Yearly Traffic Safety Analysis

119 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Guthrie County, the total number of crashes remained nearly stable, with 119 incidents in 2016 compared to 120 in 2015. The most significant year-over-year change was a reduction in crash severity; fatalities dropped from two to zero. However, the total number of non-fatal injuries increased by 28.6%, rising from 35 in 2015 to 45 in 2016.

119

▼ -0.8%was 120

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

45

▲ 28.6%was 35

Persons Injured

0

▼ -100.0%was 2

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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

Overall traffic crash volume in Guthrie County was stable year-over-year, decreasing by just one incident from 120 to 119. While the data indicates a positive trend in fatal crash reduction, moving from two fatal crashes to none, the number of persons injured in collisions rose from 35 to 45.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 2↓-100.0%

1

Pedestrians Injured

Prior: 0→%

44

Motorists Injured

Prior: 35↑25.7%

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 timing of crashes shifted between the two periods. In 2016, the peak day for crashes was Sunday with 22 incidents, a change from 2015 when Thursday and Friday were the peak days with 21 crashes each. Similarly, the peak hour for crashes moved from the 5 p.m. hour in 2015 (15 crashes) to the 12 p.m. hour in 2016 (12 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

There was a notable improvement in crash severity, with fatal crashes dropping from two in 2015 to zero in 2016. The number of serious injury crashes also saw a slight decrease from three to two. Conversely, the count of minor injury crashes increased from 11 to 20, and possible injury crashes rose from 14 to 16, indicating a shift towards a higher volume of less severe injury collisions.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes1.7%
↓-33.3%prior 3
Minor Injury20minor injury crashes16.8%
↑81.8%prior 11
Possible Injury16possible injury crashes13.4%
↑14.3%prior 14
No Injury81no injury crashes68.1%
↓-10.0%prior 90

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 with animals remained the top contributing factor in both periods, although the count of such incidents decreased by 25% from 44 in 2015 to 33 in 2016. 'Lost Control' was the second-ranked factor in 2016 with 15 crashes, an increase from 11 crashes in the prior year. Crashes attributed to 'Driving too fast for conditions' decreased slightly from 9 to 8, while 'Ran off road - straight' incidents increased from 6 to 8.

Officer-Reported Primary Contributing Cause

Animal33 (27.7%)↓-25.0%prior 44
Lost Control15 (12.6%)↑36.4%prior 11
Driving too fast for conditions8 (6.7%)↓-11.1%prior 9
Ran off road - straight8 (6.7%)↑33.3%prior 6
Driver Distraction: Other interior distraction4 (3.4%)
FTYROW: From stop sign4 (3.4%)↓-33.3%prior 6
FTYROW: From yield sign4 (3.4%)
Operating vehicle in an reckless, erratic, careless, negligent manner4 (3.4%)
Other (explain in narrative): Other4 (3.4%)
Ran off road - left3 (2.5%)↓-40.0%prior 5

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

Road & Environmental Conditions

A higher proportion of crashes occurred during daylight in 2016, accounting for 68 incidents (57.1% of total) compared to 46 incidents (38.3% of total) in 2015. Correspondingly, crashes in dark, unlighted conditions decreased from 31 to 17. While crashes on dry roads were stable, incidents on adverse surfaces such as ice, frost, or wet pavement increased, with a combined count rising from 11 in 2015 to 20 in 2016.

Weather

Clear58 (64.4%)
↓-9.4%prior 64
Cloudy17 (18.9%)
↑41.7%prior 12
Rain5 (5.6%)
Snow4 (4.4%)
↓-20.0%prior 5
Fog, smoke, smog2 (2.2%)
Freezing rain/drizzle2 (2.2%)
Other (explain in narrative)1 (1.1%)
Blowing Snow1 (1.1%)

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

Lighting

Daylight68 (74.7%)
↑47.8%prior 46
Dark - roadway not lighted17 (18.7%)
↓-45.2%prior 31
Dusk2 (2.2%)
↓-66.7%prior 6
Dark - roadway lighted2 (2.2%)
Dawn1 (1.1%)
Dark - unknown roadway lighting1 (1.1%)

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

Road Surface

Dry59 (65.6%)
↓-3.3%prior 61
Ice/frost9 (10.0%)
Wet8 (8.9%)
Gravel8 (8.9%)
↓-38.5%prior 13
Snow3 (3.3%)
Other (explain in narrative)1 (1.1%)
Sand1 (1.1%)
Mud, dirt1 (1.1%)

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

Vehicles & Demographics

Analysis of vehicles involved shows a significant shift in the top makes; Chevrolet-branded vehicles were involved in 50 crashes in 2016, a notable increase from 26 in 2015. In contrast, Ford vehicle involvements decreased from 41 to 26. Looking at participant demographics, the number of individuals aged 65 and older involved in crashes increased from 16 to 23, while involvement for the 45-54 age group decreased from 24 to 14.

Top Vehicle Makes (161 vehicles)

1
CHEVROLET30 (18.6%)
↑100.0%prior 15
2
FORD26 (16.1%)
↓-36.6%prior 41
3
CHEV20 (12.4%)
↑81.8%prior 11
4
DODGE8 (5%)
→0.0%prior 8
5
PONTIAC5 (3.1%)
6
BUICK4 (2.5%)
7
DEER3 (1.9%)
8
FREIGHTLINER3 (1.9%)
9
HOND3 (1.9%)
10
CHRYSLER3 (1.9%)

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

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

Sex Distribution (120 persons with recorded sex)

Male66 (55.0%)
↓-18.5%prior 81
Female54 (45.0%)
↓-18.2%prior 66

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: 119
  • Total persons involved: 188
  • Total vehicles involved: 161

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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