Yearly Traffic Safety Analysis

195 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Cherokee County recorded 195 total crashes, a 14.0% increase from the 171 crashes documented in 2015. While total fatalities decreased from two to one and injuries fell from 78 to 71, the number of single-vehicle crashes rose from 78 in the prior year to 105 in the current period. The leading contributing factor in both years was collisions involving an animal.

195

▲ 14.0%was 171

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

71

▼ -9.0%was 78

Persons Injured

1

▼ -50.0%was 2

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 · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Cherokee County showed an overall increase in volume from 2015 to 2016. Total crashes rose by 14.0%, from 171 to 195. Despite the rise in crash events, the human toll saw a decrease, with total injuries declining by 9.0% from 78 to 71 and fatalities halving from two to one.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 2↓-50.0%

2

Pedestrians Injured

Prior: 4↓-50.0%

69

Motorists Injured

Prior: 72↓-4.2%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained broadly consistent year-over-year. Friday was the peak day for crashes in both 2016 (40 crashes) and 2015 (31 crashes). The peak hour for collisions shifted slightly later, from 5 p.m. in 2015 (20 crashes) to 6 p.m. in 2016 (23 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased from 2015 to 2016. The number of fatal crashes was halved from two to one, causing the fatal crash rate to drop from 1.17% to 0.51%. While the absolute number of injury-related crashes was similar (53 in 2016 vs. 51 in 2015), their proportion of all crashes decreased from 29.8% to 27.2%. Correspondingly, the share of no-injury crashes increased from 69.0% to 72.3% of all incidents.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
↓-50.0%prior 2
Serious Injury9serious injury crashes4.6%
↑28.6%prior 7
Minor Injury23minor injury crashes11.8%
↓-4.2%prior 24
Possible Injury21possible injury crashes10.8%
↑5.0%prior 20
No Injury141no injury crashes72.3%
↑19.5%prior 118

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the leading contributing factor in both periods, with the count increasing from 41 in 2015 to 48 in 2016. 'Lost Control' rose in rank to become the second-most cited factor, increasing from 12 to 17 incidents. Conversely, crashes attributed to 'Driving too fast for conditions' saw a notable decrease, falling from 15 incidents in 2015 to 10 in 2016. 'Followed too close' remained a consistent issue, with 13 crashes in 2016 compared to 14 in the prior year.

Officer-Reported Primary Contributing Cause

Animal48 (24.6%)↑17.1%prior 41
Lost Control17 (8.7%)↑41.7%prior 12
Followed too close13 (6.7%)↓-7.1%prior 14
Ran off road - straight12 (6.2%)↑50.0%prior 8
Improper Backing11 (5.6%)
Driving too fast for conditions10 (5.1%)↓-33.3%prior 15
Ran off road - left5 (2.6%)→0.0%prior 5
FTYROW: From stop sign5 (2.6%)
Other (explain in narrative): Other4 (2.1%)
Driver Distraction: Reaching for object(s)/fallen object(s)4 (2.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in clear weather and on dry road surfaces remained relatively stable between 2015 and 2016. Crashes in daylight conditions constituted a larger share of the total in 2016, rising to 56.4% from 50.9% in the previous year. Incidents during snowy weather saw an increase in count from 8 to 13, representing a slight rise in their overall share of crashes.

Weather

Clear123 (77.4%)
↑12.8%prior 109
Cloudy15 (9.4%)
↑15.4%prior 13
Snow13 (8.2%)
↑62.5%prior 8
Fog, smoke, smog3 (1.9%)
Blowing Snow2 (1.3%)
Severe Winds1 (0.6%)
Sleet, hail1 (0.6%)
Freezing rain/drizzle1 (0.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight110 (69.2%)
↑26.4%prior 87
Dark - roadway not lighted31 (19.5%)
↑14.8%prior 27
Dark - roadway lighted10 (6.3%)
↑25.0%prior 8
Dusk4 (2.5%)
↓-50.0%prior 8
Dawn3 (1.9%)
↓-57.1%prior 7
Dark - unknown roadway lighting1 (0.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry106 (66.7%)
↑11.6%prior 95
Gravel17 (10.7%)
↑54.5%prior 11
Snow16 (10.1%)
↑45.5%prior 11
Wet9 (5.7%)
→0.0%prior 9
Ice/frost8 (5.0%)
↓-11.1%prior 9
Slush3 (1.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Chevrolet and Ford leading in both 2015 and 2016. An analysis of persons involved shows significant shifts among age groups. The number of individuals in the 26-34 age group decreased from 65 to 38, and the 16-20 age group saw a reduction from 54 to 39. Conversely, there was a notable increase in the involvement of the 45-54 age group, which grew from 28 to 45 persons, and the 65+ age group, which increased from 39 to 47 persons.

Top Vehicle Makes (286 vehicles)

1
FORD63 (22%)
↑46.5%prior 43
2
CHEVROLET43 (15%)
↑186.7%prior 15
3
CHEV31 (10.8%)
↓-36.7%prior 49
4
GMC17 (5.9%)
↑6.3%prior 16
5
DODG12 (4.2%)
→0.0%prior 12
6
DODGE10 (3.5%)
↓-9.1%prior 11
7
PONT10 (3.5%)
↑42.9%prior 7
8
BUIC10 (3.5%)
→0.0%prior 10
9
BUICK8 (2.8%)
↑60.0%prior 5
10
CHRYSLER7 (2.4%)
↑40.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (205 persons with recorded sex)

Male113 (55.1%)
↓-11.0%prior 127
Female92 (44.9%)
↓-14.0%prior 107

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 195
  • Total persons involved: 321
  • Total vehicles involved: 286

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: 2016." Published September 9, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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