Yearly Traffic Safety Analysis

183 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Jackson County, traffic crashes increased by 20.4% from 152 in 2015 to 183 in 2016. While the number of fatalities remained stable at four, total injuries rose from 76 to 96. The most significant shift was in crashes related to failure to yield from a stop sign, which saw a count increase from 7 in the prior year to 20 in the current year.

183

▲ 20.4%was 152

Total Crash Events

4

Persons Killed

96

▲ 26.3%was 76

Persons Injured

4

▲ 33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 Jackson County showed an upward trend year-over-year. Total collisions increased by 20.4%, rising from 152 in 2015 to 183 in 2016. This was accompanied by a 26.3% increase in total injuries, from 76 to 96, while fatalities remained unchanged at 4.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 4→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

1

Cyclists Injured

Prior: 0→%

94

Motorists Injured

Prior: 75↑25.3%

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 temporal patterns of crashes showed a shift in the peak day of the week, moving from Friday (25 crashes) in 2015 to Saturday (33 crashes) in 2016. The peak hour for crashes remained consistent at the 3 p.m. hour in both periods, with 15 crashes in 2015 and 14 in 2016. Crashes occurring on weekends (Saturday and Sunday) increased from 40 in the prior year to 63 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

Crash severity saw a slight increase in fatal incidents and a notable rise in lower-level injury crashes. The number of fatal crashes increased from 3 to 4, and the fatal crash rate rose from 1.97% to 2.19% year-over-year. While crashes resulting in serious injuries decreased from 10 to 7, the count of crashes with possible injuries increased from 25 to 45. Overall, the proportion of crashes involving any injury increased from 35.5% in 2015 to 38.8% in 2016.

Outcome by Severity (Crash Events)

Fatal4fatal crashes2.2%
↑33.3%prior 3
Serious Injury7serious injury crashes3.8%
↓-30.0%prior 10
Minor Injury19minor injury crashes10.4%
→0.0%prior 19
Possible Injury45possible injury crashes24.6%
↑80.0%prior 25
No Injury108no injury crashes59%
↑13.7%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

Comparing contributing factors, 'Lost Control' remained the most cited factor in both periods, with its count increasing from 28 to 38. A significant change was observed in crashes attributed to 'FTYROW: From stop sign,' which increased in count from 7 to 20, moving it from the sixth-ranked factor in 2015 to the second-ranked in 2016. Conversely, crashes involving 'Ran off road - straight' decreased from 23 to 17.

Officer-Reported Primary Contributing Cause

Lost Control38 (20.8%)↑35.7%prior 28
FTYROW: From stop sign20 (10.9%)↑185.7%prior 7
Ran off road - straight17 (9.3%)↓-26.1%prior 23
Animal12 (6.6%)↑9.1%prior 11
Driving too fast for conditions9 (4.9%)
FTYROW: Making left turn7 (3.8%)↓-12.5%prior 8
Ran Stop Sign7 (3.8%)
Followed too close7 (3.8%)
Other (explain in narrative): Other6 (3.3%)↑20.0%prior 5
Driver Distraction: Other interior distraction5 (2.7%)

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

Road & Environmental Conditions

Crash conditions in 2016 were more frequently associated with clear weather and dry roads compared to the prior year. The proportion of crashes occurring in daylight increased from 56.6% in 2015 to 65.0% in 2016. Similarly, crashes on dry road surfaces increased from 55.9% to 67.2% of all incidents. Crashes on snow-covered roads saw a notable decrease, falling from 16 incidents in 2015 to 9 in 2016.

Weather

Clear114 (65.1%)
↑20.0%prior 95
Cloudy42 (24.0%)
↑82.6%prior 23
Rain9 (5.1%)
↓-18.2%prior 11
Fog, smoke, smog4 (2.3%)
Freezing rain/drizzle3 (1.7%)
Snow3 (1.7%)

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

Lighting

Daylight119 (68.4%)
↑38.4%prior 86
Dark - roadway not lighted28 (16.1%)
↓-30.0%prior 40
Dark - roadway lighted18 (10.3%)
↑80.0%prior 10
Dawn5 (2.9%)
Dusk3 (1.7%)
Dark - unknown roadway lighting1 (0.6%)

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

Road Surface

Dry123 (70.7%)
↑44.7%prior 85
Wet20 (11.5%)
↑66.7%prior 12
Gravel13 (7.5%)
↓-27.8%prior 18
Snow9 (5.2%)
↓-43.8%prior 16
Ice/frost6 (3.4%)
Slush3 (1.7%)

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

Vehicles & Demographics

The distribution of vehicle makes involved in crashes remained consistent, with Ford and Chevrolet vehicles being the most common in both years. In 2016, a combined 110 Chevrolet and Ford vehicles were involved, compared to 105 in 2015. The demographic profile of persons involved in crashes showed little change, with the 16-20 age group being one of the most represented in both periods, accounting for 53 individuals in 2015 and 51 in 2016.

Top Vehicle Makes (292 vehicles)

1
FORD46 (15.8%)
↓-8.0%prior 50
2
CHEV35 (12%)
↑66.7%prior 21
3
CHEVROLET29 (9.9%)
↓-14.7%prior 34
4
DODG14 (4.8%)
↑180.0%prior 5
5
BUICK14 (4.8%)
↑180.0%prior 5
6
JEEP13 (4.5%)
↑85.7%prior 7
7
BUIC13 (4.5%)
↑85.7%prior 7
8
PONT12 (4.1%)
9
DODGE11 (3.8%)
↓-15.4%prior 13
10
GMC8 (2.7%)
↑60.0%prior 5

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

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

Sex Distribution (196 persons with recorded sex)

Male120 (61.2%)
↓-0.8%prior 121
Female76 (38.8%)
↑1.3%prior 75

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: 183
  • Total persons involved: 346
  • Total vehicles involved: 292

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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