Yearly Traffic Safety Analysis

405 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Boone County, total crashes decreased by 7.1% from 436 in 2015 to 405 in 2016. During this period, the number of fatalities was halved, falling from 4 to 2. The total number of injuries also saw a decline, dropping from 163 in 2015 to 149 in 2016.

405

▼ -7.1%was 436

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

149

▼ -8.6%was 163

Persons Injured

2

▼ -50.0%was 4

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

Overall, traffic safety metrics in Boone County showed improvement from 2015 to 2016. Total crashes fell by 7.1%, from 436 to 405 incidents. Similarly, total injuries decreased by 8.6% from 163 to 149, and the number of fatalities was reduced by 50% from 4 to 2.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

0

Other Killed

Prior: 0→0.0%

2

Cyclists Injured

Prior: 1↑100.0%

146

Motorists Injured

Prior: 160↓-8.8%

1

Other Injured

Prior: 0→%

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 saw some shifts between 2015 and 2016. The peak day for collisions moved from Thursday (74 crashes) in the prior year to Friday (71 crashes) in the current year. However, the peak hour for crashes remained unchanged at 3 p.m. for both periods, with 43 crashes in 2016 compared to 42 in 2015.

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 in Boone County shifted between the two years, with a notable decrease in fatal outcomes. The number of fatal crashes was halved, decreasing from 4 in 2015 to 2 in 2016, with their share of total crashes falling from 0.9% to 0.5%. While the count of serious injury crashes increased slightly from 14 to 16, minor injury crashes fell from 46 (10.6% share) to 29 (7.2% share). Consequently, the proportion of crashes resulting in no injury rose from 71.3% in 2015 to 73.3% in 2016.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.5%
↓-50.0%prior 4
Serious Injury16serious injury crashes4%
↑14.3%prior 14
Minor Injury29minor injury crashes7.2%
↓-37.0%prior 46
Possible Injury61possible injury crashes15.1%
→0.0%prior 61
No Injury297no injury crashes73.3%
↓-4.5%prior 311

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 involving an animal remained the leading contributing factor in both periods, though the count decreased from 92 crashes in 2015 to 84 in 2016. A significant change occurred with crashes attributed to 'FTYROW: From stop sign,' which fell from 47 incidents to 33, dropping its rank from second to third. Conversely, crashes due to 'Lost Control' increased from 31 to 33, moving it up to the second-ranked factor. 'Followed too close' also saw an increase in count from 25 to 29 crashes.

Officer-Reported Primary Contributing Cause

Animal84 (20.7%)↓-8.7%prior 92
Lost Control33 (8.1%)↑6.5%prior 31
FTYROW: From stop sign33 (8.1%)↓-29.8%prior 47
Followed too close29 (7.2%)↑16.0%prior 25
Driving too fast for conditions26 (6.4%)↑85.7%prior 14
Ran off road - straight22 (5.4%)↓-8.3%prior 24
FTYROW: Making left turn19 (4.7%)↑72.7%prior 11
Other (explain in narrative): Other15 (3.7%)↓-25.0%prior 20
Ran Stop Sign15 (3.7%)↑7.1%prior 14
Ran off road - left13 (3.2%)↑85.7%prior 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

The distribution of crashes by lighting and weather conditions remained largely stable year-over-year, with most incidents in both periods occurring in daylight and clear weather. However, there was a notable shift in road surface conditions. The number of crashes on icy or frosty roads more than doubled, increasing from 14 in 2015 to 34 in 2016. Conversely, crashes on wet surfaces decreased from 45 to 25 over the same period.

Weather

Clear199 (59.9%)
↓-4.8%prior 209
Cloudy85 (25.6%)
↓-8.6%prior 93
Snow17 (5.1%)
↑54.5%prior 11
Rain14 (4.2%)
↓-44.0%prior 25
Blowing Snow6 (1.8%)
Freezing rain/drizzle5 (1.5%)
→0.0%prior 5
Severe Winds3 (0.9%)
Fog, smoke, smog3 (0.9%)

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

Lighting

Daylight245 (73.6%)
↓-2.8%prior 252
Dark - roadway not lighted46 (13.8%)
↓-11.5%prior 52
Dark - roadway lighted25 (7.5%)
↑19.0%prior 21
Dawn11 (3.3%)
↓-21.4%prior 14
Dusk5 (1.5%)
↓-44.4%prior 9
Dark - unknown roadway lighting1 (0.3%)

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

Road Surface

Dry227 (68.0%)
↓-9.9%prior 252
Ice/frost34 (10.2%)
↑142.9%prior 14
Wet25 (7.5%)
↓-44.4%prior 45
Snow22 (6.6%)
↓-12.0%prior 25
Gravel19 (5.7%)
↑58.3%prior 12
Slush4 (1.2%)
Mud, dirt3 (0.9%)

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 saw some changes in volume between periods. Ford, the top-ranked make, was involved in 106 crashes in 2016, a decrease from 130 in the prior year. The separate listings for 'Chevrolet' and 'CHEV' also shifted, with 'Chevrolet' increasing from 64 to 82 vehicles and 'CHEV' decreasing from 88 to 74. The age distribution of individuals involved in crashes remained broadly similar, with the 16-20 and 26-34 age groups being the most represented cohorts in both 2015 and 2016.

Top Vehicle Makes (636 vehicles)

1
FORD106 (16.7%)
↓-18.5%prior 130
2
CHEVROLET82 (12.9%)
↑28.1%prior 64
3
CHEV74 (11.6%)
↓-15.9%prior 88
4
DODGE29 (4.6%)
→0.0%prior 29
5
DODG26 (4.1%)
↓-25.7%prior 35
6
JEEP19 (3%)
→0.0%prior 19
7
GMC18 (2.8%)
↓-14.3%prior 21
8
BUIC14 (2.2%)
↑27.3%prior 11
9
NISS14 (2.2%)
↑40.0%prior 10
10
TOYT13 (2%)
↓-38.1%prior 21

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

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

Sex Distribution (485 persons with recorded sex)

Male249 (51.3%)
↓-29.5%prior 353
Female236 (48.7%)
↓-14.5%prior 276

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: 405
  • Total persons involved: 721
  • Total vehicles involved: 636

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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