Yearly Traffic Safety Analysis

81 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Davis County, total traffic crashes decreased from 94 in 2016 to 81 in 2017, a 13.8% reduction. The most significant year-over-year change was the elimination of traffic fatalities, which fell from 3 in the prior period to 0 in the current period. Overall injuries remained nearly stable, with 40 recorded in 2017 compared to 41 in 2016.

81

▼ -13.8%was 94

Total Crash Events

0

▼ -100.0%was 3

Persons Killed

40

▼ -2.4%was 41

Persons Injured

0

▼ -100.0%was 2

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety trends in Davis County showed improvement year-over-year. Total crashes fell by 13.8%, from 94 in 2016 to 81 in 2017. This positive trend was most pronounced in crash severity, with fatalities dropping from 3 to 0 and total injuries seeing a slight decrease from 41 to 40.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 3↓-100.0%

40

Motorists Injured

Prior: 38↑5.3%

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. The peak day for crashes moved from Friday, with 22 crashes in 2016, to Thursday, with 16 crashes in 2017. Similarly, the peak hour for collisions shifted from the 5 p.m. hour in the prior year, which saw 12 crashes, to the 6 p.m. hour in the current year, with 8 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 improved significantly from 2016 to 2017. Fatal crashes were eliminated, dropping from 2 in the prior period to 0 in the current period, representing a 2.1% share of crashes in 2016. The proportion of serious injury crashes also decreased, falling from 6.4% to 3.7% of all incidents. Conversely, the share of crashes involving possible injuries increased from 16% in 2016 to 21% in 2017.

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes3.7%
↓-50.0%prior 6
Minor Injury7minor injury crashes8.6%
↑16.7%prior 6
Possible Injury17possible injury crashes21%
↑13.3%prior 15
No Injury54no injury crashes66.7%
↓-16.9%prior 65

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, with a consistent count of 24 crashes. The second-leading factor changed significantly; crashes attributed to 'Followed too close' dropped from 11 in 2016 to 3 in 2017, a 72.7% decrease in count. 'Ran off road - straight' became the second-ranked factor in 2017, increasing from 4 to 7 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal24 (29.6%)→0.0%prior 24
Ran off road - straight7 (8.6%)
FTYROW: Other (explain in narrative)5 (6.2%)
Driving too fast for conditions4 (4.9%)
FTYROW: From stop sign4 (4.9%)↓-20.0%prior 5
Lost Control4 (4.9%)↓-33.3%prior 6
Ran Traffic Signal4 (4.9%)
Followed too close3 (3.7%)↓-72.7%prior 11
Ran Stop Sign3 (3.7%)
FTYROW: Making left turn2 (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

In both 2016 and 2017, the majority of crashes occurred during daylight on dry roads under clear skies. The number of crashes in daylight conditions decreased from 59 to 47 year-over-year. Incidents on roads with ice or frost were cut in half, dropping from 8 crashes in 2016 to 4 in 2017. Similarly, crashes during clear weather fell from 58 to 43.

Weather

Clear43 (71.7%)
↓-25.9%prior 58
Cloudy11 (18.3%)
↓-26.7%prior 15
Rain4 (6.7%)
Freezing rain/drizzle1 (1.7%)
Snow1 (1.7%)

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

Lighting

Daylight47 (77.0%)
↓-20.3%prior 59
Dark - roadway not lighted10 (16.4%)
↓-9.1%prior 11
Dark - roadway lighted3 (4.9%)
Dawn1 (1.6%)

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

Road Surface

Dry50 (82.0%)
↓-10.7%prior 56
Ice/frost4 (6.6%)
↓-50.0%prior 8
Wet4 (6.6%)
Gravel2 (3.3%)
↓-75.0%prior 8
Other (explain in narrative)1 (1.6%)

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

Vehicles & Demographics

Ford and Chevrolet were the most common vehicle makes involved in crashes during both periods. After combining variations in make names, the count of Chevrolet vehicles involved in crashes decreased from 33 in 2016 to 18 in 2017, while Ford vehicles decreased from 31 to 24. Regarding person demographics, the number of individuals aged 16-20 involved in crashes fell from 28 to 21. In contrast, involvement for the 45-54 age group increased from 19 persons in 2016 to 24 in 2017.

Top Vehicle Makes (123 vehicles)

1
FORD24 (19.5%)
↓-22.6%prior 31
2
CHEV12 (9.8%)
↓-29.4%prior 17
3
TOYT8 (6.5%)
4
DODG7 (5.7%)
↓-12.5%prior 8
5
DODGE7 (5.7%)
↓-22.2%prior 9
6
BUICK7 (5.7%)
↑40.0%prior 5
7
PONT6 (4.9%)
8
CHEVROLET6 (4.9%)
↓-62.5%prior 16
9
CHRY6 (4.9%)
10
TOYOTA4 (3.3%)

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

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

Sex Distribution (81 persons with recorded sex)

Male54 (66.7%)
↓-3.6%prior 56
Female27 (33.3%)
↓-40.0%prior 45

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: 81
  • Total persons involved: 156
  • Total vehicles involved: 123

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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