Yearly Traffic Safety Analysis

470 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Sioux County, total traffic crashes increased slightly from 459 in 2015 to 470 in 2016, a change of 2.4%. During this period, the number of fatalities remained stable at 3, while total injuries rose from 207 to 213. A notable shift occurred in the location of crashes, with the top city, Sioux Center, seeing a significant decrease in incidents, while other cities like Rock Valley and Hawarden experienced increases.

470

▲ 2.4%was 459

Total Crash Events

3

Persons Killed

213

▲ 2.9%was 207

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 overall trend in Sioux County shows a minor increase in traffic incidents year-over-year. Total crashes rose by 2.4%, from 459 to 470. Similarly, the number of people injured in these crashes increased by 2.9%, from 207 in 2015 to 213 in 2016, while fatalities held steady at 3 for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 2↑50.0%

5

Pedestrians Injured

Prior: 2↑150.0%

1

Cyclists Injured

Prior: 3↓-66.7%

207

Motorists Injured

Prior: 202↑2.5%

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 between the two periods. In 2016, the peak day for crashes was Friday with 88 incidents, a change from 2015 when Thursday was the peak day with 91 crashes. The busiest hour also shifted slightly later, from the 3 p.m. hour in 2015 (39 crashes) to the 4 p.m. hour in 2016 (40 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

While the number of fatal crashes was unchanged at 3 in both 2015 and 2016, the distribution of injury severity shifted. The proportion of serious injury crashes decreased from 3.7% to 2.8% of all crashes, and minor injury crashes fell from 14.6% to 12.6%. Conversely, crashes resulting in possible injuries saw a notable increase, rising from 60 incidents (13.1% of total) in 2015 to 75 incidents (16.0% of total) in 2016.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.6%
→0.0%prior 3
Serious Injury13serious injury crashes2.8%
↓-23.5%prior 17
Minor Injury59minor injury crashes12.6%
↓-11.9%prior 67
Possible Injury75possible injury crashes16%
↑25.0%prior 60
No Injury320no injury crashes68.1%
↑2.6%prior 312

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, increasing from 65 incidents in 2015 to 71 in 2016. Crashes attributed to 'Following too close' decreased from 63 to 52, though it remained the second-most cited factor. Incidents involving 'Driving too fast for conditions' increased by 29%, from 31 to 40 crashes, and 'Lost Control' incidents rose from 31 to 39.

Officer-Reported Primary Contributing Cause

Animal71 (15.1%)↑9.2%prior 65
Followed too close52 (11.1%)↓-17.5%prior 63
Driving too fast for conditions40 (8.5%)↑29.0%prior 31
Lost Control39 (8.3%)↑25.8%prior 31
FTYROW: From stop sign37 (7.9%)↑23.3%prior 30
FTYROW: At uncontrolled intersection21 (4.5%)↓-8.7%prior 23
Ran off road - left20 (4.3%)→0.0%prior 20
Ran off road - straight19 (4%)↓-29.6%prior 27
Improper Backing18 (3.8%)↑63.6%prior 11
FTYROW: Making left turn14 (3%)↓-12.5%prior 16

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 data shows a shift in the conditions under which crashes occurred. While crashes on dry roads remained stable, incidents on roads with ice or frost increased from 35 to 47. Correspondingly, crashes during 'Clear' weather decreased from 227 to 214, while those in 'Cloudy' conditions rose from 102 to 136. Crashes attributed to 'Blowing Snow' also saw a notable increase from 2 to 19 incidents.

Weather

Clear214 (51.8%)
↓-5.7%prior 227
Cloudy136 (32.9%)
↑33.3%prior 102
Blowing Snow19 (4.6%)
Snow17 (4.1%)
↓-37.0%prior 27
Rain14 (3.4%)
↓-41.7%prior 24
Freezing rain/drizzle5 (1.2%)
↓-61.5%prior 13
Fog, smoke, smog4 (1.0%)
↓-33.3%prior 6
Severe Winds4 (1.0%)

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

Lighting

Daylight281 (67.9%)
↑1.4%prior 277
Dark - roadway not lighted68 (16.4%)
↑3.0%prior 66
Dark - roadway lighted39 (9.4%)
↑11.4%prior 35
Dawn11 (2.7%)
↑10.0%prior 10
Dusk11 (2.7%)
↓-35.3%prior 17
Dark - unknown roadway lighting4 (1.0%)

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

Road Surface

Dry268 (64.7%)
↑1.1%prior 265
Ice/frost47 (11.4%)
↑34.3%prior 35
Snow37 (8.9%)
↓-15.9%prior 44
Wet33 (8.0%)
↓-10.8%prior 37
Gravel22 (5.3%)
↑15.8%prior 19
Slush5 (1.2%)
Sand1 (0.2%)
Other (explain in narrative)1 (0.2%)

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 remained consistent, with Chevrolet and Ford being the most frequent in both periods. In 2016, 185 Chevrolet and 117 Ford vehicles were involved, compared to 190 and 145, respectively, in 2015. The age demographics of persons involved in crashes also shifted; there was a decrease in the 16-20 age group (from 178 to 148 persons) and the 65+ age group (from 118 to 98 persons), while the 26-34 age group saw an increase from 121 to 131 persons.

Top Vehicle Makes (755 vehicles)

1
FORD117 (15.5%)
↓-19.3%prior 145
2
CHEVROLET95 (12.6%)
↑23.4%prior 77
3
CHEV90 (11.9%)
↓-20.4%prior 113
4
GMC34 (4.5%)
↓-15.0%prior 40
5
DODGE28 (3.7%)
↑64.7%prior 17
6
DODG24 (3.2%)
↓-4.0%prior 25
7
BUICK22 (2.9%)
↑29.4%prior 17
8
PONT19 (2.5%)
↓-26.9%prior 26
9
BUIC19 (2.5%)
↓-17.4%prior 23
10
JEEP18 (2.4%)
↑38.5%prior 13

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

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

Sex Distribution (580 persons with recorded sex)

Male339 (58.4%)
↓-17.3%prior 410
Female241 (41.6%)
↓-6.9%prior 259

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: 470
  • Total persons involved: 885
  • Total vehicles involved: 755

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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