Yearly Traffic Safety Analysis

156 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Cherokee County, total traffic crashes decreased by 20%, from 195 in 2016 to 156 in 2017. While the number of fatalities remained stable at one, the most notable year-over-year shift was a 36.6% reduction in the total number of injuries, which fell from 71 to 45.

156

▼ -20.0%was 195

Total Crash Events

1

Persons Killed

45

▼ -36.6%was 71

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 in Cherokee County shows a decline in traffic incidents from 2016 to 2017. Total crashes fell by 20% from 195 to 156, and the number of people injured decreased by 36.6% from 71 to 45. The number of fatalities was unchanged, with one person killed in a crash in both periods.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

44

Motorists Injured

Prior: 69↓-36.2%

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 shifts between the two periods. Friday remained the peak day for crashes in both 2016 (40 crashes) and 2017 (29 crashes). However, the peak hour for collisions shifted from the evening commute at 6 p.m. in 2016, which saw 23 crashes, to the morning commute at 7 a.m. in 2017, which recorded 16 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 severity of crashes shifted year-over-year, with a notable decrease in injury-related incidents. The number of fatal crashes was unchanged at one in both 2016 and 2017, though the fatal crash rate as a percentage of all crashes increased from 0.51% to 0.64%. Crashes resulting in serious injuries fell from 9 to 4, and minor injury crashes decreased from 23 to 14. The count of possible injury crashes was stable at 21 for both years.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.6%
→0.0%prior 1
Serious Injury4serious injury crashes2.6%
↓-55.6%prior 9
Minor Injury14minor injury crashes9%
↓-39.1%prior 23
Possible Injury21possible injury crashes13.5%
→0.0%prior 21
No Injury116no injury crashes74.4%
↓-17.7%prior 141

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

The leading contributing factors for crashes showed some changes between 2016 and 2017. Collisions involving an animal remained the top factor in both years, though the count decreased from 48 in 2016 to 39 in 2017. The most significant increase was in crashes due to failure to yield from a stop sign, which tripled in count from 5 to 15. Conversely, crashes attributed to following too closely saw a significant decrease, dropping from 13 incidents in 2016 to 5 in 2017.

Officer-Reported Primary Contributing Cause

Animal39 (25%)↓-18.8%prior 48
FTYROW: From stop sign15 (9.6%)↑200.0%prior 5
Lost Control14 (9%)↓-17.6%prior 17
Driving too fast for conditions7 (4.5%)↓-30.0%prior 10
Improper Backing7 (4.5%)↓-36.4%prior 11
Ran off road - left6 (3.8%)↑20.0%prior 5
Other (explain in narrative): Other6 (3.8%)
Followed too close5 (3.2%)↓-61.5%prior 13
FTYROW: At uncontrolled intersection5 (3.2%)
Ran Stop Sign5 (3.2%)

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

Road & Environmental Conditions

While the majority of crashes in both years occurred in clear weather and on dry roads, there was a notable decrease in crashes under adverse winter conditions. In 2017, there were 6 crashes attributed to snow as a weather condition, down from 15 in 2016. Similarly, the number of crashes occurring on road surfaces with snow or ice dropped from 27 in 2016 to 15 in 2017.

Weather

Clear102 (81.6%)
↓-17.1%prior 123
Cloudy10 (8.0%)
↓-33.3%prior 15
Rain5 (4.0%)
Snow4 (3.2%)
↓-69.2%prior 13
Blowing Snow2 (1.6%)
Other (explain in narrative)1 (0.8%)
Fog, smoke, smog1 (0.8%)

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

Lighting

Daylight79 (62.7%)
↓-28.2%prior 110
Dark - roadway not lighted25 (19.8%)
↓-19.4%prior 31
Dark - roadway lighted14 (11.1%)
↑40.0%prior 10
Dawn5 (4.0%)
Dusk3 (2.4%)

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

Road Surface

Dry91 (71.7%)
↓-14.2%prior 106
Wet13 (10.2%)
↑44.4%prior 9
Snow10 (7.9%)
↓-37.5%prior 16
Gravel7 (5.5%)
↓-58.8%prior 17
Ice/frost5 (3.9%)
↓-37.5%prior 8
Mud, dirt1 (0.8%)

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 Ford, Chevrolet, and Dodge being the most common in both 2016 and 2017, though their total counts decreased in line with the overall trend. An analysis of persons involved shows a demographic shift; while the total number of people in crashes decreased, the number of individuals aged 16-20 increased from 39 to 42. This resulted in their share of all persons involved growing from 12.1% in 2016 to 15.7% in 2017.

Top Vehicle Makes (228 vehicles)

1
FORD39 (17.1%)
↓-38.1%prior 63
2
CHEV32 (14%)
↑3.2%prior 31
3
CHEVROLET17 (7.5%)
↓-60.5%prior 43
4
DODG10 (4.4%)
↓-16.7%prior 12
5
BUICK9 (3.9%)
↑12.5%prior 8
6
BUIC8 (3.5%)
↓-20.0%prior 10
7
DODGE8 (3.5%)
↓-20.0%prior 10
8
GMC7 (3.1%)
↓-58.8%prior 17
9
HONDA7 (3.1%)
10
CHRYSLER6 (2.6%)
↓-14.3%prior 7

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

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

Sex Distribution (168 persons with recorded sex)

Male105 (62.5%)
↓-7.1%prior 113
Female63 (37.5%)
↓-31.5%prior 92

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: 156
  • Total persons involved: 267
  • Total vehicles involved: 228

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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