Yearly Traffic Safety Analysis

321 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Poweshiek County recorded 321 total crashes, a 5.0% decrease from the 338 crashes documented in 2015. While overall crashes declined, the number of incidents involving a driver under the influence (DUI) increased significantly, rising from 3 in 2015 to 14 in 2016.

321

▼ -5.0%was 338

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

101

▲ 13.5%was 89

Persons Injured

2

▼ -33.3%was 3

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 crashes in Poweshiek County saw a modest decline in 2016, falling by 5.0% to 321 incidents from 338 the previous year. While the number of fatalities also decreased from 3 to 2, total injuries rose by 13.5%, from 89 in 2015 to 101 in 2016.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

1

Pedestrians Injured

Prior: 0→%

2

Cyclists Injured

Prior: 0→%

98

Motorists Injured

Prior: 89↑10.1%

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 timing of crashes shifted slightly between the two periods. In 2016, the peak day for crashes was Saturday with 54 incidents, a change from 2015 when Friday was the peak day with 60 crashes. The single busiest hour also moved from 4 p.m. in 2015 (27 crashes) to 5 p.m. in 2016 (29 crashes), aligning with the evening commute.

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

Crash severity saw a mixed change year-over-year. The number of fatal crashes decreased from 3 in 2015 to 2 in 2016, and serious injury crashes were halved from 10 to 5. However, crashes resulting in possible injuries increased from 36 to 44. Overall, the share of crashes involving any level of injury rose from 22.5% in 2015 to 24.3% in 2016.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.6%
↓-33.3%prior 3
Serious Injury5serious injury crashes1.6%
↓-50.0%prior 10
Minor Injury27minor injury crashes8.4%
→0.0%prior 27
Possible Injury44possible injury crashes13.7%
↑22.2%prior 36
No Injury243no injury crashes75.7%
↓-7.3%prior 262

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 with animals remained the leading contributing factor in both years, though the count slightly decreased from 60 in 2015 to 56 in 2016. There were significant reductions in crashes attributed to 'Lost Control' (from 49 to 28) and 'Ran off road - straight' (from 44 to 33). Conversely, crashes due to 'Failure to Yield Right of Way from a stop sign' increased by 69%, from 13 incidents in 2015 to 22 in 2016, making it a more prominent factor in the current period.

Officer-Reported Primary Contributing Cause

Animal56 (17.4%)↓-6.7%prior 60
Ran off road - straight33 (10.3%)↓-25.0%prior 44
Lost Control28 (8.7%)↓-42.9%prior 49
Driving too fast for conditions27 (8.4%)↑8.0%prior 25
FTYROW: From stop sign22 (6.9%)↑69.2%prior 13
Followed too close14 (4.4%)↓-17.6%prior 17
Other (explain in narrative): Other12 (3.7%)→0.0%prior 12
Operating vehicle in an reckless, erratic, careless, negligent manner12 (3.7%)↑140.0%prior 5
Ran off road - left9 (2.8%)↓-18.2%prior 11
Driver Distraction: Exterior distraction8 (2.5%)

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 broadly similar year-over-year, with clear weather and dry roads being the most common circumstances in both periods. In 2016, approximately 49% of crashes occurred in clear weather, nearly identical to the 50% in 2015. Crashes on dry roads accounted for 55% of the total in 2016, down slightly from 56% in the prior year. The proportion of crashes occurring in daylight increased from 51% in 2015 to 55% in 2016.

Weather

Clear157 (57.3%)
↓-7.1%prior 169
Cloudy56 (20.4%)
↓-5.1%prior 59
Snow26 (9.5%)
↓-21.2%prior 33
Rain13 (4.7%)
→0.0%prior 13
Freezing rain/drizzle13 (4.7%)
Fog, smoke, smog5 (1.8%)
Blowing Snow4 (1.5%)
↓-20.0%prior 5

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

Lighting

Daylight176 (64.2%)
↑2.9%prior 171
Dark - roadway not lighted68 (24.8%)
↓-19.0%prior 84
Dark - roadway lighted14 (5.1%)
↓-6.7%prior 15
Dawn9 (3.3%)
↑50.0%prior 6
Dusk7 (2.6%)
→0.0%prior 7

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

Road Surface

Dry176 (64.0%)
↓-7.4%prior 190
Wet32 (11.6%)
↑39.1%prior 23
Snow26 (9.5%)
↓-36.6%prior 41
Ice/frost22 (8.0%)
↑37.5%prior 16
Gravel11 (4.0%)
↓-15.4%prior 13
Slush6 (2.2%)
Sand1 (0.4%)
Mud, dirt1 (0.4%)

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

Vehicles & Demographics

Ford and Chevrolet vehicles were the most frequently involved in crashes in both years, with their counts increasing in 2016. The number of Ford vehicles in crashes rose from 73 to 90, while Chevrolet models (including 'CHEV' variants) increased from 83 to 115. Regarding driver and passenger demographics, there was a notable increase in the number of persons aged 16-20 involved in crashes, rising from 61 to 70. Conversely, involvement for most older age groups, such as 55-64, decreased from 88 to 71 persons.

Top Vehicle Makes (496 vehicles)

1
FORD90 (18.1%)
↑23.3%prior 73
2
CHEVROLET61 (12.3%)
↑84.8%prior 33
3
CHEV54 (10.9%)
↑8.0%prior 50
4
DODGE18 (3.6%)
↓-14.3%prior 21
5
GMC17 (3.4%)
↑70.0%prior 10
6
HONDA17 (3.4%)
↑54.5%prior 11
7
TOYOTA14 (2.8%)
↓-22.2%prior 18
8
DODG12 (2.4%)
↓-52.0%prior 25
9
JEEP11 (2.2%)
→0.0%prior 11
10
PONTIAC11 (2.2%)
↑83.3%prior 6

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

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

Sex Distribution (372 persons with recorded sex)

Male219 (58.9%)
↓-19.8%prior 273
Female153 (41.1%)
↓-1.3%prior 155

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: 321
  • Total persons involved: 569
  • Total vehicles involved: 496

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