)
↑12.9%prior 116
Cloudy31 (15.2%)
↓-11.4%prior 35
Snow19 (9.3%)
↓-17.4%prior 23
Blowing Snow9 (4.4%)
↑80.0%prior 5
Freezing rain/drizzle6 (2.9%)
Rain4 (2.0%)
Fog, smoke, smog2 (1.0%)
↓-60.0%prior 5
Severe Winds1 (0.5%)
Sleet, hail1 (0.5%)

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

Lighting

Daylight114 (55.6%)
↑5.6%prior 108
Dark - roadway not lighted61 (29.8%)
↓-1.6%prior 62
Dark - roadway lighted13 (6.3%)
↑18.2%prior 11
Dawn9 (4.4%)
Dusk6 (2.9%)
→0.0%prior 6
Dark - unknown roadway lighting2 (1.0%)

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

Road Surface

Dry127 (62.0%)
↑7.6%prior 118
Snow33 (16.1%)
↑32.0%prior 25
Gravel16 (7.8%)
↓-33.3%prior 24
Ice/frost13 (6.3%)
↑8.3%prior 12
Wet11 (5.4%)
↑37.5%prior 8
Slush5 (2.4%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Chevrolet and Ford leading in both years; combining abbreviated names, Chevrolet-branded vehicles were involved in 108 crashes, while Ford vehicles were involved in 61. This represents a shift from the prior year, where Ford (72) and Chevrolet (106) were more closely matched. An analysis of persons involved shows a decrease in the representation of older individuals; the 65+ age group accounted for 12.4% of persons in 2016, down from 14.7% in the prior year.

Top Vehicle Makes (393 vehicles)

1
CHEVROLET72 (18.3%)
↑4.3%prior 69
2
FORD61 (15.5%)
↓-15.3%prior 72
3
CHEV36 (9.2%)
↓-2.7%prior 37
4
DODGE31 (7.9%)
→0.0%prior 31
5
TOYOTA14 (3.6%)
↓-6.7%prior 15
6
GMC13 (3.3%)
↑44.4%prior 9
7
PONTIAC12 (3.1%)
↓-25.0%prior 16
8
BUICK11 (2.8%)
↑37.5%prior 8
9
DODG11 (2.8%)
↓-21.4%prior 14
10
JEEP9 (2.3%)
→0.0%prior 9

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

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

Sex Distribution (290 persons with recorded sex)

Male183 (63.1%)
↓-11.2%prior 206
Female107 (36.9%)
↓-27.7%prior 148

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: 312
  • Total persons involved: 459
  • Total vehicles involved: 393

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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