Yearly Traffic Safety Analysis

342 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Clay County, total crashes rose from 247 in 2016 to 342 in 2017, a 38.5% increase. While total crashes and injuries saw a significant rise, the most notable year-over-year shift was in DUI-related crashes, which increased from 6 to 13 incidents.

342

▲ 38.5%was 247

Total Crash Events

1

Persons Killed

120

▲ 57.9%was 76

Persons Injured

1

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend shows a significant year-over-year increase in traffic collisions. Total crashes rose by 38.5%, from 247 to 342. Correspondingly, the number of people injured increased by 57.9%, from 76 to 120, while the number of fatalities remained stable at one.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 1↓-100.0%

1

Pedestrians Injured

Prior: 1→0.0%

3

Cyclists Injured

Prior: 3→0.0%

116

Motorists Injured

Prior: 71↑63.4%

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 peak day for crashes was consistently Friday in both years, with the count increasing from 51 in 2016 to 77 in 2017. The peak hour for collisions shifted later in the afternoon, moving from the 3 p.m. hour in 2016 (28 crashes) to the 5 p.m. hour in 2017 (28 crashes).

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

The number of fatal crashes remained unchanged at one in both 2016 and 2017, causing the fatal crash rate per collision to decrease from 0.4% to 0.3%. Crashes resulting in minor injuries more than doubled, increasing from 22 to 48 incidents, while the count of serious injury crashes fell from 3 to 2. The proportion of crashes with no injuries decreased slightly from 74.1% to 71.9%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
→0.0%prior 1
Serious Injury2serious injury crashes0.6%
↓-33.3%prior 3
Minor Injury48minor injury crashes14%
↑118.2%prior 22
Possible Injury45possible injury crashes13.2%
↑18.4%prior 38
No Injury246no injury crashes71.9%
↑34.4%prior 183

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 involving an animal remained the top contributing factor in both periods, with the count increasing by 68.9% from 45 crashes in 2016 to 76 in 2017. 'Failure to yield from a stop sign' was the second-most cited factor, with its count rising from 22 to 31. Notably, crashes attributed to 'driving too fast for conditions' more than doubled, increasing from 10 to 22 incidents.

Officer-Reported Primary Contributing Cause

Animal76 (22.2%)↑68.9%prior 45
FTYROW: From stop sign31 (9.1%)↑40.9%prior 22
Other (explain in narrative): Other25 (7.3%)↑78.6%prior 14
Driving too fast for conditions22 (6.4%)↑120.0%prior 10
Followed too close17 (5%)↑88.9%prior 9
Ran Stop Sign15 (4.4%)↓-25.0%prior 20
Lost Control13 (3.8%)↓-18.8%prior 16
Driver Distraction: Other interior distraction11 (3.2%)
FTYROW: Making left turn10 (2.9%)↑42.9%prior 7
Ran off road - left10 (2.9%)

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 environmental conditions at the time of crashes were largely similar year-over-year. In both 2017 and 2016, the majority of crashes occurred in clear weather (62.9% and 64.0% of crashes, respectively) and during daylight hours (62.9% and 64.8%). Similarly, crashes on dry road surfaces accounted for a consistent majority, with 63.4% in 2017 and 62.3% in 2016.

Weather

Clear215 (71.7%)
↑36.1%prior 158
Cloudy52 (17.3%)
↑136.4%prior 22
Rain11 (3.7%)
↓-8.3%prior 12
Freezing rain/drizzle8 (2.7%)
Snow6 (2.0%)
↓-50.0%prior 12
Blowing Snow4 (1.3%)
↓-33.3%prior 6
Fog, smoke, smog2 (0.7%)
Other (explain in narrative)1 (0.3%)
Blowing sand, soil, dirt1 (0.3%)

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

Lighting

Daylight215 (71.4%)
↑34.4%prior 160
Dark - roadway not lighted41 (13.6%)
↑51.9%prior 27
Dark - roadway lighted26 (8.6%)
↑44.4%prior 18
Dawn7 (2.3%)
→0.0%prior 7
Dusk7 (2.3%)
↑16.7%prior 6
Dark - unknown roadway lighting5 (1.7%)

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

Road Surface

Dry217 (72.3%)
↑40.9%prior 154
Wet32 (10.7%)
↑23.1%prior 26
Ice/frost20 (6.7%)
↑42.9%prior 14
Snow14 (4.7%)
↓-12.5%prior 16
Gravel14 (4.7%)
↑133.3%prior 6
Slush2 (0.7%)
Mud, dirt1 (0.3%)

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

Vehicles & Demographics

Chevrolet and Ford were the most common vehicle makes involved in crashes for both years, and both saw an increase in counts. The number of Chevrolet vehicles involved rose from 84 to 143, while Fords increased from 66 to 89. Examining person demographics, the proportion of individuals aged 65 and older involved in crashes grew, accounting for 14.9% of all persons in 2017 compared to 11.6% in 2016.

Top Vehicle Makes (559 vehicles)

1
CHEV100 (17.9%)
↑138.1%prior 42
2
FORD89 (15.9%)
↑34.8%prior 66
3
CHEVROLET43 (7.7%)
↑2.4%prior 42
4
DODG33 (5.9%)
↑83.3%prior 18
5
DODGE27 (4.8%)
↑17.4%prior 23
6
BUIC25 (4.5%)
↑108.3%prior 12
7
GMC25 (4.5%)
↑25.0%prior 20
8
CHRY23 (4.1%)
↑27.8%prior 18
9
JEEP19 (3.4%)
↑26.7%prior 15
10
TOYT16 (2.9%)
↑14.3%prior 14

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

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

Sex Distribution (416 persons with recorded sex)

Male241 (57.9%)
↑49.7%prior 161
Female175 (42.1%)
↑8.7%prior 161

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: 342
  • Total persons involved: 662
  • Total vehicles involved: 559

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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