Yearly Traffic Safety Analysis

201 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Allamakee County, total crashes rose from 186 in 2016 to 201 in 2017, an 8.1% increase. Despite this rise in total incidents, the outcomes were less severe, with total injuries decreasing by 23.9% from 71 to 54 and fatalities dropping from 3 to 2. The most notable underlying change was a nearly 50% reduction in crashes involving driving under the influence, which fell from 13 to 7.

201

▲ 8.1%was 186

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

54

▼ -23.9%was 71

Persons Injured

2

▼ -33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

The overall trend shows an increase in the total number of crashes, rising by 8.1% from 186 in 2016 to 201 in 2017. However, this was accompanied by a notable decrease in crash severity. Fatalities declined from 3 to 2, and the number of people injured fell from 71 to 54 year-over-year.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 3↓-33.3%

54

Motorists Injured

Prior: 69↓-21.7%

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

Temporal crash patterns shifted between the two periods. The peak day for crashes moved from Sunday (42 incidents) in 2016 to Friday (38 incidents) in 2017. Similarly, the peak hour for incidents shifted from the morning at 7 a.m. (14 crashes) in the prior year to the evening commute at 5 p.m. (20 crashes) in the current year.

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 total number of crashes increased, the overall severity of incidents trended downward. Fatal crashes fell from 3 to 2, and the total number of people injured dropped from 71 to 54. However, the composition of injury crashes changed, with serious injury crashes increasing from 5 to 13, while minor injury crashes fell from 24 to 8. The share of crashes resulting in no injuries increased from 69.4% to 74.6% of all incidents.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1%
↓-33.3%prior 3
Serious Injury13serious injury crashes6.5%
↑160.0%prior 5
Minor Injury8minor injury crashes4%
↓-66.7%prior 24
Possible Injury28possible injury crashes13.9%
↑12.0%prior 25
No Injury150no injury crashes74.6%
↑16.3%prior 129

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 an animal remained the leading contributing factor in both years, with counts increasing slightly from 62 to 65 incidents. The most significant year-over-year changes were seen in other factors; crashes attributed to 'Driving too fast for conditions' increased from 5 to 13, and incidents involving 'FTYROW: From stop sign' rose from 7 to 13. Conversely, crashes where 'Ran off road - straight' was a factor decreased from 18 to 14.

Officer-Reported Primary Contributing Cause

Animal65 (32.3%)↑4.8%prior 62
Lost Control24 (11.9%)↑14.3%prior 21
Ran off road - straight14 (7%)↓-22.2%prior 18
FTYROW: From stop sign13 (6.5%)↑85.7%prior 7
Driving too fast for conditions13 (6.5%)↑160.0%prior 5
Other (explain in narrative): Other10 (5%)↑100.0%prior 5
Ran off road - left10 (5%)↑11.1%prior 9
FTYROW: Making left turn7 (3.5%)
FTYROW: From driveway5 (2.5%)
Ran Stop Sign4 (2%)

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

Road & Environmental Conditions

A notable shift occurred in lighting conditions, with the proportion of crashes in daylight increasing from 43.5% (81 crashes) in 2016 to 56.7% (114 crashes) in 2017. Correspondingly, crashes on dark, unlit roadways decreased from 52 to 31. Regarding road surface, incidents on icy or frosty roads doubled, increasing from 6 to 12 year-over-year, while crashes on dry roads remained stable with 110 incidents compared to 108.

Weather

Clear109 (68.1%)
↓-9.2%prior 120
Cloudy25 (15.6%)
↑31.6%prior 19
Snow10 (6.3%)
↑42.9%prior 7
Rain8 (5.0%)
↑33.3%prior 6
Freezing rain/drizzle4 (2.5%)
Fog, smoke, smog3 (1.9%)
Blowing Snow1 (0.6%)

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

Lighting

Daylight114 (70.4%)
↑40.7%prior 81
Dark - roadway not lighted31 (19.1%)
↓-40.4%prior 52
Dusk9 (5.6%)
↓-35.7%prior 14
Dark - roadway lighted7 (4.3%)
↓-12.5%prior 8
Dark - unknown roadway lighting1 (0.6%)

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

Road Surface

Dry110 (68.3%)
↑1.9%prior 108
Wet16 (9.9%)
↑14.3%prior 14
Ice/frost12 (7.5%)
↑100.0%prior 6
Snow12 (7.5%)
→0.0%prior 12
Gravel9 (5.6%)
↓-47.1%prior 17
Sand1 (0.6%)
Slush1 (0.6%)

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

Vehicles & Demographics

The demographic profile of persons involved in crashes shifted toward older age groups. The share of individuals aged 55-64 rose from 13.2% (37 people) to 17.3% (54 people), and the 65+ age group's involvement increased from 12.9% (36 people) to 16.3% (51 people). The top vehicle makes involved in crashes, Chevrolet and Ford, maintained their primary rankings with a moderate increase in total counts, reflecting the overall rise in incidents.

Top Vehicle Makes (277 vehicles)

1
CHEV41 (14.8%)
↑78.3%prior 23
2
FORD39 (14.1%)
↑21.9%prior 32
3
CHEVROLET32 (11.6%)
↓-23.8%prior 42
4
DODGE16 (5.8%)
↑23.1%prior 13
5
GMC13 (4.7%)
6
HONDA9 (3.2%)
7
TOYOTA9 (3.2%)
8
JEEP9 (3.2%)
→0.0%prior 9
9
DODG8 (2.9%)
↓-38.5%prior 13
10
NR6 (2.2%)

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

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

Sex Distribution (214 persons with recorded sex)

Male125 (58.4%)
↑11.6%prior 112
Female89 (41.6%)
↑39.1%prior 64

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: 201
  • Total persons involved: 312
  • Total vehicles involved: 277

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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