Yearly Traffic Safety Analysis

210 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Clarke County, total vehicle crashes increased slightly from 202 in 2016 to 210 in 2017, a change of approximately 4.0%. Despite the rise in total incidents, there was a significant year-over-year improvement in crash outcomes. The most notable shift was a 50% reduction in fatalities, which fell from 6 to 3, and a 42.2% decrease in total injuries, dropping from 83 to 48.

210

▲ 4.0%was 202

Total Crash Events

3

▼ -50.0%was 6

Persons Killed

48

▼ -42.2%was 83

Persons Injured

2

▼ -50.0%was 4

Fatal Crash Events

Note: "Persons Killed" (3) 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

The overall trend in Clarke County shows a minor increase in the total number of crashes, which rose by 8 incidents from 2016 to 2017. However, this was accompanied by a positive trend in safety outcomes, as both the number of individuals killed and injured in crashes decreased substantially. Fatalities were halved from 6 to 3, and injuries fell from 83 to 48.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 6↓-50.0%

1

Pedestrians Injured

Prior: 3↓-66.7%

47

Motorists Injured

Prior: 79↓-40.5%

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 shifts between the two years. The peak day for crashes moved from Tuesday (37 crashes) in 2016 to Friday (38 crashes) in 2017. The peak hour for collisions remained 6 p.m. in both periods, although the number of incidents during that hour decreased slightly from 18 in 2016 to 16 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 improved significantly from 2016 to 2017. The fatal crash rate decreased by more than half, from 1.98 to 0.95, with the count of fatal crashes dropping from 4 to 2. The proportion of crashes involving serious injuries also fell sharply, from 4.5% of all incidents in 2016 (9 crashes) to 1.0% in 2017 (2 crashes). Consequently, the share of crashes resulting in no injuries increased from 74.8% to 80.0% year-over-year.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1%
↓-50.0%prior 4
Serious Injury2serious injury crashes1%
↓-77.8%prior 9
Minor Injury18minor injury crashes8.6%
↓-18.2%prior 22
Possible Injury20possible injury crashes9.5%
↑25.0%prior 16
No Injury168no injury crashes80%
↑11.3%prior 151

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, with the count of such incidents increasing by 40% from 50 crashes in 2016 to 70 in 2017. In contrast, crashes attributed to a driver losing control decreased in count from 21 to 17. Incidents involving driving too fast for conditions were also reduced by 50%, falling from 8 crashes in 2016 to 4 in 2017.

Officer-Reported Primary Contributing Cause

Animal70 (33.3%)↑40.0%prior 50
Other (explain in narrative): Other17 (8.1%)↑41.7%prior 12
Lost Control17 (8.1%)↓-19.0%prior 21
Ran off road - straight16 (7.6%)↑6.7%prior 15
FTYROW: From stop sign12 (5.7%)↑9.1%prior 11
Ran off road - left9 (4.3%)↑50.0%prior 6
FTYROW: Making left turn7 (3.3%)↑16.7%prior 6
Operating vehicle in an reckless, erratic, careless, negligent manner6 (2.9%)→0.0%prior 6
Followed too close6 (2.9%)↓-25.0%prior 8
Driving too fast for conditions4 (1.9%)↓-50.0%prior 8

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

Road & Environmental Conditions

Year-over-year, there was a shift in lighting conditions during crashes. The proportion of crashes occurring in daylight decreased from 56.4% in 2016 to 49.0% in 2017, while crashes on unlit dark roadways increased in count from 23 to 39. Regarding road surface conditions, incidents on snowy, icy, or slush-covered roads decreased from 17 in 2016 to 11 in 2017. Weather conditions were broadly similar, though snow-related crashes fell from 15 to 8.

Weather

Clear93 (57.1%)
↑3.3%prior 90
Cloudy46 (28.2%)
→0.0%prior 46
Rain9 (5.5%)
↑28.6%prior 7
Snow8 (4.9%)
↓-42.9%prior 14
Fog, smoke, smog4 (2.5%)
Freezing rain/drizzle3 (1.8%)

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

Lighting

Daylight103 (62.4%)
↓-9.6%prior 114
Dark - roadway not lighted39 (23.6%)
↑69.6%prior 23
Dark - roadway lighted13 (7.9%)
↑8.3%prior 12
Dusk6 (3.6%)
→0.0%prior 6
Dawn3 (1.8%)
↓-57.1%prior 7
Dark - unknown roadway lighting1 (0.6%)

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

Road Surface

Dry123 (75.0%)
↑12.8%prior 109
Wet20 (12.2%)
↑5.3%prior 19
Gravel10 (6.1%)
↓-37.5%prior 16
Ice/frost5 (3.0%)
Snow5 (3.0%)
↓-54.5%prior 11
Slush1 (0.6%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes, including Ford, Chevrolet, and Dodge, remained consistent across both years. A notable demographic shift occurred among persons involved in crashes; involvement of the 55-64 age group increased from 45 individuals in 2016 to 57 in 2017, making it the largest cohort. Conversely, the number of individuals aged 65 and older involved in crashes decreased from 53 to 37.

Top Vehicle Makes (305 vehicles)

1
FORD49 (16.1%)
→0.0%prior 49
2
CHEV36 (11.8%)
→0.0%prior 36
3
CHEVROLET31 (10.2%)
↓-20.5%prior 39
4
DODG16 (5.2%)
↑33.3%prior 12
5
DODGE15 (4.9%)
↓-25.0%prior 20
6
HOND11 (3.6%)
↑57.1%prior 7
7
BUIC10 (3.3%)
↑42.9%prior 7
8
NISS9 (3%)
9
KIA8 (2.6%)
↑33.3%prior 6
10
HONDA8 (2.6%)
↑60.0%prior 5

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

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

Sex Distribution (233 persons with recorded sex)

Male142 (60.9%)
↑8.4%prior 131
Female91 (39.1%)
↑5.8%prior 86

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: 210
  • Total persons involved: 338
  • Total vehicles involved: 305

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

ThatCarHitMe.com · An Injuria.ai Company