Yearly Traffic Safety Analysis

368 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Henry County recorded 368 total crashes, representing a 7.6% increase from the 342 crashes documented in 2015. While overall collisions saw a modest rise, the number of crashes resulting in serious injuries increased substantially, climbing from 2 in 2015 to 14 in 2016.

368

▲ 7.6%was 342

Total Crash Events

5

▲ 66.7%was 3

Persons Killed

110

▲ 0.9%was 109

Persons Injured

5

▲ 66.7%was 3

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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 metrics in Henry County indicated a negative trend from 2015 to 2016. The total number of crashes increased by 7.6%, rising from 342 to 368. In line with this, the number of fatal crashes grew from 3 to 5, and total fatalities increased from 3 to 5 people.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 3↑66.7%

0

Other Killed

Prior: 0→0.0%

2

Pedestrians Injured

Prior: 0→%

3

Cyclists Injured

Prior: 1↑200.0%

104

Motorists Injured

Prior: 107↓-2.8%

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 slightly between the two periods. The peak day for crashes moved from Wednesday (65 crashes) in 2015 to Thursday (60 crashes) in 2016. The busiest hour for collisions also shifted, from 3 PM in the prior year (31 crashes) to 2 PM in the current year (27 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 worsened in 2016 compared to the prior year. The number of fatal crashes increased from 3 to 5, and their share of all incidents rose from 0.9% to 1.4%. Most significantly, serious injury crashes increased from just 2 incidents in 2015 to 14 in 2016, with their proportion of total crashes climbing from 0.6% to 3.8%. Consequently, the share of crashes resulting in no injuries fell from 76.3% to 75.5%.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.4%
↑66.7%prior 3
Serious Injury14serious injury crashes3.8%
↑600.0%prior 2
Minor Injury39minor injury crashes10.6%
↑18.2%prior 33
Possible Injury32possible injury crashes8.7%
↓-25.6%prior 43
No Injury278no injury crashes75.5%
↑6.5%prior 261

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 involving an animal remained the leading contributing factor in both years, though the count of these incidents decreased from 118 in 2015 to 110 in 2016. 'Lost Control' was the second most common factor, with its count increasing from 31 to 38 incidents. Crashes attributed to 'Followed too close' saw a notable drop in count from 15 to 8, while incidents involving 'Failure to Yield Right of Way from a stop sign' increased from 16 to 19.

Officer-Reported Primary Contributing Cause

Animal110 (29.9%)↓-6.8%prior 118
Lost Control38 (10.3%)↑22.6%prior 31
Other (explain in narrative): Other20 (5.4%)↑100.0%prior 10
FTYROW: From stop sign19 (5.2%)↑18.8%prior 16
Ran off road - straight17 (4.6%)↓-10.5%prior 19
Driving too fast for conditions17 (4.6%)↑21.4%prior 14
Ran Stop Sign12 (3.3%)→0.0%prior 12
FTYROW: Making left turn10 (2.7%)↑42.9%prior 7
Ran off road - left10 (2.7%)↓-9.1%prior 11
Other (explain in narrative): No improper action10 (2.7%)

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 periods occurred during daylight hours on dry roads. In 2016, the proportion of crashes on dry surfaces increased, accounting for 64.4% of incidents compared to 51.7% in 2015. The count of crashes occurring on dark, unlighted roadways rose from 54 to 73. Conversely, the share of crashes happening on adverse road surfaces like snow, ice, or wet pavement decreased from 17.0% in 2015 to 12.5% in 2016.

Weather

Clear193 (65.9%)
↑22.2%prior 158
Cloudy69 (23.5%)
↑35.3%prior 51
Rain11 (3.8%)
↑57.1%prior 7
Snow9 (3.1%)
↓-25.0%prior 12
Freezing rain/drizzle5 (1.7%)
↓-28.6%prior 7
Fog, smoke, smog3 (1.0%)
↓-62.5%prior 8
Sleet, hail1 (0.3%)
Other (explain in narrative)1 (0.3%)
Blowing Snow1 (0.3%)

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

Lighting

Daylight185 (62.9%)
↑12.8%prior 164
Dark - roadway not lighted73 (24.8%)
↑35.2%prior 54
Dark - roadway lighted25 (8.5%)
↑92.3%prior 13
Dawn6 (2.0%)
↓-40.0%prior 10
Dark - unknown roadway lighting3 (1.0%)
Dusk2 (0.7%)

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

Road Surface

Dry237 (80.3%)
↑33.9%prior 177
Wet21 (7.1%)
↑16.7%prior 18
Ice/frost14 (4.7%)
→0.0%prior 14
Gravel12 (4.1%)
↑20.0%prior 10
Snow8 (2.7%)
↓-57.9%prior 19
Slush2 (0.7%)
↓-60.0%prior 5
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 crashes remained consistent, led by Chevrolet, Ford, and Dodge. An analysis of person demographics reveals shifts in the age of those involved in crashes. The number of persons in the 55-64 age group involved in collisions increased from 63 to 92. In contrast, involvement for the 26-34 age group decreased from 111 to 85 persons.

Top Vehicle Makes (532 vehicles)

1
FORD86 (16.2%)
↑19.4%prior 72
2
CHEVROLET69 (13%)
↑81.6%prior 38
3
CHEV43 (8.1%)
↓-14.0%prior 50
4
DODGE29 (5.5%)
↓-27.5%prior 40
5
TOYT24 (4.5%)
↑4.3%prior 23
6
GMC20 (3.8%)
↑33.3%prior 15
7
DODG19 (3.6%)
↓-26.9%prior 26
8
TOYOTA18 (3.4%)
→0.0%prior 18
9
JEEP17 (3.2%)
→0.0%prior 17
10
CHRYSLER17 (3.2%)
↑13.3%prior 15

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

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

Sex Distribution (400 persons with recorded sex)

Male229 (57.3%)
↓-11.9%prior 260
Female171 (42.8%)
↓-3.9%prior 178

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: 368
  • Total persons involved: 604
  • Total vehicles involved: 532

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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