Yearly Traffic Safety Analysis

154 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Sac County recorded 154 vehicle crashes, a 23.2% increase from the 125 crashes reported in 2015. The most significant year-over-year change was the increase in crash severity, with total fatalities rising from one in 2015 to four in 2016, and total injuries increasing by 63.3% from 49 to 80.

154

▲ 23.2%was 125

Total Crash Events

4

▲ 300.0%was 1

Persons Killed

80

▲ 63.3%was 49

Persons Injured

3

▲ 200.0%was 1

Fatal Crash Events

Note: "Persons Killed" (4) 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

Crash trends in Sac County showed a notable increase from 2015 to 2016. Total crashes rose by 23.2%, from 125 to 154 incidents. This upward trend was also reflected in crash outcomes, with total injuries increasing from 49 to 80 and fatalities rising from one to four.

Vulnerable Road User Casualties

4

Motorists Killed

Prior: 1↑300.0%

80

Motorists Injured

Prior: 49↑63.3%

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 2015 and 2016. The peak day for crashes moved from Friday (23 crashes) in 2015 to Saturday (30 crashes) in 2016. The peak hour also changed, shifting from a four-way tie in the afternoon and evening in 2015 (10 crashes each at 1 p.m., 2 p.m., 3 p.m., and 6 p.m.) to the 8 a.m. hour in 2016, which saw 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 worsened in 2016 compared to the prior year. The number of fatal crashes increased from one to three, and the fatal crash rate more than doubled from 0.8 to 1.95 per 100 crashes. While the count of serious injury crashes remained stable at six incidents in both years, the count of minor injury crashes rose from 18 to 29. Overall, the proportion of crashes resulting in any level of injury increased from 31.2% in 2015 to 35.1% in 2016.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.9%
↑200.0%prior 1
Serious Injury6serious injury crashes3.9%
→0.0%prior 6
Minor Injury29minor injury crashes18.8%
↑61.1%prior 18
Possible Injury19possible injury crashes12.3%
↑26.7%prior 15
No Injury97no injury crashes63%
↑14.1%prior 85

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 periods, accounting for 32 crashes in 2016 and 32 in 2015; however, its share of all crashes decreased from 25.6% to 20.8%. The count of crashes attributed to 'Lost Control' nearly doubled, rising from 11 incidents in 2015 to 21 in 2016, a 90.9% increase in count. Similarly, incidents of 'Ran off road - straight' increased from 7 to 12, and 'Ran Stop Sign' incidents increased from one to six year-over-year.

Officer-Reported Primary Contributing Cause

Animal32 (20.8%)→0.0%prior 32
Lost Control21 (13.6%)↑90.9%prior 11
Ran off road - straight12 (7.8%)↑71.4%prior 7
FTYROW: From stop sign9 (5.8%)↑28.6%prior 7
Other (explain in narrative): Other8 (5.2%)↓-11.1%prior 9
Driving too fast for conditions8 (5.2%)↑33.3%prior 6
Ran off road - left7 (4.5%)
Ran Stop Sign6 (3.9%)
Followed too close5 (3.2%)↓-16.7%prior 6
Crossed centerline (undivided)4 (2.6%)

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

Road & Environmental Conditions

While the majority of crashes in both years occurred in daylight on dry roads, there was a significant increase in collisions under dark, unlit conditions, which doubled from 17 in 2015 to 34 in 2016. The proportion of crashes in clear weather decreased from 56.8% to 51.3%, while the count of crashes on wet roads rose from 10 to 15. The number of crashes on roads with ice or frost remained unchanged at 11 incidents in both years.

Weather

Clear79 (62.2%)
↑11.3%prior 71
Cloudy21 (16.5%)
↑75.0%prior 12
Snow8 (6.3%)
↑60.0%prior 5
Rain5 (3.9%)
↓-37.5%prior 8
Fog, smoke, smog4 (3.1%)
Freezing rain/drizzle3 (2.4%)
Blowing Snow2 (1.6%)
Severe Winds2 (1.6%)
Sleet, hail2 (1.6%)
Other (explain in narrative)1 (0.8%)

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

Lighting

Daylight84 (65.1%)
↑20.0%prior 70
Dark - roadway not lighted34 (26.4%)
↑100.0%prior 17
Dark - roadway lighted7 (5.4%)
↑40.0%prior 5
Dawn2 (1.6%)
↓-66.7%prior 6
Dusk2 (1.6%)

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

Road Surface

Dry80 (62.5%)
↑27.0%prior 63
Wet15 (11.7%)
↑50.0%prior 10
Gravel11 (8.6%)
↑57.1%prior 7
Ice/frost11 (8.6%)
→0.0%prior 11
Snow9 (7.0%)
↑80.0%prior 5
Slush1 (0.8%)
Mud, dirt1 (0.8%)

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 most common vehicle makes involved in crashes in both 2015 and 2016, with their counts increasing in line with the overall rise in collisions. An analysis of persons involved in crashes shows increases across most age demographics, most notably a rise from 28 to 42 individuals in the 55-64 age group. In a significant demographic shift, the number of females involved in crashes decreased from 82 in 2015 to 63 in 2016, while the number of males involved saw a slight increase from 91 to 97.

Top Vehicle Makes (226 vehicles)

1
FORD36 (15.9%)
↑12.5%prior 32
2
CHEVROLET30 (13.3%)
↑87.5%prior 16
3
CHEV23 (10.2%)
→0.0%prior 23
4
GMC8 (3.5%)
↓-50.0%prior 16
5
TOYOTA8 (3.5%)
6
TOYT7 (3.1%)
7
BUIC7 (3.1%)
↓-22.2%prior 9
8
VOLVO7 (3.1%)
9
JEEP7 (3.1%)
→0.0%prior 7
10
DODGE6 (2.7%)
↓-14.3%prior 7

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

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

Sex Distribution (160 persons with recorded sex)

Male97 (60.6%)
↑6.6%prior 91
Female63 (39.4%)
↓-23.2%prior 82

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: 154
  • Total persons involved: 275
  • Total vehicles involved: 226

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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