Yearly Traffic Safety Analysis

152 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Humboldt County recorded 152 total crashes, a slight increase from the 150 crashes reported in 2015. The most significant year-over-year change was the increase in crash severity, with fatalities rising from zero in the prior period to three in the current period. Correspondingly, the total number of people injured more than doubled, increasing from 33 to 74.

152

▲ 1.3%was 150

Total Crash Events

3

Persons Killed

74

▲ 124.2%was 33

Persons Injured

3

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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

The total number of crashes in Humboldt County remained relatively stable, increasing by 1.3% from 150 in 2015 to 152 in 2016. However, the outcomes of these crashes worsened significantly. The number of people injured increased by 124.2%, from 33 to 74, and the county recorded three fatalities in 2016 after having none in the previous year.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

73

Motorists Injured

Prior: 33↑121.2%

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 timing of crashes showed some shifts between the two periods. The peak day for collisions moved from Thursday (28 crashes) in 2015 to Monday (27 crashes) in 2016. While the 3 p.m. hour remained the peak time for crashes in both years, the volume of crashes during this hour decreased from 19 to 12. October was the month with the most crashes in 2016, compared to February and November in the prior year.

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 increased markedly in 2016 compared to the previous year. The county recorded three fatal crashes, accounting for 2% of all collisions, whereas there were no fatal crashes in 2015. The proportion of crashes resulting in an injury rose from 18.6% in 2015 to 34.2% in 2016, driven by the emergence of seven serious injury crashes and an increase in minor injury crashes from 11 to 24. Consequently, the share of no-injury crashes decreased from 81.3% to 63.8% of the total.

Outcome by Severity (Crash Events)

Fatal3fatal crashes2%
Serious Injury7serious injury crashes4.6%
Minor Injury24minor injury crashes15.8%
↑118.2%prior 11
Possible Injury21possible injury crashes13.8%
↑23.5%prior 17
No Injury97no injury crashes63.8%
↓-20.5%prior 122

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 an animal remained a primary contributing factor in both years, though the count decreased by 32.6% from 43 incidents in 2015 to 29 in 2016. Conversely, crashes attributed to a driver losing control increased by 75%, from 12 to 21 incidents, making it the second-most cited factor in 2016. Crashes related to failure to yield the right of way while making a left turn also saw a notable increase, rising from one crash in 2015 to six in 2016.

Officer-Reported Primary Contributing Cause

Animal29 (19.1%)↓-32.6%prior 43
Lost Control21 (13.8%)↑75.0%prior 12
Other (explain in narrative): Other16 (10.5%)↑77.8%prior 9
Ran off road - straight11 (7.2%)↑10.0%prior 10
Ran Stop Sign8 (5.3%)↑33.3%prior 6
Driver Distraction: Other interior distraction7 (4.6%)↑40.0%prior 5
FTYROW: Making left turn6 (3.9%)
Ran off road - left6 (3.9%)↓-25.0%prior 8
FTYROW: From stop sign6 (3.9%)→0.0%prior 6
Driving too fast for conditions5 (3.3%)↓-37.5%prior 8

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

Road & Environmental Conditions

Crashes were more likely to occur in clear weather in 2016, with 80 such incidents compared to 69 in 2015. Correspondingly, collisions during adverse weather conditions like snow and rain decreased from a combined 27 incidents in 2015 to 15 in 2016. The proportion of crashes happening in daylight increased from 60.7% to 64.5%. Despite fewer crashes in inclement weather, incidents on adverse road surfaces such as snow, ice, or wet pavement increased from 36 to 42 year-over-year.

Weather

Clear80 (62.0%)
↑15.9%prior 69
Cloudy30 (23.3%)
↑36.4%prior 22
Snow7 (5.4%)
↓-46.2%prior 13
Rain4 (3.1%)
↓-50.0%prior 8
Blowing Snow3 (2.3%)
Severe Winds3 (2.3%)
Fog, smoke, smog1 (0.8%)
Freezing rain/drizzle1 (0.8%)

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

Lighting

Daylight98 (75.4%)
↑7.7%prior 91
Dark - roadway not lighted19 (14.6%)
↓-5.0%prior 20
Dark - roadway lighted7 (5.4%)
Dawn5 (3.8%)
Dusk1 (0.8%)

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

Road Surface

Dry86 (66.2%)
↑6.2%prior 81
Snow14 (10.8%)
↑55.6%prior 9
Ice/frost14 (10.8%)
↓-12.5%prior 16
Wet12 (9.2%)
↑20.0%prior 10
Slush2 (1.5%)
Other (explain in narrative)1 (0.8%)
Gravel1 (0.8%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Chevrolet and Ford being the most common in both periods. The number of Chevrolets in collisions decreased from 70 in 2015 to 58 in 2016, while Fords saw a smaller drop from 39 to 36. An analysis of persons involved shows a relatively stable age distribution, with the most notable shifts being an increase in the 35-44 age group (from 33 to 41 people) and a decrease in the 65+ age group (from 47 to 40 people).

Top Vehicle Makes (231 vehicles)

1
FORD36 (15.6%)
↓-7.7%prior 39
2
CHEVROLET36 (15.6%)
↑20.0%prior 30
3
CHEV22 (9.5%)
↓-45.0%prior 40
4
GMC13 (5.6%)
↑85.7%prior 7
5
DODGE11 (4.8%)
→0.0%prior 11
6
TOYOTA10 (4.3%)
↑66.7%prior 6
7
DODG9 (3.9%)
↓-30.8%prior 13
8
JEEP9 (3.9%)
9
PONTIAC6 (2.6%)
↓-25.0%prior 8
10
TOYT6 (2.6%)
↑20.0%prior 5

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

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

Sex Distribution (169 persons with recorded sex)

Male108 (63.9%)
↓-12.9%prior 124
Female61 (36.1%)
↓-28.2%prior 85

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: 152
  • Total persons involved: 274
  • Total vehicles involved: 231

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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