Yearly Traffic Safety Analysis

297 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Tama County recorded 297 total crashes, an 8.4% increase from the 274 crashes reported in 2015. Despite the rise in overall collisions, the number of resulting injuries decreased by 28.6% from 105 to 75, and fatalities fell by 50% from 4 to 2. The most significant year-over-year change was a 76.9% reduction in crashes resulting in serious injuries, which fell from 13 in 2015 to 3 in 2016.

297

▲ 8.4%was 274

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

75

▼ -28.6%was 105

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

Overall traffic collisions in Tama County showed an upward trend, increasing by 8.4% from 274 crashes in 2015 to 297 in 2016. This represents an addition of 23 crashes year-over-year. However, this increase in crash volume was accompanied by a decrease in severity, as total injuries dropped by 28.6% and the number of fatalities was halved from 4 to 2.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 4↓-50.0%

75

Motorists Injured

Prior: 105↓-28.6%

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 52 incidents, a change from 2015 when Friday was the peak day with 46 crashes. The peak hour for collisions moved an hour earlier, from 7 p.m. in 2015 (24 crashes) to 6 p.m. in 2016 (30 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 stable at 2 for both years, the overall severity of crashes decreased in 2016. Crashes resulting in serious injuries saw a substantial drop, falling from 13 incidents (4.7% of all crashes) in 2015 to just 3 (1.0%) in 2016. Consequently, the proportion of no-injury crashes increased from 75.2% of all collisions in 2015 to 80.8% in 2016.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.7%
→0.0%prior 2
Serious Injury3serious injury crashes1%
↓-76.9%prior 13
Minor Injury27minor injury crashes9.1%
↓-10.0%prior 30
Possible Injury25possible injury crashes8.4%
↑8.7%prior 23
No Injury240no injury crashes80.8%
↑16.5%prior 206

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, with counts increasing by 5.4% from 111 crashes in 2015 to 117 in 2016. The number of crashes attributed to 'Lost Control' grew by 34.8%, from 23 to 31 incidents, making it the second most common factor in 2016. Similarly, crashes involving 'Driving too fast for conditions' increased in count by 62.5% from 16 to 26 incidents.

Officer-Reported Primary Contributing Cause

Animal117 (39.4%)↑5.4%prior 111
Lost Control31 (10.4%)↑34.8%prior 23
Driving too fast for conditions26 (8.8%)↑62.5%prior 16
Ran off road - straight12 (4%)↓-36.8%prior 19
Operating vehicle in an reckless, erratic, careless, negligent manner7 (2.4%)→0.0%prior 7
Driver Distraction: Inattentive/lost in thought7 (2.4%)
Made improper turn7 (2.4%)
FTYROW: From stop sign6 (2%)↓-45.5%prior 11
Swerving/Evasive Action6 (2%)↓-14.3%prior 7
Driver Distraction: Exterior distraction5 (1.7%)

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 environmental conditions remained largely consistent year-over-year, with the majority of crashes in both 2016 and 2015 occurring in clear weather and during daylight hours. The number of crashes on dry roads increased from 114 to 125, in line with the overall rise in collisions. A notable change was observed in lighting conditions, where crashes on dark, unlighted roadways increased from 39 in 2015 to 48 in 2016.

Weather

Clear122 (66.3%)
↑11.9%prior 109
Cloudy28 (15.2%)
↓-3.4%prior 29
Snow12 (6.5%)
→0.0%prior 12
Fog, smoke, smog7 (3.8%)
→0.0%prior 7
Rain6 (3.3%)
↓-45.5%prior 11
Blowing Snow3 (1.6%)
Freezing rain/drizzle2 (1.1%)
Other (explain in narrative)2 (1.1%)
Sleet, hail1 (0.5%)
Severe Winds1 (0.5%)

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

Lighting

Daylight109 (60.2%)
↓-0.9%prior 110
Dark - roadway not lighted48 (26.5%)
↑23.1%prior 39
Dark - roadway lighted14 (7.7%)
↑16.7%prior 12
Dusk7 (3.9%)
Dawn3 (1.7%)
↓-40.0%prior 5

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

Road Surface

Dry125 (67.6%)
↑9.6%prior 114
Snow18 (9.7%)
↑12.5%prior 16
Gravel13 (7.0%)
↓-13.3%prior 15
Ice/frost12 (6.5%)
↑20.0%prior 10
Wet10 (5.4%)
↓-37.5%prior 16
Slush5 (2.7%)
Other (explain in narrative)2 (1.1%)

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

Vehicles & Demographics

Ford and Chevrolet remained the top two vehicle makes involved in crashes for both years, with their counts increasing slightly in 2016. An analysis of persons involved shows a shift in age demographics between the two periods. In 2015, the 16-20 and 45-54 age groups were most represented with 70 persons each. By 2016, the number of persons in the 16-20 group fell to 63 and the 45-54 group fell to 50, while the 26-34 age group became the most frequently involved with 69 persons.

Top Vehicle Makes (393 vehicles)

1
FORD79 (20.1%)
↑2.6%prior 77
2
CHEVROLET54 (13.7%)
↑8.0%prior 50
3
CHEV41 (10.4%)
↑13.9%prior 36
4
DODGE21 (5.3%)
↑5.0%prior 20
5
HONDA17 (4.3%)
↑142.9%prior 7
6
DODG17 (4.3%)
↑54.5%prior 11
7
GMC11 (2.8%)
↑57.1%prior 7
8
PONTIAC11 (2.8%)
↑120.0%prior 5
9
TOYT10 (2.5%)
10
CHRYSLER9 (2.3%)
↑80.0%prior 5

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

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

Sex Distribution (293 persons with recorded sex)

Male190 (64.8%)
↓-9.5%prior 210
Female103 (35.2%)
↓-23.1%prior 134

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: 297
  • Total persons involved: 444
  • Total vehicles involved: 393

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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