Yearly Traffic Safety Analysis

223 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Floyd County recorded 223 total crashes, a 20.1% decrease from the 279 crashes reported in 2016. This overall reduction was accompanied by a decrease in total injuries from 84 to 71 and a slight drop in fatalities from 6 to 5. The most notable year-over-year change was a significant reduction in the number of crashes attributed to animals, which fell from 97 incidents in 2016 to 68 in 2017.

223

▼ -20.1%was 279

Total Crash Events

5

▼ -16.7%was 6

Persons Killed

71

▼ -15.5%was 84

Persons Injured

4

▼ -20.0%was 5

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

Overall, traffic crashes in Floyd County showed a downward trend from 2016 to 2017. The total number of crashes decreased by 20.1%, from 279 to 223. This decline was accompanied by a 15.5% reduction in total injuries (from 84 to 71) and one fewer fatality (5 in 2017 versus 6 in 2016).

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 1↓-100.0%

5

Motorists Killed

Prior: 4↑25.0%

3

Cyclists Injured

Prior: 2↑50.0%

68

Motorists Injured

Prior: 81↓-16.0%

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 Monday with 36 incidents, a change from 2016 when Friday was the peak day with 52 crashes. The peak hour for collisions also moved slightly, from 6 p.m. in 2016 (22 crashes) to 7 p.m. in 2017 (16 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 some changes year-over-year, although the fatal crash rate remained stable at 1.8% in both 2017 (4 fatal crashes) and 2016 (5 fatal crashes). The number of serious injury crashes was more than halved, dropping from 7 in 2016 to 3 in 2017. The proportion of crashes resulting in no injury increased from 76.0% in the prior year to 78.0% in the current year.

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

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.8%
↓-20.0%prior 5
Serious Injury3serious injury crashes1.3%
↓-57.1%prior 7
Minor Injury23minor injury crashes10.3%
↓-8.0%prior 25
Possible Injury19possible injury crashes8.5%
↓-36.7%prior 30
No Injury174no injury crashes78%
↓-17.9%prior 212

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 for crashes remained consistent across both years, though their counts decreased. Collisions involving an 'Animal' was the top factor in both 2017 (68 crashes) and 2016 (97 crashes), representing a 29.9% decrease in count. 'Lost Control' (16 crashes in 2017 vs. 20 in 2016) and 'Ran off road - straight' (12 crashes in 2017 vs. 16 in 2016) also remained the second and third most common factors, respectively, with both seeing a reduction in incidents.

Officer-Reported Primary Contributing Cause

Animal68 (30.5%)↓-29.9%prior 97
Lost Control16 (7.2%)↓-20.0%prior 20
Ran off road - straight12 (5.4%)↓-25.0%prior 16
Ran Stop Sign11 (4.9%)↑83.3%prior 6
FTYROW: From stop sign11 (4.9%)↓-26.7%prior 15
Ran off road - left9 (4%)↓-10.0%prior 10
Driving too fast for conditions8 (3.6%)↓-42.9%prior 14
Driver Distraction: Other interior distraction6 (2.7%)↑20.0%prior 5
FTYROW: Making left turn6 (2.7%)
Operating vehicle in an reckless, erratic, careless, negligent manner6 (2.7%)↑20.0%prior 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. In 2017, the proportion of crashes on dry road surfaces increased to 51.6% from 44.8% in 2016. Correspondingly, crashes on adverse road surfaces like ice, snow, or wet pavement decreased, accounting for 19.7% of all crashes in 2017 compared to 25.1% in the previous year. The share of crashes happening in dark conditions saw a slight increase from 14.3% in 2016 to 17.0% in 2017.

Weather

Clear112 (67.1%)
↓-5.9%prior 119
Cloudy38 (22.8%)
↓-24.0%prior 50
Snow6 (3.6%)
↓-50.0%prior 12
Rain5 (3.0%)
Blowing Snow2 (1.2%)
↓-66.7%prior 6
Severe Winds2 (1.2%)
Fog, smoke, smog1 (0.6%)
↓-80.0%prior 5
Freezing rain/drizzle1 (0.6%)

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

Lighting

Daylight123 (73.7%)
↓-17.4%prior 149
Dark - roadway not lighted24 (14.4%)
↓-25.0%prior 32
Dark - roadway lighted14 (8.4%)
↑75.0%prior 8
Dark - unknown roadway lighting3 (1.8%)
Dawn2 (1.2%)
↓-66.7%prior 6
Dusk1 (0.6%)

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

Road Surface

Dry115 (69.3%)
↓-8.0%prior 125
Wet15 (9.0%)
↑15.4%prior 13
Ice/frost15 (9.0%)
↓-34.8%prior 23
Snow14 (8.4%)
↓-33.3%prior 21
Gravel6 (3.6%)
↓-53.8%prior 13
Sand1 (0.6%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Chevrolet, Ford, and Dodge being the top three in both 2017 and 2016, although the total number of vehicles involved for each make decreased. An analysis of persons involved in crashes shows a notable shift in age demographics. The number of individuals in the 21-25 age group involved in crashes decreased from 55 in 2016 to 34 in 2017, and the 65+ age group saw a decline from 72 persons to 57.

Top Vehicle Makes (340 vehicles)

1
FORD51 (15%)
↓-16.4%prior 61
2
CHEV41 (12.1%)
↓-6.8%prior 44
3
CHEVROLET33 (9.7%)
↓-26.7%prior 45
4
DODG12 (3.5%)
↑20.0%prior 10
5
GMC12 (3.5%)
↑9.1%prior 11
6
DODGE12 (3.5%)
↓-58.6%prior 29
7
BUIC11 (3.2%)
↓-21.4%prior 14
8
JEEP11 (3.2%)
↑22.2%prior 9
9
PONTIAC11 (3.2%)
10
CHRY9 (2.6%)
↑12.5%prior 8

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

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

Sex Distribution (243 persons with recorded sex)

Male150 (61.7%)
↓-20.6%prior 189
Female93 (38.3%)
↓-27.3%prior 128

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: 223
  • Total persons involved: 402
  • Total vehicles involved: 340

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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