Yearly Traffic Safety Analysis

306 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Jones County, total traffic crashes decreased by 2.6% from 314 in 2015 to 306 in 2016. While total crashes and fatalities (down from 4 to 3) saw a slight decline, the number of people injured rose from 75 to 82. One of the most significant year-over-year shifts was a 44.4% reduction in crashes involving driving under the influence, which fell from 9 incidents in the prior period to 5 in the current period.

306

▼ -2.5%was 314

Total Crash Events

3

▼ -25.0%was 4

Persons Killed

82

▲ 9.3%was 75

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 crash trends in Jones County show a slight decrease in volume year-over-year. Total crashes fell by 2.6%, from 314 to 306, and fatalities decreased from 4 to 3. However, the number of individuals injured in these incidents increased by 9.3%, from 75 to 82, indicating a mixed but generally stable trend.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 4↓-25.0%

3

Pedestrians Injured

Prior: 3→0.0%

1

Cyclists Injured

Prior: 1→0.0%

78

Motorists Injured

Prior: 71↑9.9%

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 shifted between the two periods. In 2016, the peak day for crashes was Saturday with 55 incidents, whereas in 2015, Monday and Friday were the peak days with 54 crashes each. The peak hour for collisions remained in the late evening but shifted slightly from 8 p.m. in the prior year (27 crashes) to 9 p.m. in the current year (27 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 severity of crashes showed some changes year-over-year, though the number of fatal crashes remained constant at 2 for both periods. The proportion of crashes resulting in any injury saw a slight increase from 19.1% to 20.7% of all incidents. This was primarily driven by a rise in minor injury crashes from 16 to 25, while serious injury crashes concurrently decreased from 13 to 10.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.7%
→0.0%prior 2
Serious Injury10serious injury crashes3.3%
↓-23.1%prior 13
Minor Injury25minor injury crashes8.2%
↑56.3%prior 16
Possible Injury28possible injury crashes9.2%
↓-9.7%prior 31
No Injury241no injury crashes78.8%
↓-4.4%prior 252

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 top contributing factor in both periods, though their count decreased by 17.5% from 143 in 2015 to 118 in 2016. 'Lost Control' was the second most common factor in both years, with its count also falling from 29 to 25. Notably, crashes attributed to 'Failure to Yield Right of Way from a stop sign' increased in count by 63.6%, from 11 to 18 incidents.

Officer-Reported Primary Contributing Cause

Animal118 (38.6%)↓-17.5%prior 143
Lost Control25 (8.2%)↓-13.8%prior 29
Driving too fast for conditions19 (6.2%)↑35.7%prior 14
FTYROW: From stop sign18 (5.9%)↑63.6%prior 11
Ran off road - straight15 (4.9%)↑36.4%prior 11
Followed too close12 (3.9%)↑9.1%prior 11
Ran off road - left10 (3.3%)↑42.9%prior 7
Other (explain in narrative): Other9 (2.9%)↓-47.1%prior 17
Other (explain in narrative): No improper action7 (2.3%)
Swerving/Evasive Action7 (2.3%)

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

Road & Environmental Conditions

There was a noticeable shift in crash conditions year-over-year, with a higher number of incidents occurring in clear conditions. Crashes on dry roads increased from 132 to 141, while those on snow-covered roads fell from 25 to 16. Similarly, crashes in daylight rose from 107 to 127, and collisions during snowy weather dropped from 22 to 9, suggesting a decrease in the proportion of crashes occurring in adverse conditions.

Weather

Clear119 (57.5%)
↑16.7%prior 102
Cloudy67 (32.4%)
↑31.4%prior 51
Snow9 (4.3%)
↓-59.1%prior 22
Rain4 (1.9%)
↓-71.4%prior 14
Blowing Snow3 (1.4%)
Severe Winds3 (1.4%)
Fog, smoke, smog2 (1.0%)

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

Lighting

Daylight127 (61.1%)
↑18.7%prior 107
Dark - roadway not lighted57 (27.4%)
↓-6.6%prior 61
Dark - roadway lighted14 (6.7%)
↓-12.5%prior 16
Dawn6 (2.9%)
Dusk4 (1.9%)
↓-42.9%prior 7

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

Road Surface

Dry141 (67.8%)
↑6.8%prior 132
Wet23 (11.1%)
↑9.5%prior 21
Snow16 (7.7%)
↓-36.0%prior 25
Gravel15 (7.2%)
↑150.0%prior 6
Ice/frost12 (5.8%)
↑33.3%prior 9
Slush1 (0.5%)

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

Vehicles & Demographics

An analysis of vehicles involved in crashes shows that Ford (95 vehicles) surpassed Chevrolet (77 vehicles) as the most common make in 2016, a reversal from 2015 when Chevrolet led with 91 vehicles to Ford's 81. The age distribution of persons involved also shifted; the 26-34 age group saw an increase in involvement from 72 to 83 individuals. Conversely, the 55-64 age group's involvement decreased significantly from 88 to 54 persons.

Top Vehicle Makes (398 vehicles)

1
FORD95 (23.9%)
↑17.3%prior 81
2
CHEVROLET45 (11.3%)
↓-11.8%prior 51
3
CHEV32 (8%)
↓-20.0%prior 40
4
DODGE19 (4.8%)
↑18.8%prior 16
5
PONTIAC12 (3%)
↑20.0%prior 10
6
GMC12 (3%)
→0.0%prior 12
7
DODG10 (2.5%)
↓-28.6%prior 14
8
NISSAN9 (2.3%)
9
TOYOTA9 (2.3%)
↓-47.1%prior 17
10
JEEP8 (2%)
↑14.3%prior 7

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

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

Sex Distribution (312 persons with recorded sex)

Male202 (64.7%)
↓-13.7%prior 234
Female110 (35.3%)
↓-28.1%prior 153

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: 306
  • Total persons involved: 460
  • Total vehicles involved: 398

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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