Yearly Traffic Safety Analysis

247 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Clay County, traffic crashes decreased by 18.8% from 304 in 2015 to 247 in 2016. Total injuries also fell from 93 to 76, while fatalities remained constant at one. The most significant year-over-year change was in contributing factors, where crashes attributed to 'Ran Stop Sign' increased from 7 to 20 incidents.

247

▼ -18.8%was 304

Total Crash Events

1

Persons Killed

76

▼ -18.3%was 93

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 · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic safety trends in Clay County showed improvement from 2015 to 2016. The total number of crashes declined by 57, from 304 to 247, representing an 18.8% reduction. This downward trend was mirrored in total injuries, which decreased by 18.3% from 93 to 76, while the number of fatalities held steady at one for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 0→%

0

Other Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 5↓-80.0%

3

Cyclists Injured

Prior: 5↓-40.0%

71

Motorists Injured

Prior: 82↓-13.4%

1

Other Injured

Prior: 1→0.0%

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 years. In 2016, Friday was the peak day for crashes with 51 incidents, a change from 2015 when Wednesday was the peak day with 62 crashes. However, the peak hour for collisions remained consistent, with the 3 p.m. hour having the highest frequency in both 2016 (28 crashes) and 2015 (34 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 severity of crashes saw some changes year-over-year, though the number of fatal incidents remained stable at one in both 2015 and 2016. The count of serious injury crashes decreased from 5 to 3, and minor injury crashes fell from 32 to 22. Conversely, the proportion of crashes resulting in possible injury increased from 14.1% in 2015 to 15.4% in 2016, and the share of no-injury crashes rose slightly from 73.4% to 74.1%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
→0.0%prior 1
Serious Injury3serious injury crashes1.2%
↓-40.0%prior 5
Minor Injury22minor injury crashes8.9%
↓-31.3%prior 32
Possible Injury38possible injury crashes15.4%
↓-11.6%prior 43
No Injury183no injury crashes74.1%
↓-17.9%prior 223

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

While 'Animal' remained the top contributing factor in both years, its count decreased from 57 in 2015 to 45 in 2016. The most dramatic shift was in crashes attributed to 'Ran Stop Sign,' which surged from 7 incidents in 2015 to 20 in 2016, a 186% increase in count. In contrast, crashes involving 'Lost Control' decreased from 21 to 16, and incidents related to 'Driving too fast for conditions' fell from 17 to 10.

Officer-Reported Primary Contributing Cause

Animal45 (18.2%)↓-21.1%prior 57
FTYROW: From stop sign22 (8.9%)→0.0%prior 22
Ran Stop Sign20 (8.1%)↑185.7%prior 7
Lost Control16 (6.5%)↓-23.8%prior 21
Other (explain in narrative): Other14 (5.7%)↓-48.1%prior 27
Driving too fast for conditions10 (4%)↓-41.2%prior 17
Followed too close9 (3.6%)↓-10.0%prior 10
Operating vehicle in an reckless, erratic, careless, negligent manner7 (2.8%)→0.0%prior 7
FTYROW: From driveway7 (2.8%)
FTYROW: At uncontrolled intersection7 (2.8%)↓-50.0%prior 14

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

Road & Environmental Conditions

Comparing conditions, a higher percentage of crashes in 2016 occurred in clear weather (64.0%) and on dry roads (62.3%) compared to 2015 (55.6% and 57.2%, respectively). There was a notable decrease in crashes on snowy road surfaces, which fell from 35 incidents in 2015 to 16 in 2016. Similarly, crashes during cloudy weather decreased from 49 to 22. The proportion of crashes in daylight was stable, at 64.8% in 2016 versus 63.5% in 2015.

Weather

Clear158 (73.5%)
↓-6.5%prior 169
Cloudy22 (10.2%)
↓-55.1%prior 49
Snow12 (5.6%)
↓-20.0%prior 15
Rain12 (5.6%)
↓-14.3%prior 14
Blowing Snow6 (2.8%)
Fog, smoke, smog4 (1.9%)
Freezing rain/drizzle1 (0.5%)

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

Lighting

Daylight160 (73.1%)
↓-17.1%prior 193
Dark - roadway not lighted27 (12.3%)
↓-3.6%prior 28
Dark - roadway lighted18 (8.2%)
↓-18.2%prior 22
Dawn7 (3.2%)
↑40.0%prior 5
Dusk6 (2.7%)
↓-25.0%prior 8
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry154 (71.0%)
↓-11.5%prior 174
Wet26 (12.0%)
→0.0%prior 26
Snow16 (7.4%)
↓-54.3%prior 35
Ice/frost14 (6.5%)
↑27.3%prior 11
Gravel6 (2.8%)
↓-25.0%prior 8
Slush1 (0.5%)

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

Vehicles & Demographics

Analysis of vehicles involved shows a decrease in total vehicles from 486 in 2015 to 402 in 2016. Vehicles made by Chevrolet saw a substantial drop from 126 to 84, and Ford vehicles decreased from 77 to 66. In terms of persons involved, there was a significant decline in the 16-20 age group, whose involvement fell from 87 individuals in 2015 to 51 in 2016.

Top Vehicle Makes (402 vehicles)

1
FORD66 (16.4%)
↓-14.3%prior 77
2
CHEVROLET42 (10.4%)
↓-22.2%prior 54
3
CHEV42 (10.4%)
↓-41.7%prior 72
4
DODGE23 (5.7%)
↑64.3%prior 14
5
GMC20 (5%)
↑11.1%prior 18
6
DODG18 (4.5%)
↓-25.0%prior 24
7
CHRY18 (4.5%)
↑12.5%prior 16
8
JEEP15 (3.7%)
↑66.7%prior 9
9
TOYT14 (3.5%)
↑27.3%prior 11
10
BUIC12 (3%)
↓-40.0%prior 20

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

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

Sex Distribution (322 persons with recorded sex)

Female161 (50.0%)
↓-20.7%prior 203
Male161 (50.0%)
↓-31.8%prior 236

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: 247
  • Total persons involved: 455
  • Total vehicles involved: 402

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