Yearly Traffic Safety Analysis

345 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Henry County recorded 345 total crashes, representing a 6.3% decrease from the 368 crashes documented in 2016. The most significant year-over-year change was a substantial reduction in traffic fatalities, which fell by 60% from 5 in 2016 to 2 in 2017. Total injuries also decreased by 10.9%, from 110 to 98.

345

▼ -6.3%was 368

Total Crash Events

2

▼ -60.0%was 5

Persons Killed

98

▼ -10.9%was 110

Persons Injured

2

▼ -60.0%was 5

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic incidents in Henry County showed a downward trend between 2016 and 2017. Total crashes decreased by 6.3% from 368 to 345. This trend extended to crash outcomes, with total injuries falling by 10.9% (from 110 to 98) and fatalities dropping by 60% (from 5 to 2).

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 5↓-60.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

1

Cyclists Injured

Prior: 3↓-66.7%

96

Motorists Injured

Prior: 104↓-7.7%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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 periods. In 2017, the peak day for crashes was Saturday with 59 incidents, whereas in 2016 it was Thursday with 60 incidents. The peak hour also moved from the afternoon to the evening commute, shifting from 2 p.m. (27 crashes) in 2016 to 5 p.m. (28 crashes) in 2017.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased from 2016 to 2017. The number of fatal crashes dropped from 5 to 2, and their share of all crashes fell from 1.4% to 0.6%. Similarly, serious injury crashes were halved, decreasing from 14 incidents (3.8% of total) in 2016 to 7 incidents (2.0% of total) in 2017. The proportion of crashes resulting in no injury increased slightly from 75.5% to 76.5%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.6%
↓-60.0%prior 5
Serious Injury7serious injury crashes2%
↓-50.0%prior 14
Minor Injury40minor injury crashes11.6%
↑2.6%prior 39
Possible Injury32possible injury crashes9.3%
→0.0%prior 32
No Injury264no injury crashes76.5%
↓-5.0%prior 278

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with an animal remained the top contributing factor in both years, with the count of such incidents increasing by 12.7% from 110 crashes in 2016 to 124 in 2017. "Lost Control" was the second-most common factor in both periods, with a nearly stable count of 38 and 37 crashes, respectively. "Failure to yield from a stop sign" ranked third in 2017 with 22 incidents, an increase from 19 the prior year.

Officer-Reported Primary Contributing Cause

Animal124 (35.9%)↑12.7%prior 110
Lost Control37 (10.7%)↓-2.6%prior 38
FTYROW: From stop sign22 (6.4%)↑15.8%prior 19
Ran off road - straight15 (4.3%)↓-11.8%prior 17
Driver Distraction: Other interior distraction12 (3.5%)
Followed too close11 (3.2%)↑37.5%prior 8
Driving too fast for conditions11 (3.2%)↓-35.3%prior 17
Ran off road - left10 (2.9%)→0.0%prior 10
Ran Stop Sign9 (2.6%)↓-25.0%prior 12
Operating vehicle in an reckless, erratic, careless, negligent manner9 (2.6%)↑50.0%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of environmental conditions remained largely consistent, with most crashes in both periods occurring in clear weather on dry roads during daylight. In 2017, there was a slight increase in the proportion of crashes happening on dark, unlighted roadways, which accounted for 24.6% of incidents compared to 19.8% in 2016. Crashes during cloudy weather saw a proportional decrease, from 18.8% of the total in 2016 to 11.6% in 2017.

Weather

Clear198 (74.7%)
↑2.6%prior 193
Cloudy40 (15.1%)
↓-42.0%prior 69
Rain14 (5.3%)
↑27.3%prior 11
Snow5 (1.9%)
↓-44.4%prior 9
Fog, smoke, smog4 (1.5%)
Freezing rain/drizzle4 (1.5%)
↓-20.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight152 (56.9%)
↓-17.8%prior 185
Dark - roadway not lighted85 (31.8%)
↑16.4%prior 73
Dark - roadway lighted15 (5.6%)
↓-40.0%prior 25
Dusk7 (2.6%)
Dawn6 (2.2%)
→0.0%prior 6
Dark - unknown roadway lighting2 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry210 (78.7%)
↓-11.4%prior 237
Wet25 (9.4%)
↑19.0%prior 21
Gravel17 (6.4%)
↑41.7%prior 12
Snow8 (3.0%)
→0.0%prior 8
Ice/frost6 (2.2%)
↓-57.1%prior 14
Water (standing or moving)1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The vehicle makes most frequently involved in crashes remained consistent, with Chevrolet and Ford leading in both 2017 and 2016. A notable shift occurred in the age distribution of persons involved in crashes. The count of individuals aged 16-20 increased from 76 to 86, while the 55-64 age group saw a significant decrease from 92 persons in 2016 to 67 in 2017.

Top Vehicle Makes (467 vehicles)

1
FORD81 (17.3%)
↓-5.8%prior 86
2
CHEV68 (14.6%)
↑58.1%prior 43
3
CHEVROLET39 (8.4%)
↓-43.5%prior 69
4
TOYOTA23 (4.9%)
↑27.8%prior 18
5
GMC22 (4.7%)
↑10.0%prior 20
6
DODG21 (4.5%)
↑10.5%prior 19
7
DODGE20 (4.3%)
↓-31.0%prior 29
8
TOYT15 (3.2%)
↓-37.5%prior 24
9
HONDA14 (3%)
↑7.7%prior 13
10
PONT13 (2.8%)
↑85.7%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (370 persons with recorded sex)

Male231 (62.4%)
↑0.9%prior 229
Female139 (37.6%)
↓-18.7%prior 171

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 345
  • Total persons involved: 537
  • Total vehicles involved: 467

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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