Yearly Traffic Safety Analysis

115 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Ida County, total traffic crashes increased by 10.6% from 104 in 2016 to 115 in 2017. While fatalities remained stable with one death in each period, the number of injuries rose from 30 to 34. The most significant year-over-year change was a rise in crashes involving a driver under the influence (DUI), which increased from one crash in 2016 to six in 2017.

115

▲ 10.6%was 104

Total Crash Events

1

Persons Killed

34

▲ 13.3%was 30

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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

Overall traffic safety trends in Ida County worsened from 2016 to 2017. The total number of crashes increased by 10.6%, rising from 104 to 115. Similarly, the number of people injured in these incidents grew by 13.3%, from 30 to 34, while the number of fatalities held steady at one.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 1→0.0%

34

Motorists Injured

Prior: 30↑13.3%

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 25 incidents, a change from 2016 when Saturday was the peak day with 21 crashes. The most common time for a crash also moved later in the day, from the 3 p.m. hour in 2016 (10 crashes) to the 7 p.m. hour in 2017 (11 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

While the number of fatal crashes remained unchanged at one for both years, the overall severity profile of crashes shifted. The proportion of crashes resulting in no injuries increased from 75% of all crashes in 2016 to 80% in 2017. Concurrently, the share of crashes involving minor or possible injuries decreased, with minor injury crashes falling from 10.6% to 8.7% and possible injury crashes falling from 12.5% to 9.6% of the total.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.9%
→0.0%prior 1
Serious Injury1serious injury crashes0.9%
→0.0%prior 1
Minor Injury10minor injury crashes8.7%
↓-9.1%prior 11
Possible Injury11possible injury crashes9.6%
↓-15.4%prior 13
No Injury92no injury crashes80%
↑17.9%prior 78

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 with animals remained the leading contributing factor in both periods, with the count of such incidents increasing from 24 in 2016 to 32 in 2017. 'Driving too fast for conditions' dropped from the second-most common factor with 11 crashes in 2016 to the sixth-most with 6 crashes in 2017. Conversely, crashes attributed to 'Failure to yield right of way when making a left turn' increased from 2 to 7, and 'Lost Control' incidents rose from 3 to 7.

Officer-Reported Primary Contributing Cause

Animal32 (27.8%)↑33.3%prior 24
Other (explain in narrative): Other10 (8.7%)↑66.7%prior 6
FTYROW: Making left turn7 (6.1%)
Ran off road - straight7 (6.1%)
Lost Control7 (6.1%)
Driving too fast for conditions6 (5.2%)↓-45.5%prior 11
FTYROW: From stop sign4 (3.5%)
Followed too close4 (3.5%)↓-20.0%prior 5
Ran off road - left3 (2.6%)↓-40.0%prior 5
Failed to yield to emergency vehicle2 (1.7%)

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

Road & Environmental Conditions

There was a notable shift in crash conditions year-over-year. The number of crashes occurring on dry road surfaces increased from 45 in 2016 to 67 in 2017. Correspondingly, crashes on adverse surfaces like ice or snow saw a significant decrease, falling from a combined 25 incidents in 2016 to 12 in 2017. The proportion of crashes happening in daylight also decreased from 54.8% to 48.7% of the total.

Weather

Clear57 (66.3%)
↑5.6%prior 54
Cloudy15 (17.4%)
↑66.7%prior 9
Snow6 (7.0%)
↓-14.3%prior 7
Blowing Snow2 (2.3%)
↓-60.0%prior 5
Fog, smoke, smog2 (2.3%)
Other (explain in narrative)2 (2.3%)
Rain2 (2.3%)

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

Lighting

Daylight56 (65.9%)
↓-1.8%prior 57
Dark - roadway not lighted14 (16.5%)
↑27.3%prior 11
Dark - roadway lighted10 (11.8%)
↑100.0%prior 5
Dusk3 (3.5%)
Dawn2 (2.4%)

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

Road Surface

Dry67 (77.0%)
↑48.9%prior 45
Snow8 (9.2%)
↓-33.3%prior 12
Wet5 (5.7%)
Ice/frost4 (4.6%)
↓-69.2%prior 13
Gravel3 (3.4%)
↓-50.0%prior 6

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

Vehicles & Demographics

The makes of vehicles involved in crashes saw a shift, with Chevrolet-branded vehicles (49) surpassing Ford (31) as the most common make in 2017, a reversal from 2016 when Ford led with 37 vehicles to Chevrolet's 33. The demographics of persons involved also changed, as the share of individuals aged 65 and older increased from 8.7% of all persons in 2016 to 13.8% in 2017.

Top Vehicle Makes (175 vehicles)

1
CHEV32 (18.3%)
↑113.3%prior 15
2
FORD31 (17.7%)
↓-16.2%prior 37
3
CHEVROLET17 (9.7%)
↓-5.6%prior 18
4
GMC11 (6.3%)
↑120.0%prior 5
5
CHRY10 (5.7%)
6
DODGE6 (3.4%)
↓-25.0%prior 8
7
DODG6 (3.4%)
8
PONT5 (2.9%)
9
KIA5 (2.9%)
10
OLDS4 (2.3%)

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

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

Sex Distribution (131 persons with recorded sex)

Male76 (58.0%)
↑7.0%prior 71
Female55 (42.0%)
↑44.7%prior 38

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: 115
  • Total persons involved: 210
  • Total vehicles involved: 175

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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