Yearly Traffic Safety Analysis

142 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Palo Alto County, total traffic crashes decreased by 5.3% from 150 in 2015 to 142 in 2016. While overall incidents fell, the most notable year-over-year change was a tripling in the number of crashes involving driving under the influence (DUI), which increased from 3 to 9 incidents.

142

▼ -5.3%was 150

Total Crash Events

0

Persons Killed

51

▼ -5.6%was 54

Persons Injured

0

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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 shows a slight decline in traffic incidents year-over-year. Total crashes fell from 150 to 142, and the number of people injured decreased from 54 to 51. There were no fatalities recorded in either period.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

1

Cyclists Injured

Prior: 0→%

50

Motorists Injured

Prior: 54↓-7.4%

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 slightly between the two years. The peak day for crashes moved from Monday (31 crashes) in the prior year to Tuesday (26 crashes) in the current year. Similarly, the peak hour for incidents shifted one hour later, from 3 PM (12 crashes) to 4 PM (12 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 there were no fatal crashes in either year, the severity of injury crashes increased. The number of crashes resulting in a serious injury more than doubled, rising from 2 in the prior period to 5 in the current period. Consequently, the share of crashes classified as serious injury rose from 1.3% to 3.5% of all incidents.

Outcome by Severity (Crash Events)

Serious Injury5serious injury crashes3.5%
↑150.0%prior 2
Minor Injury20minor injury crashes14.1%
→0.0%prior 20
Possible Injury15possible injury crashes10.6%
↑36.4%prior 11
No Injury102no injury crashes71.8%
↓-12.8%prior 117

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 periods, accounting for 40 crashes each year. The count of crashes attributed to a vehicle running off a straight road increased from 9 to 14. A significant shift occurred in crashes involving driving under the influence (DUI), which saw a 200% increase in count from 3 incidents in the prior year to 9 in the current year.

Officer-Reported Primary Contributing Cause

Animal40 (28.2%)→0.0%prior 40
Ran off road - straight14 (9.9%)↑55.6%prior 9
Lost Control13 (9.2%)↑18.2%prior 11
FTYROW: From stop sign11 (7.7%)↓-8.3%prior 12
Other (explain in narrative): Other9 (6.3%)↑80.0%prior 5
Ran Stop Sign5 (3.5%)↓-28.6%prior 7
Driving too fast for conditions5 (3.5%)↓-37.5%prior 8
Ran off road - left5 (3.5%)↓-16.7%prior 6
FTYROW: From driveway4 (2.8%)↓-20.0%prior 5
FTYROW: From parked position3 (2.1%)

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

Road & Environmental Conditions

There was a notable shift in lighting conditions for crashes year-over-year. Crashes occurring in daylight decreased from 80 to 66, while incidents on dark, unlit roadways increased from 24 to 33. Correspondingly, crashes on dry road surfaces increased from 70 to 80, while crashes on icy surfaces decreased from 15 to 10.

Weather

Clear54 (49.5%)
→0.0%prior 54
Cloudy43 (39.4%)
↑26.5%prior 34
Snow6 (5.5%)
↓-25.0%prior 8
Freezing rain/drizzle3 (2.8%)
Rain2 (1.8%)
↓-66.7%prior 6
Blowing Snow1 (0.9%)

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

Lighting

Daylight66 (58.4%)
↓-17.5%prior 80
Dark - roadway not lighted33 (29.2%)
↑37.5%prior 24
Dawn6 (5.3%)
Dark - roadway lighted4 (3.5%)
Dusk3 (2.7%)
Dark - unknown roadway lighting1 (0.9%)

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

Road Surface

Dry80 (70.8%)
↑14.3%prior 70
Snow12 (10.6%)
↑50.0%prior 8
Ice/frost10 (8.8%)
↓-33.3%prior 15
Gravel7 (6.2%)
↑40.0%prior 5
Wet3 (2.7%)
↓-66.7%prior 9
Slush1 (0.9%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes showed some changes; the count of Fords increased from 37 to 52, while the combined count for Chevrolet vehicles ("CHEV" and "CHEVROLET") decreased from 62 to 45. There was a significant decrease in the number of younger people involved in crashes, with the 16-25 age group accounting for 46 individuals in the current year, down from 80 in the prior year.

Top Vehicle Makes (201 vehicles)

1
FORD52 (25.9%)
↑40.5%prior 37
2
CHEVROLET31 (15.4%)
↑19.2%prior 26
3
CHEV14 (7%)
↓-61.1%prior 36
4
PONTIAC10 (5%)
↑100.0%prior 5
5
GMC8 (4%)
→0.0%prior 8
6
DODGE8 (4%)
↓-33.3%prior 12
7
BUICK7 (3.5%)
↑16.7%prior 6
8
JEEP5 (2.5%)
9
CHRY5 (2.5%)
10
TOYOTA5 (2.5%)
→0.0%prior 5

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

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

Sex Distribution (148 persons with recorded sex)

Male96 (64.9%)
↓-18.6%prior 118
Female52 (35.1%)
↓-40.2%prior 87

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: 142
  • Total persons involved: 220
  • Total vehicles involved: 201

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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