Yearly Traffic Safety Analysis

422 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Plymouth County, total vehicle crashes increased by 15.6% from 365 in 2015 to 422 in 2016. While overall collisions rose, the number of fatalities decreased from 4 to 3. The most significant year-over-year change was a 39.7% increase in the count of crashes attributed to animals, which rose from 63 to 88 incidents.

422

▲ 15.6%was 365

Total Crash Events

3

▼ -25.0%was 4

Persons Killed

178

▲ 16.3%was 153

Persons Injured

3

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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

Traffic safety trends in Plymouth County showed a general worsening from 2015 to 2016. The total number of crashes increased by 15.6% (from 365 to 422), and the number of people injured rose by 16.3% (from 153 to 178). However, the number of fatalities recorded saw a slight decrease from 4 to 3 in the same period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 4↓-25.0%

3

Pedestrians Injured

Prior: 0→%

5

Cyclists Injured

Prior: 2↑150.0%

170

Motorists Injured

Prior: 151↑12.6%

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 timing of crashes shifted between the two years. While the peak hour for collisions remained consistent at 3 p.m. in both 2015 and 2016, the peak day of the week changed. In 2016, Friday was the most frequent day for crashes with 85 incidents, a shift from Wednesday (59 incidents) in the prior 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

While total crashes increased, the severity profile saw a mixed change. The number of fatalities declined from 4 in 2015 to 3 in 2016, and the fatal crash rate per 100 crashes dropped from 0.82 to 0.71. However, the proportion of crashes resulting in some level of injury (serious, minor, or possible) increased slightly from 29.3% in 2015 to 30.5% in 2016.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.7%
→0.0%prior 3
Serious Injury16serious injury crashes3.8%
↑14.3%prior 14
Minor Injury55minor injury crashes13%
↑17.0%prior 47
Possible Injury58possible injury crashes13.7%
↑26.1%prior 46
No Injury290no injury crashes68.7%
↑13.7%prior 255

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 saw shifts in volume between 2015 and 2016. Collisions involving an animal remained the top factor in both years, with the count increasing by 39.7% from 63 to 88 incidents. In contrast, crashes attributed to a driver losing control, the second-ranked factor, decreased in count by 30.4% from 46 to 32. Following too closely also saw a notable increase, rising from 18 to 29 incidents.

Officer-Reported Primary Contributing Cause

Animal88 (20.9%)↑39.7%prior 63
Lost Control32 (7.6%)↓-30.4%prior 46
Followed too close29 (6.9%)↑61.1%prior 18
Ran off road - left28 (6.6%)↑21.7%prior 23
FTYROW: From stop sign24 (5.7%)→0.0%prior 24
FTYROW: At uncontrolled intersection21 (5%)↑75.0%prior 12
Ran off road - straight21 (5%)↓-12.5%prior 24
Other (explain in narrative): Other16 (3.8%)↑6.7%prior 15
Driving too fast for conditions16 (3.8%)↓-38.5%prior 26
FTYROW: Making left turn13 (3.1%)↑62.5%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

Comparatively, 2016 saw a smaller proportion of crashes occur under adverse road conditions than 2015. Crashes on icy roads decreased from 11.8% of the total in 2015 to 7.6% in 2016, and crashes on snowy surfaces fell from 10.4% to 7.3%. Conversely, the share of crashes occurring in dark, unlit conditions increased from 14.5% to 18.2% year-over-year, while the proportion of daylight crashes declined.

Weather

Clear221 (62.6%)
↑18.2%prior 187
Cloudy83 (23.5%)
↑22.1%prior 68
Snow22 (6.2%)
↓-12.0%prior 25
Rain9 (2.5%)
→0.0%prior 9
Blowing Snow5 (1.4%)
Freezing rain/drizzle5 (1.4%)
↓-50.0%prior 10
Severe Winds4 (1.1%)
Fog, smoke, smog4 (1.1%)

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

Lighting

Daylight236 (66.9%)
↑1.7%prior 232
Dark - roadway not lighted77 (21.8%)
↑45.3%prior 53
Dark - roadway lighted27 (7.6%)
↑58.8%prior 17
Dawn7 (2.0%)
→0.0%prior 7
Dusk5 (1.4%)
Dark - unknown roadway lighting1 (0.3%)

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

Road Surface

Dry238 (67.2%)
↑19.6%prior 199
Ice/frost32 (9.0%)
↓-25.6%prior 43
Wet31 (8.8%)
↑82.4%prior 17
Snow31 (8.8%)
↓-18.4%prior 38
Gravel9 (2.5%)
↑12.5%prior 8
Sand6 (1.7%)
Slush5 (1.4%)
Other (explain in narrative)1 (0.3%)
Mud, dirt1 (0.3%)

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

Vehicles & Demographics

The most common vehicle makes involved in collisions, Ford and Chevrolet, remained consistent across both years. Analysis of persons involved in crashes shows a demographic shift between periods. The 26-34 age group's involvement increased from 114 individuals in 2015 to 131 in 2016. In contrast, involvement for the 16-20 age group decreased from 133 to 125, and for the 65+ age group, it fell from 81 to 66.

Top Vehicle Makes (664 vehicles)

1
FORD109 (16.4%)
↑17.2%prior 93
2
CHEVROLET92 (13.9%)
↑84.0%prior 50
3
CHEV59 (8.9%)
↓-26.3%prior 80
4
GMC26 (3.9%)
↑8.3%prior 24
5
DODG25 (3.8%)
↓-3.8%prior 26
6
PONT19 (2.9%)
↑11.8%prior 17
7
BUICK18 (2.7%)
↑260.0%prior 5
8
JEEP18 (2.7%)
↑80.0%prior 10
9
PONTIAC18 (2.7%)
↑80.0%prior 10
10
KIA17 (2.6%)
↑54.5%prior 11

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

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

Sex Distribution (479 persons with recorded sex)

Male293 (61.2%)
↑1.0%prior 290
Female186 (38.8%)
↓-13.5%prior 215

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: 422
  • Total persons involved: 771
  • Total vehicles involved: 664

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