Yearly Traffic Safety Analysis

306 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Clayton County, total vehicle crashes remained relatively stable year-over-year, decreasing slightly from 312 incidents in 2016 to 306 in 2017, a change of -1.9%. The most significant development was a sharp reduction in crash severity. The number of fatalities fell from four in the prior period to one in the current period, representing a 75% decrease.

306

▼ -1.9%was 312

Total Crash Events

1

▼ -75.0%was 4

Persons Killed

80

▼ -13.0%was 92

Persons Injured

1

▼ -75.0%was 4

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 in traffic incidents shows a slight decline in volume but a significant improvement in outcomes. While total crashes decreased by just 1.9% from 312 to 306, the number of people injured fell by 13% from 92 to 80. Fatalities also saw a substantial drop from four individuals in 2016 to one in 2017.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

1

Motorists Killed

Prior: 3↓-66.7%

1

Pedestrians Injured

Prior: 1→0.0%

79

Motorists Injured

Prior: 91↓-13.2%

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 temporal patterns of crashes showed some consistency and some change between the two periods. The 5 PM hour remained the most frequent time for crashes in both 2017 (29 crashes) and 2016 (33 crashes). However, while Friday was the standalone peak day for crashes in 2016 with 53 incidents, in 2017 it tied with Wednesday, as both days recorded 48 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

Crash severity outcomes improved significantly from 2016 to 2017. The number of fatal crashes dropped from four to one, and the proportion of crashes involving any type of injury (fatal, serious, minor, or possible) decreased from 25.0% to 21.2%. Specifically, serious injury crashes fell from 12 to 8. Correspondingly, the share of crashes resulting in no injuries increased from 75.0% in 2016 to 78.8% in 2017.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
↓-75.0%prior 4
Serious Injury8serious injury crashes2.6%
↓-33.3%prior 12
Minor Injury29minor injury crashes9.5%
↓-3.3%prior 30
Possible Injury27possible injury crashes8.8%
↓-15.6%prior 32
No Injury241no injury crashes78.8%
↑3.0%prior 234

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 animals remained the leading contributing factor in both periods, though the count decreased from 132 incidents in 2016 to 124 in 2017. The second most common factor in 2017 was 'Lost Control,' which saw its count increase from 26 to 36 crashes year-over-year. Conversely, crashes attributed to 'Driving too fast for conditions' decreased notably, falling from 22 incidents in 2016 to 14 in 2017.

Officer-Reported Primary Contributing Cause

Animal124 (40.5%)↓-6.1%prior 132
Lost Control36 (11.8%)↑38.5%prior 26
Ran off road - straight18 (5.9%)↓-10.0%prior 20
Other (explain in narrative): Other15 (4.9%)→0.0%prior 15
Driving too fast for conditions14 (4.6%)↓-36.4%prior 22
Ran off road - left13 (4.2%)→0.0%prior 13
FTYROW: From stop sign8 (2.6%)↑14.3%prior 7
Followed too close7 (2.3%)↑16.7%prior 6
Driver Distraction: Inattentive/lost in thought7 (2.3%)
FTYROW: From parked position4 (1.3%)

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

Road & Environmental Conditions

There was a notable shift in the conditions under which crashes occurred, particularly related to winter weather. The number of crashes happening on snowy road surfaces dropped from 33 in 2016 to 10 in 2017, and incidents during snowy weather fell from 19 to 8. However, crashes on icy or frosty roads increased from 13 to 20 over the same period. The number of crashes occurring in daylight increased from 114 to 130, while those in unlit dark conditions decreased from 61 to 44.

Weather

Clear138 (71.5%)
↑5.3%prior 131
Cloudy27 (14.0%)
↓-12.9%prior 31
Rain8 (4.1%)
Snow8 (4.1%)
↓-57.9%prior 19
Freezing rain/drizzle6 (3.1%)
→0.0%prior 6
Fog, smoke, smog5 (2.6%)
Sleet, hail1 (0.5%)

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

Lighting

Daylight130 (66.0%)
↑14.0%prior 114
Dark - roadway not lighted44 (22.3%)
↓-27.9%prior 61
Dusk9 (4.6%)
↑50.0%prior 6
Dark - roadway lighted8 (4.1%)
↓-38.5%prior 13
Dawn6 (3.0%)
↓-33.3%prior 9

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

Road Surface

Dry125 (64.1%)
↓-1.6%prior 127
Gravel21 (10.8%)
↑31.3%prior 16
Ice/frost20 (10.3%)
↑53.8%prior 13
Wet19 (9.7%)
↑72.7%prior 11
Snow10 (5.1%)
↓-69.7%prior 33

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Chevrolet and Ford models being the most frequent in both years. The number of Chevrolets involved increased from 108 to 122, and Fords from 61 to 73. Analysis of persons involved shows an increased representation of older age groups; the number of individuals in the 45-54 age bracket rose from 56 to 75, and the 65+ age group grew from 57 to 68 people involved in crashes.

Top Vehicle Makes (396 vehicles)

1
FORD73 (18.4%)
↑19.7%prior 61
2
CHEV65 (16.4%)
↑80.6%prior 36
3
CHEVROLET57 (14.4%)
↓-20.8%prior 72
4
DODGE23 (5.8%)
↓-25.8%prior 31
5
CHRYSLER16 (4%)
↑77.8%prior 9
6
DODG14 (3.5%)
↑27.3%prior 11
7
GMC10 (2.5%)
↓-23.1%prior 13
8
TOYT10 (2.5%)
9
PONT8 (2%)
→0.0%prior 8
10
BUIC8 (2%)

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

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

Sex Distribution (277 persons with recorded sex)

Male173 (62.5%)
↓-5.5%prior 183
Female104 (37.5%)
↓-2.8%prior 107

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: 306
  • Total persons involved: 461
  • Total vehicles involved: 396

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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