Yearly Traffic Safety Analysis

395 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Boone County, total crashes decreased by 2.5% from 405 in 2016 to 395 in 2017. During this period, fatalities were halved from two to one, though the total number of injuries rose by 8.1% from 149 to 161. The most notable year-over-year shift was a 19% increase in crashes involving an animal, which rose from 84 to 100 incidents.

395

▼ -2.5%was 405

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

161

▲ 8.1%was 149

Persons Injured

1

▼ -50.0%was 2

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 crashes in Boone County saw a minor decrease of 2.5%, falling from 405 in 2016 to 395 in 2017. While total fatalities were cut in half from two to one, the number of people injured in crashes increased by 8.1%, rising from 149 to 161. This indicates a slight decline in crash frequency but an increase in non-fatal injury outcomes.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 2↓-50.0%

2

Pedestrians Injured

Prior: 0→%

3

Cyclists Injured

Prior: 2↑50.0%

156

Motorists Injured

Prior: 146↑6.8%

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 daily and hourly patterns of crashes shifted between the two periods. The peak day for crashes moved from Friday (71 incidents) in 2016 to Monday (67 incidents) in 2017. The peak hour for collisions also shifted later in the afternoon, moving from 3 p.m. in the prior year (43 crashes) to 5 p.m. in the current year (32 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

The severity of crashes showed a mixed trend year-over-year. The number of fatal crashes decreased from two in 2016 to one in 2017, and serious injury crashes also declined slightly from 16 to 14. However, crashes resulting in minor injuries saw a substantial increase, rising from 29 incidents (7.2% of total) in 2016 to 52 incidents (13.2% of total) in 2017.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
↓-50.0%prior 2
Serious Injury14serious injury crashes3.5%
↓-12.5%prior 16
Minor Injury52minor injury crashes13.2%
↑79.3%prior 29
Possible Injury50possible injury crashes12.7%
↓-18.0%prior 61
No Injury278no injury crashes70.4%
↓-6.4%prior 297

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

The leading contributing factors remained consistent, with collisions involving an animal being the top cause in both years. The count of animal-related crashes grew by 19%, from 84 incidents in 2016 to 100 in 2017. In contrast, crashes attributed to driving too fast for conditions were halved, dropping from 26 incidents to 13. "Followed too close" also saw a decrease in count from 29 to 23 incidents.

Officer-Reported Primary Contributing Cause

Animal100 (25.3%)↑19.0%prior 84
Lost Control37 (9.4%)↑12.1%prior 33
FTYROW: From stop sign34 (8.6%)↑3.0%prior 33
Ran off road - straight24 (6.1%)↑9.1%prior 22
Followed too close23 (5.8%)↓-20.7%prior 29
Ran Stop Sign15 (3.8%)→0.0%prior 15
Other (explain in narrative): Other13 (3.3%)↓-13.3%prior 15
Driving too fast for conditions13 (3.3%)↓-50.0%prior 26
FTYROW: Making left turn12 (3%)↓-36.8%prior 19
Ran off road - left12 (3%)↓-7.7%prior 13

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 shift in crash conditions between the two periods. The proportion of crashes occurring in daylight decreased from 60.5% of all crashes in 2016 to 54.2% in 2017, while crashes on unlit dark roadways increased from 46 to 58 incidents. Collisions reported during freezing rain or drizzle more than doubled, increasing from 5 incidents in 2016 to 15 in 2017.

Weather

Clear175 (56.6%)
↓-12.1%prior 199
Cloudy85 (27.5%)
→0.0%prior 85
Freezing rain/drizzle15 (4.9%)
↑200.0%prior 5
Rain12 (3.9%)
↓-14.3%prior 14
Snow9 (2.9%)
↓-47.1%prior 17
Blowing Snow7 (2.3%)
↑16.7%prior 6
Severe Winds2 (0.6%)
Sleet, hail2 (0.6%)
Fog, smoke, smog2 (0.6%)

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

Lighting

Daylight214 (69.5%)
↓-12.7%prior 245
Dark - roadway not lighted58 (18.8%)
↑26.1%prior 46
Dark - roadway lighted18 (5.8%)
↓-28.0%prior 25
Dawn9 (2.9%)
↓-18.2%prior 11
Dusk9 (2.9%)
↑80.0%prior 5

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

Road Surface

Dry228 (73.5%)
↑0.4%prior 227
Ice/frost34 (11.0%)
→0.0%prior 34
Wet26 (8.4%)
↑4.0%prior 25
Snow13 (4.2%)
↓-40.9%prior 22
Gravel7 (2.3%)
↓-63.2%prior 19
Slush2 (0.6%)

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

Vehicles & Demographics

The demographics of persons involved in crashes showed some shifts, with the number of individuals aged 65 and older increasing from 62 to 72, while the 16-20 age group decreased from 118 to 103. In terms of vehicle makes, Ford (106 vehicles) and Chevrolet (141 vehicles) were the most common types involved in crashes in 2017. The number of Fords involved was unchanged from 2016, while the count of Chevrolets decreased from 156.

Top Vehicle Makes (601 vehicles)

1
FORD106 (17.6%)
→0.0%prior 106
2
CHEV91 (15.1%)
↑23.0%prior 74
3
CHEVROLET50 (8.3%)
↓-39.0%prior 82
4
DODG33 (5.5%)
↑26.9%prior 26
5
TOYT28 (4.7%)
↑115.4%prior 13
6
JEEP23 (3.8%)
↑21.1%prior 19
7
GMC20 (3.3%)
↑11.1%prior 18
8
BUIC18 (3%)
↑28.6%prior 14
9
DODGE17 (2.8%)
↓-41.4%prior 29
10
TOYOTA17 (2.8%)
↑30.8%prior 13

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

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

Sex Distribution (438 persons with recorded sex)

Male254 (58.0%)
↑2.0%prior 249
Female184 (42.0%)
↓-22.0%prior 236

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: 395
  • Total persons involved: 716
  • Total vehicles involved: 601

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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