Yearly Traffic Safety Analysis

94 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Hancock County, total crashes decreased from 115 in 2015 to 94 in 2016, an 18.3% reduction. The most significant year-over-year change was the elimination of traffic fatalities, which dropped from 2 in the prior period to 0 in the current period. Overall injuries also saw a substantial decline, falling from 76 to 50.

94

▼ -18.3%was 115

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

50

▼ -34.2%was 76

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

Traffic crashes in Hancock County showed a downward trend year-over-year, with total incidents falling by 18.3% from 115 in 2015 to 94 in 2016. This decrease was accompanied by a 34.2% reduction in total injuries, from 76 to 50. Notably, fatal crashes were eliminated, dropping from 2 in the prior year to 0 in the current year.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 2↓-100.0%

1

Cyclists Injured

Prior: 0→%

49

Motorists Injured

Prior: 76↓-35.5%

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 showed some consistency year-over-year, with the afternoon remaining a key period for collisions. The 3 p.m. hour was a peak time in both 2015 (14 crashes) and 2016 (10 crashes). While Saturday was a peak day in both periods (20 crashes in 2015 and 17 in 2016), the crash distribution across the week was more even in 2016, with Wednesday, Thursday, and Friday each recording 14 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 significantly from 2015 to 2016. Fatal crashes were eliminated, falling from 2 incidents (1.7% of total) in the prior year to 0 in the current year. The number of serious injury crashes also declined from 10 to 6. Consequently, the proportion of crashes resulting in no injuries increased from 50.4% of all crashes in 2015 to 56.4% in 2016.

Outcome by Severity (Crash Events)

Serious Injury6serious injury crashes6.4%
↓-40.0%prior 10
Minor Injury16minor injury crashes17%
↓-27.3%prior 22
Possible Injury19possible injury crashes20.2%
↓-17.4%prior 23
No Injury53no injury crashes56.4%
↓-8.6%prior 58

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

The leading contributing factor in both periods was 'Driving too fast for conditions,' though its count decreased from 22 crashes in 2015 to 16 in 2016. 'Lost Control' incidents increased slightly from 13 to 15, becoming the second most common factor in 2016. Notably, crashes attributed to 'Ran Stop Sign' saw a significant reduction, falling from 10 incidents in the prior year to just 3 in the current year.

Officer-Reported Primary Contributing Cause

Driving too fast for conditions16 (17%)↓-27.3%prior 22
Lost Control15 (16%)↑15.4%prior 13
Ran off road - straight6 (6.4%)↓-53.8%prior 13
FTYROW: From stop sign6 (6.4%)↑20.0%prior 5
Animal4 (4.3%)
Followed too close3 (3.2%)
Ran Stop Sign3 (3.2%)↓-70.0%prior 10
Operating vehicle in an reckless, erratic, careless, negligent manner3 (3.2%)
FTYROW: At uncontrolled intersection3 (3.2%)
Driver Distraction: Exterior distraction3 (3.2%)

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 in both periods predominantly occurred in clear weather and during daylight hours. The proportion of crashes under these favorable conditions was slightly higher in 2016, with 61.7% of crashes in clear weather compared to 56.5% in 2015. Incidents on adverse road surfaces like ice or snow decreased, with crashes on icy roads falling from 19 to 12 and crashes in snowy conditions dropping from 11 to 5.

Weather

Clear58 (64.4%)
↓-10.8%prior 65
Cloudy17 (18.9%)
↑6.3%prior 16
Snow5 (5.6%)
↓-54.5%prior 11
Rain4 (4.4%)
↓-33.3%prior 6
Fog, smoke, smog2 (2.2%)
Blowing Snow2 (2.2%)
Freezing rain/drizzle1 (1.1%)
↓-80.0%prior 5
Sleet, hail1 (1.1%)

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

Lighting

Daylight62 (68.1%)
↓-11.4%prior 70
Dark - roadway not lighted16 (17.6%)
↓-42.9%prior 28
Dark - roadway lighted8 (8.8%)
↑14.3%prior 7
Dark - unknown roadway lighting2 (2.2%)
Dusk2 (2.2%)
Dawn1 (1.1%)

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

Road Surface

Dry54 (59.3%)
↓-15.6%prior 64
Ice/frost12 (13.2%)
↓-36.8%prior 19
Wet9 (9.9%)
↑28.6%prior 7
Snow8 (8.8%)
↓-42.9%prior 14
Gravel7 (7.7%)
↑40.0%prior 5
Slush1 (1.1%)

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

Vehicles & Demographics

Ford and Chevrolet remained the two most common vehicle makes involved in crashes across both years; Chevrolet's count decreased from 49 to 38 vehicles, while Ford's increased from 30 to 37. A notable shift occurred in the age distribution of persons involved in collisions. While the 26-34 age group had the highest involvement in 2015 with 49 individuals, this number fell to 13 in 2016. The 16-20 age group was the most represented group in 2016, with 35 individuals involved.

Top Vehicle Makes (156 vehicles)

1
FORD37 (23.7%)
↑23.3%prior 30
2
CHEV20 (12.8%)
↓-31.0%prior 29
3
CHEVROLET18 (11.5%)
↓-10.0%prior 20
4
DODGE10 (6.4%)
5
GMC5 (3.2%)
↓-28.6%prior 7
6
TOYOTA5 (3.2%)
7
PONTIAC5 (3.2%)
→0.0%prior 5
8
BUICK4 (2.6%)
9
FREIGHTLINER4 (2.6%)
↓-20.0%prior 5
10
PETERBILT3 (1.9%)

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

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

Sex Distribution (112 persons with recorded sex)

Male82 (73.2%)
↓-26.1%prior 111
Female30 (26.8%)
↓-48.3%prior 58

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: 94
  • Total persons involved: 174
  • Total vehicles involved: 156

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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