Yearly Traffic Safety Analysis

117 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Shelby County, total traffic crashes decreased by 7.1% from 126 in 2015 to 117 in 2016. Fatalities fell from 3 to 2, and injuries dropped from 61 to 50. The most significant year-over-year change was a 69% increase in the count of crashes where an animal was a contributing factor, rising from 13 to 22 incidents.

117

▼ -7.1%was 126

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

50

▼ -18.0%was 61

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (2) 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

The overall trend in Shelby County shows a decrease in traffic incidents year-over-year. Total crashes declined by 7.1%, from 126 in 2015 to 117 in 2016. This downward trend was also reflected in crash severity, with total injuries decreasing by 18% from 61 to 50 and fatalities declining from 3 to 2.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

1

Cyclists Injured

Prior: 0→%

49

Motorists Injured

Prior: 60↓-18.3%

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 the two periods. The peak day for crashes moved from Thursday (22 crashes) in 2015 to Friday (23 crashes) in 2016. Similarly, the peak hour for incidents shifted slightly later, from the 4 p.m. hour in the prior year to the 5 p.m. hour in the current year, which saw 11 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 remained constant at two, the fatal crash rate per 100 crashes saw a slight increase from 1.59 to 1.71. However, the overall severity of crashes lessened, with a notable increase in the proportion of no-injury crashes from 60.3% in 2015 to 70.9% in 2016. Crashes resulting in possible injuries saw a significant drop, from 26 incidents to 13.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.7%
→0.0%prior 2
Serious Injury6serious injury crashes5.1%
↓-25.0%prior 8
Minor Injury13minor injury crashes11.1%
↓-7.1%prior 14
Possible Injury13possible injury crashes11.1%
↓-50.0%prior 26
No Injury83no injury crashes70.9%
↑9.2%prior 76

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

Crashes involving an animal became the leading contributing factor in 2016, with the count of such incidents increasing by 69% from 13 to 22. In contrast, several other major factors saw a decline; crashes attributed to a driver losing control fell from 13 to 9, and those related to driving too fast for conditions decreased from 10 to 6. Failure to yield when making a left turn saw an increase, with the count rising from 2 to 7 crashes.

Officer-Reported Primary Contributing Cause

Animal22 (18.8%)↑69.2%prior 13
Lost Control9 (7.7%)↓-30.8%prior 13
Other (explain in narrative): Other8 (6.8%)↑14.3%prior 7
FTYROW: Making left turn7 (6%)
Driving too fast for conditions6 (5.1%)↓-40.0%prior 10
Made improper turn5 (4.3%)
Ran off road - straight5 (4.3%)↓-44.4%prior 9
FTYROW: From stop sign4 (3.4%)↓-60.0%prior 10
Driver Distraction: Other interior distraction3 (2.6%)
Exceeded authorized speed3 (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

There was a shift in crash conditions year-over-year. The proportion of crashes occurring in daylight decreased from 65.1% to 55.6%, while incidents on dark, unlighted roadways increased from 13.5% to 19.7% of the total. Crashes on dry road surfaces became more frequent, accounting for 61.5% of incidents compared to 51.6% previously, as crashes on wet roads fell from 19 to 8. The distribution of crashes by weather condition remained largely stable, with clear weather predominating in both years.

Weather

Clear65 (67.0%)
↓-4.4%prior 68
Cloudy16 (16.5%)
↓-23.8%prior 21
Freezing rain/drizzle5 (5.2%)
→0.0%prior 5
Blowing Snow4 (4.1%)
Rain3 (3.1%)
↓-62.5%prior 8
Severe Winds2 (2.1%)
Fog, smoke, smog1 (1.0%)
Snow1 (1.0%)
↓-85.7%prior 7

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

Lighting

Daylight65 (65.0%)
↓-20.7%prior 82
Dark - roadway not lighted23 (23.0%)
↑35.3%prior 17
Dark - roadway lighted8 (8.0%)
↓-27.3%prior 11
Dawn2 (2.0%)
Dusk2 (2.0%)
↓-60.0%prior 5

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

Road Surface

Dry72 (72.0%)
↑10.8%prior 65
Ice/frost9 (9.0%)
→0.0%prior 9
Wet8 (8.0%)
↓-57.9%prior 19
Gravel6 (6.0%)
↓-45.5%prior 11
Snow4 (4.0%)
↓-50.0%prior 8
Slush1 (1.0%)

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

Vehicles & Demographics

Ford and Chevrolet were the top two vehicle makes involved in crashes in both 2015 and 2016. Analysis of persons involved in crashes shows a shift in age demographics; the representation of the 45-54 age group increased from 15.8% to 21.5% of all individuals with a known age. The involvement of the 16-20 age group remained proportionally steady at around 15.5% in both years.

Top Vehicle Makes (180 vehicles)

1
FORD41 (22.8%)
↓-19.6%prior 51
2
CHEV21 (11.7%)
↓-22.2%prior 27
3
CHEVROLET20 (11.1%)
↑42.9%prior 14
4
PONTIAC6 (3.3%)
5
DODG6 (3.3%)
↓-57.1%prior 14
6
JEEP5 (2.8%)
→0.0%prior 5
7
BUIC5 (2.8%)
↓-37.5%prior 8
8
CHRY5 (2.8%)
9
CHRYSLER5 (2.8%)
10
DODGE5 (2.8%)
↓-16.7%prior 6

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

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

Sex Distribution (128 persons with recorded sex)

Male74 (57.8%)
↓-33.9%prior 112
Female54 (42.2%)
↓-28.0%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: 117
  • Total persons involved: 197
  • Total vehicles involved: 180

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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