Yearly Traffic Safety Analysis

523 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Wapello County recorded 523 total crashes, a 3.3% decrease from the 541 crashes documented in 2015. Despite the overall reduction in crashes, the number of fatalities increased significantly, rising from 2 in 2015 to 6 in 2016. This represents a 200% year-over-year increase in traffic-related deaths.

523

▼ -3.3%was 541

Total Crash Events

6

▲ 200.0%was 2

Persons Killed

229

▲ 4.1%was 220

Persons Injured

4

▲ 100.0%was 2

Fatal Crash Events

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 Wapello County shows a slight decrease in total crashes, falling by 3.3% from 541 in 2015 to 523 in 2016. However, the severity of these incidents worsened, with total injuries rising by 4.1% from 220 to 229, and total fatalities tripling from 2 to 6.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

6

Motorists Killed

Prior: 2↑200.0%

4

Pedestrians Injured

Prior: 4→0.0%

3

Cyclists Injured

Prior: 1↑200.0%

222

Motorists Injured

Prior: 215↑3.3%

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

Temporal patterns show a shift in the most common day for crashes, moving from Tuesday (89 crashes) in 2015 to Friday (93 crashes) in 2016. The peak hour for collisions remained consistent at 3 p.m. in both periods, though the volume of crashes during this hour decreased from 53 to 41. Crashes on Fridays saw a notable increase from 72 in the prior year to 93 in the current year.

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 total crashes decreased, their severity increased year-over-year. The number of fatal crashes doubled from 2 in 2015 to 4 in 2016, raising the fatal crash rate from 0.4% to 0.8% of all incidents. Crashes resulting in serious injuries also saw a slight increase in both count (from 11 to 12) and proportion (from 2.0% to 2.3%). Correspondingly, the share of crashes with no injuries decreased from 68.2% to 65.8%.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 6 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.8%
↑100.0%prior 2
Serious Injury12serious injury crashes2.3%
↑9.1%prior 11
Minor Injury57minor injury crashes10.9%
↓-6.6%prior 61
Possible Injury106possible injury crashes20.3%
↑8.2%prior 98
No Injury344no injury crashes65.8%
↓-6.8%prior 369

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

The leading contributing factors for crashes remained consistent between 2015 and 2016, with 'Animal' (73 crashes), 'Lost Control' (52 crashes), and 'FTYROW: From stop sign' (45 crashes) as the top three in the current period. However, the count for each of these top factors decreased from the prior year's totals of 77, 62, and 59, respectively. Conversely, incidents where 'Ran Stop Sign' was a factor increased in count by 46.7%, from 15 to 22 crashes, and crashes involving 'Ran off road - straight' grew from 19 to 26.

Officer-Reported Primary Contributing Cause

Animal73 (14%)↓-5.2%prior 77
Lost Control52 (9.9%)↓-16.1%prior 62
FTYROW: From stop sign45 (8.6%)↓-23.7%prior 59
Followed too close40 (7.6%)↓-18.4%prior 49
Driving too fast for conditions32 (6.1%)→0.0%prior 32
Ran off road - left30 (5.7%)↑11.1%prior 27
FTYROW: Making left turn26 (5%)↓-13.3%prior 30
Ran off road - straight26 (5%)↑36.8%prior 19
Other (explain in narrative): Other24 (4.6%)↑33.3%prior 18
Ran Stop Sign22 (4.2%)↑46.7%prior 15

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

Road & Environmental Conditions

Crash conditions remained largely stable year-over-year, with most incidents in both periods occurring in clear weather and on dry roads. In 2016, 60.8% of crashes happened in clear weather, compared to 61.7% in 2015. Similarly, dry road surfaces were reported in 67.3% of crashes in both years. There was a slight increase in the proportion of crashes occurring in dark conditions, rising from 20.5% in 2015 to 22.9% in 2016.

Weather

Clear318 (69.4%)
↓-4.8%prior 334
Cloudy89 (19.4%)
↑12.7%prior 79
Snow19 (4.1%)
↓-17.4%prior 23
Rain16 (3.5%)
↓-30.4%prior 23
Freezing rain/drizzle10 (2.2%)
↓-9.1%prior 11
Blowing Snow3 (0.7%)
Fog, smoke, smog2 (0.4%)
Blowing sand, soil, dirt1 (0.2%)

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

Lighting

Daylight325 (71.1%)
↓-4.7%prior 341
Dark - roadway lighted60 (13.1%)
↑5.3%prior 57
Dark - roadway not lighted60 (13.1%)
↑11.1%prior 54
Dark - unknown roadway lighting5 (1.1%)
→0.0%prior 5
Dusk4 (0.9%)
↓-55.6%prior 9
Dawn3 (0.7%)
↓-62.5%prior 8

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

Road Surface

Dry352 (77.0%)
↓-3.3%prior 364
Wet39 (8.5%)
↓-18.8%prior 48
Snow29 (6.3%)
↓-9.4%prior 32
Ice/frost19 (4.2%)
↓-13.6%prior 22
Slush9 (2.0%)
Gravel5 (1.1%)
↓-16.7%prior 6
Sand4 (0.9%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford, Chevrolet, and Dodge leading in both years. The number of Ford vehicles in crashes increased from 137 to 156, while combined Chevrolet models saw a decrease from 196 to 182. Analysis of persons involved shows a shift in age demographics; the proportion of individuals aged 65 and older increased from 9.2% in 2015 to 11.3% in 2016. Conversely, the share of persons in the 16-20 age group decreased from 16.3% to 14.5%.

Top Vehicle Makes (843 vehicles)

1
FORD156 (18.5%)
↑13.9%prior 137
2
CHEV97 (11.5%)
↓-26.0%prior 131
3
CHEVROLET85 (10.1%)
↑30.8%prior 65
4
DODGE44 (5.2%)
↑22.2%prior 36
5
DODG42 (5%)
↓-33.3%prior 63
6
TOYT31 (3.7%)
↓-13.9%prior 36
7
TOYOTA30 (3.6%)
↓-11.8%prior 34
8
GMC29 (3.4%)
↑3.6%prior 28
9
JEEP29 (3.4%)
↓-14.7%prior 34
10
PONT22 (2.6%)
↑4.8%prior 21

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

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

Sex Distribution (642 persons with recorded sex)

Male364 (56.7%)
↓-20.0%prior 455
Female278 (43.3%)
↓-21.9%prior 356

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: 523
  • Total persons involved: 994
  • Total vehicles involved: 843

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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