Yearly Traffic Safety Analysis

43 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Pocahontas County recorded 43 total crashes, a 4.9% increase from the 41 crashes reported in 2015. While fatal crashes remained at zero for both years, the number of people injured rose significantly from 17 in 2015 to 28 in 2016. This represents a 64.7% year-over-year increase in persons injured.

43

▲ 4.9%was 41

Total Crash Events

0

Persons Killed

28

▲ 64.7%was 17

Persons Injured

0

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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 collisions in Pocahontas County saw a slight increase in 2016 compared to the prior year, rising from 41 to 43 incidents. The number of people injured in these crashes increased more substantially, growing by 64.7% from 17 in 2015 to 28 in 2016. Fatalities remained unchanged at zero in both periods.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

1

Cyclists Injured

Prior: 0→%

27

Motorists Injured

Prior: 17↑58.8%

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 2015 and 2016. The most frequent day for crashes moved from Wednesday (8 crashes) in 2015 to Friday (11 crashes) in 2016. Similarly, the peak hour for collisions shifted an hour earlier from 4 p.m. in 2015 (4 crashes) to 3 p.m. in 2016, which saw a much higher concentration of incidents with 11 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

Crash severity increased in 2016 compared to the previous year, although no fatal crashes were recorded in either period. The number of crashes resulting in a serious injury doubled from 2 in 2015 to 4 in 2016, increasing their share of all crashes from 4.9% to 9.3%. Overall, the proportion of crashes involving any level of injury rose from 34.1% in 2015 to 44.2% in 2016.

Outcome by Severity (Crash Events)

Serious Injury4serious injury crashes9.3%
↑100.0%prior 2
Minor Injury9minor injury crashes20.9%
↑28.6%prior 7
Possible Injury6possible injury crashes14%
↑20.0%prior 5
No Injury24no injury crashes55.8%
↓-11.1%prior 27

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 significantly year-over-year. 'Ran off road - straight' became the primary factor in 2016, with its count doubling from 4 incidents in 2015 to 8. Conversely, 'Lost Control,' the top factor in 2015 with 5 crashes, decreased to just 2 crashes in 2016. Crashes involving animals and those attributed to 'Driving too fast for conditions' both doubled in count, from 2 to 4 incidents each.

Officer-Reported Primary Contributing Cause

Ran off road - straight8 (18.6%)
Animal4 (9.3%)
Driver Distraction: Other interior distraction4 (9.3%)
Driving too fast for conditions4 (9.3%)
FTYROW: From stop sign4 (9.3%)
FTYROW: At uncontrolled intersection3 (7%)
Lost Control2 (4.7%)↓-60.0%prior 5
Failure to dim lights/have lights on1 (2.3%)
Followed too close1 (2.3%)
FTYROW: From yield sign1 (2.3%)

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 lighting and weather conditions at the time of crashes remained relatively consistent year-over-year, there was a notable shift in road surface conditions. The proportion of collisions occurring on dry roads decreased from 73.2% in 2015 to 48.8% in 2016. Correspondingly, crashes on roads affected by ice, frost, or snow increased, accounting for 27.9% of all incidents in 2016 (12 crashes), up from 12.2% in 2015 (5 crashes).

Weather

Clear28 (66.7%)
↓-9.7%prior 31
Cloudy7 (16.7%)
↑40.0%prior 5
Blowing Snow3 (7.1%)
Rain3 (7.1%)
Snow1 (2.4%)

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

Lighting

Daylight30 (73.2%)
↑7.1%prior 28
Dark - roadway not lighted9 (22.0%)
↑28.6%prior 7
Dark - roadway lighted2 (4.9%)
↓-60.0%prior 5

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

Road Surface

Dry21 (48.8%)
↓-30.0%prior 30
Ice/frost6 (14.0%)
Snow6 (14.0%)
Gravel4 (9.3%)
Wet4 (9.3%)
Sand1 (2.3%)
Water (standing or moving)1 (2.3%)

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

Vehicles & Demographics

The vehicle makes most frequently involved in crashes changed between the two periods, with Chevrolet taking the top spot in 2016 with 14 vehicles, up from 10 in the prior year. Ford, the most common make in 2015 with 15 vehicles, dropped to second place with 9 vehicles in 2016. Analysis of persons involved shows a notable demographic shift, as the number of individuals aged 65 and older nearly doubled from 8 in 2015 to 15 in 2016.

Top Vehicle Makes (62 vehicles)

1
CHEVROLET14 (22.6%)
↑40.0%prior 10
2
FORD9 (14.5%)
↓-40.0%prior 15
3
PONTIAC4 (6.5%)
4
BUICK4 (6.5%)
5
PETERBILT3 (4.8%)
6
HONDA3 (4.8%)
7
CHRYSLER3 (4.8%)
8
DODGE3 (4.8%)
↓-62.5%prior 8
9
INTERNATIONA2 (3.2%)
10
KIA1 (1.6%)

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

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

Sex Distribution (40 persons with recorded sex)

Male28 (70.0%)
↓-17.6%prior 34
Female12 (30.0%)
↓-42.9%prior 21

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: 43
  • Total persons involved: 74
  • Total vehicles involved: 62

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