Yearly Traffic Safety Analysis

104 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Ida County recorded 104 total crashes, a 10.3% decrease from the 116 crashes in 2015. While total crashes and injuries (down from 42 to 30) declined, the number of fatalities remained unchanged at one. The most notable year-over-year shift was in crash severity, with a significant decrease in serious injury crashes from 5 to 1.

104

▼ -10.3%was 116

Total Crash Events

1

Persons Killed

30

▼ -28.6%was 42

Persons Injured

1

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

Overall traffic safety trends in Ida County showed improvement from 2015 to 2016. The total number of crashes fell from 116 to 104, and the number of people injured decreased from 42 to 30. The number of fatalities held steady, with one person killed in a crash in both years.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 1→0.0%

30

Motorists Injured

Prior: 42↓-28.6%

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 Saturday with 21 incidents, a change from Thursday in 2015, which also had 21 incidents. The peak hour for crashes also moved from 11 a.m. in 2015 (10 crashes) to 3 p.m. in 2016 (10 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

The distribution of crash severity changed year-over-year, even as the number of fatal crashes remained constant at one. There was a significant decrease in crashes resulting in serious injuries, which fell from 5 in 2015 to 1 in 2016. Conversely, crashes involving minor injuries increased from 6 to 11, and those with possible injuries rose from 9 to 13.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1%
→0.0%prior 1
Serious Injury1serious injury crashes1%
↓-80.0%prior 5
Minor Injury11minor injury crashes10.6%
↑83.3%prior 6
Possible Injury13possible injury crashes12.5%
↑44.4%prior 9
No Injury78no injury crashes75%
↓-17.9%prior 95

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 factors remained consistent, though their counts changed. Crashes involving animals were the top factor in both years but decreased in count from 30 in 2015 to 24 in 2016. The second-most common factor, 'Driving too fast for conditions,' also saw a slight decrease from 12 crashes to 11. Crashes attributed to 'Lost Control' dropped from 7 to 3, while those related to 'Driver Distraction: Other interior distraction' increased from 2 to 5.

Officer-Reported Primary Contributing Cause

Animal24 (23.1%)↓-20.0%prior 30
Driving too fast for conditions11 (10.6%)↓-8.3%prior 12
Other (explain in narrative): Other6 (5.8%)↓-14.3%prior 7
Followed too close5 (4.8%)
Driver Distraction: Other interior distraction5 (4.8%)
Ran off road - left5 (4.8%)↓-16.7%prior 6
Ran off road - straight4 (3.8%)↓-33.3%prior 6
FTYROW: From driveway4 (3.8%)
FTYROW: At uncontrolled intersection3 (2.9%)
FTYROW: From stop sign3 (2.9%)↓-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

While clear weather and dry roads were the most common conditions in both years, there was a notable shift in crashes occurring on adverse road surfaces. Crashes on snow-covered roads decreased from 17 in 2015 to 12 in 2016. However, crashes on roads with ice or frost saw a substantial increase, rising from 2 incidents in 2015 to 13 in 2016.

Weather

Clear54 (67.5%)
↓-1.8%prior 55
Cloudy9 (11.3%)
↑28.6%prior 7
Snow7 (8.8%)
↓-50.0%prior 14
Blowing Snow5 (6.3%)
Rain2 (2.5%)
↓-66.7%prior 6
Severe Winds1 (1.3%)
Sleet, hail1 (1.3%)
Freezing rain/drizzle1 (1.3%)

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

Lighting

Daylight57 (71.3%)
↓-12.3%prior 65
Dark - roadway not lighted11 (13.8%)
↓-26.7%prior 15
Dark - roadway lighted5 (6.3%)
Dusk3 (3.8%)
Dawn2 (2.5%)
Dark - unknown roadway lighting2 (2.5%)

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

Road Surface

Dry45 (56.3%)
↓-6.3%prior 48
Ice/frost13 (16.3%)
Snow12 (15.0%)
↓-29.4%prior 17
Gravel6 (7.5%)
↑20.0%prior 5
Wet3 (3.8%)
↓-57.1%prior 7
Slush1 (1.3%)
↓-83.3%prior 6

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, primarily Ford and Chevrolet, remained stable between 2015 and 2016. However, the age distribution of persons involved in crashes shifted. The number of people aged 65 and older involved in crashes decreased significantly from 29 to 15. In contrast, there were increases in the number of people involved from the 21-25 age group (from 14 to 20) and the 55-64 age group (from 19 to 25).

Top Vehicle Makes (153 vehicles)

1
FORD37 (24.2%)
↑32.1%prior 28
2
CHEVROLET18 (11.8%)
↑38.5%prior 13
3
CHEV15 (9.8%)
↓-25.0%prior 20
4
DODGE8 (5.2%)
↑33.3%prior 6
5
JEEP5 (3.3%)
6
GMC5 (3.3%)
7
PONTIAC4 (2.6%)
8
BUIC4 (2.6%)
↓-42.9%prior 7
9
LINCOLN4 (2.6%)
10
PETERBILT3 (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 (109 persons with recorded sex)

Male71 (65.1%)
↓-9.0%prior 78
Female38 (34.9%)
↓-39.7%prior 63

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: 104
  • Total persons involved: 173
  • Total vehicles involved: 153

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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