Yearly Traffic Safety Analysis

186 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Allamakee County, total traffic crashes remained stable, with 186 incidents in 2016 compared to 187 in 2015, a change of less than 1%. While total crashes and fatalities (3 in each year) were consistent, the number of injuries rose by 7.6% from 66 to 71. A notable shift was the 62.5% increase in crashes involving a driver under the influence (DUI), which grew from 8 to 13 incidents year-over-year.

186

▼ -0.5%was 187

Total Crash Events

3

Persons Killed

71

▲ 7.6%was 66

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

Overall crash trends in Allamakee County were stable between 2015 and 2016, with total crashes decreasing by a single incident from 187 to 186. Fatalities remained unchanged at 3 for both years. However, the number of persons injured in crashes increased by 7.6%, rising from 66 in 2015 to 71 in 2016.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 3→0.0%

1

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

69

Motorists Injured

Prior: 66↑4.5%

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 timing of crashes shifted significantly between the two periods. The most frequent day for crashes moved from Friday (35 crashes) in 2015 to Sunday (42 crashes) in 2016. Similarly, the peak hour for incidents changed from the evening at 7 p.m. (15 crashes) in the prior year to the morning commute at 7 a.m. (14 crashes) in the current 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

Fatal crash counts were identical with 3 incidents in both 2015 and 2016. However, the distribution of injury severity changed, with serious injury crashes decreasing by 50% from 10 to 5 incidents. This was offset by an increase in less severe collisions, as minor injury crashes rose from 18 to 24 and possible injury crashes increased from 17 to 25.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.6%
→0.0%prior 3
Serious Injury5serious injury crashes2.7%
↓-50.0%prior 10
Minor Injury24minor injury crashes12.9%
↑33.3%prior 18
Possible Injury25possible injury crashes13.4%
↑47.1%prior 17
No Injury129no injury crashes69.4%
↓-7.2%prior 139

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 leading contributing factor in both years, with the count of such incidents increasing by 19.2% from 52 in 2015 to 62 in 2016. The second most common factor, 'Lost Control,' saw a 25% decrease in count, falling from 28 crashes to 21. Another significant change was a 61.5% drop in crashes attributed to 'Driving too fast for conditions,' which fell from 13 to 5 incidents.

Officer-Reported Primary Contributing Cause

Animal62 (33.3%)↑19.2%prior 52
Lost Control21 (11.3%)↓-25.0%prior 28
Ran off road - straight18 (9.7%)↑28.6%prior 14
Ran off road - left9 (4.8%)↓-10.0%prior 10
Exceeded authorized speed7 (3.8%)
FTYROW: From stop sign7 (3.8%)↓-22.2%prior 9
Driving too fast for conditions5 (2.7%)↓-61.5%prior 13
Other (explain in narrative): Other5 (2.7%)
Ran Stop Sign4 (2.2%)
Swerving/Evasive Action3 (1.6%)

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

Road & Environmental Conditions

Year-over-year, there was a shift in the conditions under which crashes occurred. Crashes on dry roads remained the most common but decreased slightly from 113 to 108. Notably, incidents on adverse surfaces saw a significant reduction, with crashes on ice or frost falling from 19 to 6 and those on snow decreasing from 16 to 12. Conversely, crashes on gravel roads increased from 6 in 2015 to 17 in 2016.

Weather

Clear120 (75.5%)
↑15.4%prior 104
Cloudy19 (11.9%)
↓-40.6%prior 32
Snow7 (4.4%)
↓-41.7%prior 12
Rain6 (3.8%)
↓-14.3%prior 7
Fog, smoke, smog3 (1.9%)
↓-40.0%prior 5
Freezing rain/drizzle2 (1.3%)
Blowing Snow2 (1.3%)

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

Lighting

Daylight81 (50.6%)
↓-15.6%prior 96
Dark - roadway not lighted52 (32.5%)
↑6.1%prior 49
Dusk14 (8.8%)
↑180.0%prior 5
Dark - roadway lighted8 (5.0%)
↓-11.1%prior 9
Dawn5 (3.1%)
↓-28.6%prior 7

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

Road Surface

Dry108 (67.9%)
↓-4.4%prior 113
Gravel17 (10.7%)
↑183.3%prior 6
Wet14 (8.8%)
↑7.7%prior 13
Snow12 (7.5%)
↓-25.0%prior 16
Ice/frost6 (3.8%)
↓-68.4%prior 19
Other (explain in narrative)1 (0.6%)
Slush1 (0.6%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, Chevrolet and Ford, maintained their top rankings from the prior year, though their counts decreased slightly. Demographically, the number of persons aged 16-20 involved in crashes was unchanged at 56. However, involvements for older age groups decreased, with the 65+ group falling from 48 to 36 persons, and both the 26-34 and 55-64 age groups declining from 47 to 37 persons.

Top Vehicle Makes (232 vehicles)

1
CHEVROLET42 (18.1%)
↑2.4%prior 41
2
FORD32 (13.8%)
↓-15.8%prior 38
3
CHEV23 (9.9%)
↓-32.4%prior 34
4
DODGE13 (5.6%)
↓-7.1%prior 14
5
DODG13 (5.6%)
↓-7.1%prior 14
6
JEEP9 (3.9%)
↑50.0%prior 6
7
PONTIAC9 (3.9%)
8
PONT9 (3.9%)
↑80.0%prior 5
9
BUICK6 (2.6%)
↑20.0%prior 5
10
MERCURY5 (2.2%)

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

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

Sex Distribution (176 persons with recorded sex)

Male112 (63.6%)
↓-16.4%prior 134
Female64 (36.4%)
↓-34.0%prior 97

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: 186
  • Total persons involved: 280
  • Total vehicles involved: 232

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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