Yearly Traffic Safety Analysis

333 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Harrison County recorded 333 total crashes, a slight increase of 2.5% from the 325 crashes reported in 2015. Despite this small rise in total collisions, the number of fatalities decreased by half, from two in 2015 to one in 2016. A notable positive trend was the 38.9% decrease in crashes involving suspected driving under the influence (DUI), which fell from 18 incidents in 2015 to 11 in 2016.

333

▲ 2.5%was 325

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

115

▼ -3.4%was 119

Persons Injured

1

▼ -50.0%was 2

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

Traffic collisions in Harrison County showed a slight upward trend, increasing by 2.5% from 325 incidents in 2015 to 333 in 2016. However, the outcomes of these crashes improved, with total fatalities dropping by 50% from two to one. Total injuries also saw a minor reduction, declining by 3.4% from 119 to 115 persons injured year-over-year.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 1→0.0%

115

Motorists Injured

Prior: 118↓-2.5%

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 showed some year-over-year shifts. In 2016, the peak days for crashes were Thursday, Friday, and Saturday, each with 55 incidents, expanding from a single peak day of Thursday (54 crashes) in 2015. The most common time for a crash shifted one hour earlier to the 5 p.m. hour in 2016, which saw 25 crashes, compared to the 6 p.m. hour peak of 26 crashes in 2015.

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 overall severity of crashes in Harrison County decreased from 2015 to 2016. The number of fatal crashes was halved, dropping from two to one, which lowered the fatal crash rate from 0.6% to 0.3% of all collisions. The proportion of crashes resulting in any level of injury also declined from 26.8% in 2015 to 23.1% in 2016, while the share of no-injury crashes increased from 72.6% to 76.6%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
↓-50.0%prior 2
Serious Injury16serious injury crashes4.8%
↑14.3%prior 14
Minor Injury26minor injury crashes7.8%
↓-25.7%prior 35
Possible Injury35possible injury crashes10.5%
↓-7.9%prior 38
No Injury255no injury crashes76.6%
↑8.1%prior 236

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, with the count increasing by 12.5% from 72 crashes in 2015 to 81 in 2016. "Lost Control" was the second most cited factor in both years, with a nearly identical count of 43 in 2015 and 44 in 2016. The third-ranked factor changed year-over-year; "Ran off road - straight" was third in 2015 with 29 incidents but fell to 21 incidents in 2016, a 27.6% decrease in count.

Officer-Reported Primary Contributing Cause

Animal81 (24.3%)↑12.5%prior 72
Lost Control44 (13.2%)↑2.3%prior 43
Driving too fast for conditions23 (6.9%)↑9.5%prior 21
Followed too close21 (6.3%)↓-8.7%prior 23
Ran off road - straight21 (6.3%)↓-27.6%prior 29
Ran off road - left18 (5.4%)↑28.6%prior 14
FTYROW: From stop sign12 (3.6%)↑9.1%prior 11
Other (explain in narrative): Other10 (3%)↓-28.6%prior 14
Operating vehicle in an reckless, erratic, careless, negligent manner10 (3%)
Driver Distraction: Other interior distraction10 (3%)↑100.0%prior 5

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 2015 and 2016 occurred on dry roads in clear weather. In 2016, the proportion of crashes in clear weather increased, accounting for 53.5% of incidents compared to 47.1% in 2015. Crashes during daylight hours decreased from 184 to 171, while those on dark, unlighted roadways increased from 49 to 55. Incidents on adverse road surfaces saw a decline, with wet-road crashes falling from 31 to 23 and ice-related crashes dropping from 27 to 18.

Weather

Clear178 (66.7%)
↑16.3%prior 153
Cloudy54 (20.2%)
↓-6.9%prior 58
Snow20 (7.5%)
↓-16.7%prior 24
Rain7 (2.6%)
↓-65.0%prior 20
Freezing rain/drizzle6 (2.2%)
↓-25.0%prior 8
Fog, smoke, smog2 (0.7%)

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

Lighting

Daylight171 (63.8%)
↓-7.1%prior 184
Dark - roadway not lighted55 (20.5%)
↑12.2%prior 49
Dark - roadway lighted25 (9.3%)
↑25.0%prior 20
Dawn12 (4.5%)
↑20.0%prior 10
Dusk4 (1.5%)
↓-42.9%prior 7
Dark - unknown roadway lighting1 (0.4%)

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

Road Surface

Dry185 (68.8%)
↑1.6%prior 182
Wet23 (8.6%)
↓-25.8%prior 31
Snow22 (8.2%)
→0.0%prior 22
Ice/frost18 (6.7%)
↓-33.3%prior 27
Gravel13 (4.8%)
↑85.7%prior 7
Slush3 (1.1%)
Mud, dirt3 (1.1%)
Sand1 (0.4%)
Other (explain in narrative)1 (0.4%)

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 consistent year-over-year, with Ford and Chevrolet being the most common in both 2016 and 2015. Ford vehicles were involved in 87 crashes in 2016, compared to 90 in the prior year. An analysis of persons involved shows a notable decrease in the 45-54 age group, which fell from 102 individuals in 2015 to 78 in 2016. The counts for other age demographics remained relatively stable between the two periods.

Top Vehicle Makes (471 vehicles)

1
FORD87 (18.5%)
↓-3.3%prior 90
2
CHEVROLET55 (11.7%)
→0.0%prior 55
3
CHEV38 (8.1%)
↑11.8%prior 34
4
DODGE23 (4.9%)
→0.0%prior 23
5
TOYOTA19 (4%)
↑216.7%prior 6
6
JEEP14 (3%)
↓-6.7%prior 15
7
NISSAN14 (3%)
↑55.6%prior 9
8
FREIGHTLINER13 (2.8%)
→0.0%prior 13
9
HONDA13 (2.8%)
↑30.0%prior 10
10
GMC12 (2.5%)
↑9.1%prior 11

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

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

Sex Distribution (347 persons with recorded sex)

Male219 (63.1%)
↓-2.2%prior 224
Female128 (36.9%)
↓-15.2%prior 151

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: 333
  • Total persons involved: 532
  • Total vehicles involved: 471

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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