Yearly Traffic Safety Analysis

163 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Palo Alto County, total crashes increased from 142 in 2016 to 163 in 2017, a rise of approximately 14.8%. The most significant year-over-year change was the increase in traffic fatalities, which rose from zero in the prior period to five in the current period. The total number of injuries remained stable, with 50 in 2017 compared to 51 in 2016.

163

▲ 14.8%was 142

Total Crash Events

5

Persons Killed

50

▼ -2.0%was 51

Persons Injured

5

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Palo Alto County showed an upward trend, increasing by 14.8% from 142 incidents in 2016 to 163 in 2017. While the total number of injuries remained nearly unchanged, decreasing by one person to 50, the number of fatalities rose from zero to five. This indicates an increase in both the frequency and overall severity of crashes during the period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Cyclists Killed

Prior: 0→%

4

Motorists Killed

Prior: 0→%

2

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 1→0.0%

47

Motorists Injured

Prior: 50↓-6.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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 2017, the peak day for crashes was Friday with 32 incidents, a change from Tuesday (26 incidents) in the prior year. Similarly, the peak hour for crashes moved from the 4 p.m. hour in 2016, which saw 12 crashes, to the 7 a.m. hour in 2017, which saw 13 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased significantly year-over-year. Fatal crashes rose from zero in 2016 to 5 in 2017, accounting for 3.1% of all incidents in the current period. While the number of serious injury crashes remained constant at 5, minor injury crashes decreased from 20 to 14. Conversely, crashes resulting in possible injuries increased from 15 to 21.

Outcome by Severity (Crash Events)

Fatal5fatal crashes3.1%
Serious Injury5serious injury crashes3.1%
→0.0%prior 5
Minor Injury14minor injury crashes8.6%
↓-30.0%prior 20
Possible Injury21possible injury crashes12.9%
↑40.0%prior 15
No Injury118no injury crashes72.4%
↑15.7%prior 102

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the top contributing factor in both periods, increasing in count from 40 to 44 incidents. 'Driving too fast for conditions' saw a significant increase, more than doubling from 5 crashes in 2016 to 11 in 2017, making it the third most common factor in the current period. Conversely, crashes attributed to 'Ran off road - straight' decreased from 14 to 10, and incidents involving 'Failure to yield from stop sign' fell from 11 to 6.

Officer-Reported Primary Contributing Cause

Animal44 (27%)↑10.0%prior 40
Lost Control13 (8%)→0.0%prior 13
Driving too fast for conditions11 (6.7%)↑120.0%prior 5
Ran off road - straight10 (6.1%)↓-28.6%prior 14
FTYROW: At uncontrolled intersection9 (5.5%)
Other (explain in narrative): Other7 (4.3%)↓-22.2%prior 9
Ran Stop Sign7 (4.3%)↑40.0%prior 5
FTYROW: From stop sign6 (3.7%)↓-45.5%prior 11
Driver Distraction: Inattentive/lost in thought5 (3.1%)
FTYROW: From driveway4 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The majority of crashes in both periods occurred in clear weather and daylight on dry roads. Crashes during daylight hours increased from 66 in 2016 to 87 in 2017. Incidents on roads affected by snow or ice decreased, with a combined total of 15 crashes in 2017 compared to 22 in the prior year. Crashes in dark, unlighted conditions also saw a decrease from 33 to 26.

Weather

Clear68 (54.8%)
↑25.9%prior 54
Cloudy42 (33.9%)
↓-2.3%prior 43
Rain4 (3.2%)
Snow4 (3.2%)
↓-33.3%prior 6
Blowing Snow3 (2.4%)
Fog, smoke, smog2 (1.6%)
Freezing rain/drizzle1 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight87 (68.5%)
↑31.8%prior 66
Dark - roadway not lighted26 (20.5%)
↓-21.2%prior 33
Dark - roadway lighted5 (3.9%)
Dusk4 (3.1%)
Dawn3 (2.4%)
↓-50.0%prior 6
Dark - unknown roadway lighting2 (1.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry88 (69.8%)
↑10.0%prior 80
Wet11 (8.7%)
Gravel10 (7.9%)
↑42.9%prior 7
Snow8 (6.3%)
↓-33.3%prior 12
Ice/frost7 (5.6%)
↓-30.0%prior 10
Slush1 (0.8%)
Mud, dirt1 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet remained the two most common vehicle makes involved in crashes, with Ford-made vehicles increasing from 52 to 58 and Chevrolet vehicles (including 'CHEV' and 'CHEVROLET') increasing from 45 to 47. Analysis of persons involved shows a notable increase in the 16-20 age group, which grew from 30 individuals in 2016 to 53 in 2017. The number of persons aged 65 and older involved in crashes also increased from 28 to 34.

Top Vehicle Makes (238 vehicles)

1
FORD58 (24.4%)
↑11.5%prior 52
2
CHEV27 (11.3%)
↑92.9%prior 14
3
CHEVROLET20 (8.4%)
↓-35.5%prior 31
4
GMC11 (4.6%)
↑37.5%prior 8
5
DODG11 (4.6%)
6
TOYT9 (3.8%)
7
BUICK7 (2.9%)
→0.0%prior 7
8
DODGE7 (2.9%)
↓-12.5%prior 8
9
JEEP6 (2.5%)
↑20.0%prior 5
10
BUIC6 (2.5%)
↑20.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (162 persons with recorded sex)

Male101 (62.3%)
↑5.2%prior 96
Female61 (37.7%)
↑17.3%prior 52

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 163
  • Total persons involved: 263
  • Total vehicles involved: 238

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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