Yearly Traffic Safety Analysis

720 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Warren County, total crashes increased from 696 in 2016 to 720 in 2017, a rise of 3.5%. Despite the overall increase in collisions, the number of fatalities saw a significant decrease, falling from 8 in the prior period to 5 in the current period. The number of fatal crashes was halved, dropping from 8 to 4 year-over-year.

720

▲ 3.4%was 696

Total Crash Events

5

▼ -37.5%was 8

Persons Killed

236

▼ -2.9%was 243

Persons Injured

4

▼ -50.0%was 8

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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

Crash trends in Warren County show a mixed picture year-over-year. While the total number of crashes rose by 3.5% from 696 to 720, both injuries and fatalities declined. Total injuries decreased from 243 to 236, and total fatalities fell from 8 to 5.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 8↓-37.5%

1

Pedestrians Injured

Prior: 0→%

5

Cyclists Injured

Prior: 2↑150.0%

230

Motorists Injured

Prior: 239↓-3.8%

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 temporal patterns of crashes showed a notable shift in the peak hour between the two periods. While Friday remained the busiest day for crashes in both 2016 (123 crashes) and 2017 (130 crashes), the peak hour for collisions moved from 4 p.m. in the prior year (65 crashes) to 7 a.m. in the current year (68 crashes). This indicates a shift from the afternoon commute to the morning commute as the most common time for 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 decreased from 2016 to 2017. The number of fatal crashes was cut in half, from 8 to 4, and serious injury crashes also declined from 22 to 18. Consequently, the proportion of crashes resulting in no injuries increased from 71.7% to 74.3% of all incidents. Crashes classified with 'Possible Injury' were the only injury category to see an increase, rising from 90 to 100 incidents.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.6%
↓-50.0%prior 8
Serious Injury18serious injury crashes2.5%
↓-18.2%prior 22
Minor Injury63minor injury crashes8.8%
↓-18.2%prior 77
Possible Injury100possible injury crashes13.9%
↑11.1%prior 90
No Injury535no injury crashes74.3%
↑7.2%prior 499

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 an 'Animal' remained the leading contributing factor in both periods, with the count increasing from 129 in 2016 to 143 in 2017. 'Lost Control' also held its rank as the second most common factor, rising from 59 to 70 incidents. Notably, crashes attributed to 'FTYROW: From stop sign' decreased from 47 to 39 incidents, dropping from the third-ranked factor to the seventh. 'Driving too fast for conditions' saw an increase from 38 to 43 incidents, moving into the top five factors for the current period.

Officer-Reported Primary Contributing Cause

Animal143 (19.9%)↑10.9%prior 129
Lost Control70 (9.7%)↑18.6%prior 59
Ran off road - left44 (6.1%)↑10.0%prior 40
Driving too fast for conditions43 (6%)↑13.2%prior 38
Followed too close40 (5.6%)↓-11.1%prior 45
Other (explain in narrative): Other40 (5.6%)↑37.9%prior 29
FTYROW: From stop sign39 (5.4%)↓-17.0%prior 47
Ran off road - straight29 (4%)↓-19.4%prior 36
FTYROW: Making left turn27 (3.8%)↓-10.0%prior 30
Driver Distraction: Other interior distraction26 (3.6%)↑23.8%prior 21

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 on dry roads during daylight hours, with these proportions remaining relatively stable. There was a decrease in crashes occurring under adverse winter conditions, as incidents on roads with snow or ice fell from 60 in 2016 to 44 in 2017. Similarly, crashes during snowy or freezing rain conditions decreased from 42 to 28 incidents year-over-year.

Weather

Clear404 (65.7%)
↑7.4%prior 376
Cloudy137 (22.3%)
↓-8.1%prior 149
Rain39 (6.3%)
↑34.5%prior 29
Snow15 (2.4%)
↓-48.3%prior 29
Freezing rain/drizzle11 (1.8%)
→0.0%prior 11
Fog, smoke, smog5 (0.8%)
↓-50.0%prior 10
Blowing Snow2 (0.3%)
Severe Winds1 (0.2%)
Other (explain in narrative)1 (0.2%)

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

Lighting

Daylight413 (66.9%)
↑2.5%prior 403
Dark - roadway not lighted105 (17.0%)
↓-16.0%prior 125
Dark - roadway lighted49 (7.9%)
↓-9.3%prior 54
Dusk29 (4.7%)
↑123.1%prior 13
Dawn21 (3.4%)
↑61.5%prior 13

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

Road Surface

Dry474 (76.9%)
↑5.8%prior 448
Wet65 (10.6%)
↓-9.7%prior 72
Gravel29 (4.7%)
↑16.0%prior 25
Snow26 (4.2%)
↓-7.1%prior 28
Ice/frost18 (2.9%)
↓-43.8%prior 32
Mud, dirt3 (0.5%)
Slush1 (0.2%)
↓-80.0%prior 5

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

Vehicles & Demographics

Chevrolet, Ford, and Dodge remained the three most common vehicle makes involved in crashes for both years, with each seeing an increase in total involvement. Analysis of persons involved in crashes shows a shift in age demographics, with the 16-20 age group seeing its count increase from 171 to 223. Conversely, the number of individuals aged 65 and older involved in crashes decreased from 145 to 125.

Top Vehicle Makes (1,147 vehicles)

1
FORD201 (17.5%)
↑1.5%prior 198
2
CHEV169 (14.7%)
↑20.7%prior 140
3
CHEVROLET82 (7.1%)
↓-13.7%prior 95
4
DODG66 (5.8%)
↑24.5%prior 53
5
TOYT50 (4.4%)
↑66.7%prior 30
6
DODGE40 (3.5%)
↓-20.0%prior 50
7
JEEP34 (3%)
↓-22.7%prior 44
8
NISS29 (2.5%)
↑70.6%prior 17
9
GMC29 (2.5%)
↓-3.3%prior 30
10
PONT25 (2.2%)
↑31.6%prior 19

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

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

Sex Distribution (870 persons with recorded sex)

Male490 (56.3%)
↓-0.8%prior 494
Female380 (43.7%)
→0.0%prior 380

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: 720
  • Total persons involved: 1,330
  • Total vehicles involved: 1,147

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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