Yearly Traffic Safety Analysis

82 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Worth County recorded 82 total crashes, a 15.5% decrease from the 97 crashes reported in 2015. Despite the overall reduction in collisions, the outcomes were more severe on average, with the number of fatalities increasing from 2 to 3 and total injuries rising from 33 to 35.

82

▼ -15.5%was 97

Total Crash Events

3

▲ 50.0%was 2

Persons Killed

35

▲ 6.1%was 33

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

Traffic crashes in Worth County showed a downward trend, decreasing from 97 incidents in 2015 to 82 in 2016, a 15.5% reduction in total collisions. However, the severity of these crashes increased, as total injuries rose from 33 to 35 and fatalities increased from 2 to 3.

Vulnerable Road User Casualties

3

Motorists Killed

Prior: 2↑50.0%

35

Motorists Injured

Prior: 33↑6.1%

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 Friday with 20 incidents, a change from 2015 when Sunday was the peak day with 20 crashes. The peak hour for collisions also moved later into the evening, shifting from a peak of 7 crashes at 10 AM in 2015 to a peak of 10 crashes at 9 PM in 2016.

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 2 for both 2015 and 2016, the number of people killed increased from 2 to 3, and the fatal crash rate rose from 2.06% to 2.44%. The proportion of crashes resulting in serious injury decreased from 4.1% (4 crashes) in 2015 to 2.4% (2 crashes) in 2016. Overall, the share of crashes involving any level of injury (fatal, serious, minor, or possible) declined from 27.8% in 2015 to 24.4% in 2016.

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

Outcome by Severity (Crash Events)

Fatal2fatal crashes2.4%
→0.0%prior 2
Serious Injury2serious injury crashes2.4%
↓-50.0%prior 4
Minor Injury8minor injury crashes9.8%
↓-20.0%prior 10
Possible Injury8possible injury crashes9.8%
↓-27.3%prior 11
No Injury62no injury crashes75.6%
↓-11.4%prior 70

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 the count decreasing slightly from 29 in 2015 to 26 in 2016. Crashes attributed to 'Lost Control' saw a significant reduction, dropping from 11 incidents in 2015 to 4 in 2016. In contrast, crashes where a driver 'Ran Stop Sign' more than doubled, increasing from 2 to 5, and incidents involving 'Driving too fast for conditions' rose from 8 to 9.

Officer-Reported Primary Contributing Cause

Animal26 (31.7%)↓-10.3%prior 29
Driving too fast for conditions9 (11%)↑12.5%prior 8
Ran off road - straight9 (11%)↑12.5%prior 8
FTYROW: From stop sign5 (6.1%)
Ran Stop Sign5 (6.1%)
Followed too close4 (4.9%)↓-42.9%prior 7
Lost Control4 (4.9%)↓-63.6%prior 11
Other (explain in narrative): Other3 (3.7%)
Driver Distraction: Other interior distraction2 (2.4%)
Driver Distraction: Passenger2 (2.4%)

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 proportion of crashes occurring under adverse conditions shifted between the two years. In 2016, crashes on dry road surfaces accounted for 32.9% of the total (27 crashes), a proportional decrease from 50.5% (49 crashes) in 2015. The share of crashes on wet roads increased from 9.3% in 2015 to 13.4% in 2016. Similarly, the proportion of collisions happening in clear weather conditions decreased from 41.2% to 28.0% year-over-year.

Weather

Clear23 (39.0%)
↓-42.5%prior 40
Cloudy12 (20.3%)
↓-25.0%prior 16
Snow11 (18.6%)
↓-15.4%prior 13
Rain7 (11.9%)
↑40.0%prior 5
Blowing Snow3 (5.1%)
Other (explain in narrative)1 (1.7%)
Freezing rain/drizzle1 (1.7%)
Fog, smoke, smog1 (1.7%)

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

Lighting

Daylight34 (57.6%)
↓-22.7%prior 44
Dark - roadway not lighted19 (32.2%)
↓-32.1%prior 28
Dusk4 (6.8%)
↓-33.3%prior 6
Dark - roadway lighted1 (1.7%)
Dark - unknown roadway lighting1 (1.7%)

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

Road Surface

Dry27 (45.8%)
↓-44.9%prior 49
Wet11 (18.6%)
↑22.2%prior 9
Snow10 (16.9%)
↓-23.1%prior 13
Ice/frost6 (10.2%)
→0.0%prior 6
Slush3 (5.1%)
Gravel2 (3.4%)

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

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 139 in 2015 to 107 in 2016. Chevrolet and Ford remained the top two vehicle makes involved in crashes in both years, though their counts decreased from 25 to 16 and 22 to 14, respectively. Regarding persons involved, the 45-54 age group saw its representation drop from 36 individuals in 2015 to 15 in 2016. The 26-34 age group remained one of the most involved demographics with 24 individuals in 2016, a slight decrease from 26 the prior year.

Top Vehicle Makes (107 vehicles)

1
CHEVROLET16 (15%)
↓-36.0%prior 25
2
FORD14 (13.1%)
↓-36.4%prior 22
3
DODGE9 (8.4%)
4
CHEV6 (5.6%)
↓-53.8%prior 13
5
INTERNATIONA5 (4.7%)
↓-16.7%prior 6
6
GMC5 (4.7%)
7
JEEP5 (4.7%)
8
BUICK4 (3.7%)
9
PONT4 (3.7%)
10
VOLVO3 (2.8%)

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

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

Sex Distribution (67 persons with recorded sex)

Male49 (73.1%)
↓-38.0%prior 79
Female18 (26.9%)
↓-41.9%prior 31

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: 82
  • Total persons involved: 126
  • Total vehicles involved: 107

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

ThatCarHitMe.com · An Injuria.ai Company