Yearly Traffic Safety Analysis

318 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Poweshiek County, total traffic crashes remained nearly stable, with 318 incidents in 2017 compared to 321 in 2016, representing a decrease of less than 1%. While the overall crash volume was consistent, the most significant year-over-year change was a sharp increase in traffic fatalities, which rose from 2 in 2016 to 7 in 2017. Concurrently, total injuries reported in crashes declined from 101 to 81.

318

▼ -0.9%was 321

Total Crash Events

7

▲ 250.0%was 2

Persons Killed

81

▼ -19.8%was 101

Persons Injured

4

▲ 100.0%was 2

Fatal Crash Events

Note: "Persons Killed" (7) 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

The overall trend in crash volume was stable between 2016 and 2017, with a marginal decrease of just three incidents. However, the severity of outcomes worsened, as total fatalities increased by 250% from 2 to 7. In contrast, the number of people injured in crashes saw a notable decline of 19.8%, falling from 101 in the prior year to 81 in the current year.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

6

Motorists Killed

Prior: 2↑200.0%

0

Other Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

2

Cyclists Injured

Prior: 2→0.0%

77

Motorists Injured

Prior: 98↓-21.4%

1

Other Injured

Prior: 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 showed some shifts between the two periods. The peak hour for collisions remained the 5 p.m. hour in both 2017 and 2016, with 25 and 29 crashes respectively. However, the peak day for crashes moved from Saturday in 2016 (54 crashes) to Friday in 2017 (66 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 increased from 2016 to 2017. The number of fatal crashes doubled from 2 to 4, and the fatal crash rate rose from 0.62 to 1.26 per 100 crashes. Crashes resulting in serious injuries also increased, from 5 in 2016 to 8 in 2017. Conversely, crashes involving minor or possible injuries decreased from a combined total of 71 in 2016 to 58 in 2017.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 7 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.3%
↑100.0%prior 2
Serious Injury8serious injury crashes2.5%
↑60.0%prior 5
Minor Injury24minor injury crashes7.5%
↓-11.1%prior 27
Possible Injury34possible injury crashes10.7%
↓-22.7%prior 44
No Injury248no injury crashes78%
↑2.1%prior 243

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 were consistent across both years, with collisions involving an animal being the top cause in 2017 (60 crashes) and 2016 (56 crashes). "Ran off road - straight" and "Lost Control" also remained top factors. Notably, crashes attributed to failure to yield from a stop sign decreased from 22 incidents in 2016 to 12 in 2017. In contrast, crashes involving a driver running a stop sign increased from 3 to 12 during the same period.

Officer-Reported Primary Contributing Cause

Animal60 (18.9%)↑7.1%prior 56
Ran off road - straight34 (10.7%)↑3.0%prior 33
Lost Control33 (10.4%)↑17.9%prior 28
Driving too fast for conditions27 (8.5%)→0.0%prior 27
Other (explain in narrative): Other25 (7.9%)↑108.3%prior 12
Ran off road - left13 (4.1%)↑44.4%prior 9
FTYROW: From stop sign12 (3.8%)↓-45.5%prior 22
Ran Stop Sign12 (3.8%)
Followed too close11 (3.5%)↓-21.4%prior 14
FTYROW: Making left turn8 (2.5%)→0.0%prior 8

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 distribution of crashes across environmental conditions was largely similar year-over-year. Crashes on dry roads were most frequent in both 2017 (180) and 2016 (176), as were crashes during daylight hours (164 vs. 176). There was a significant decrease in crashes occurring on icy or frosty road surfaces, which fell from 22 incidents in 2016 to just 4 in 2017.

Weather

Clear166 (62.2%)
↑5.7%prior 157
Cloudy45 (16.9%)
↓-19.6%prior 56
Snow28 (10.5%)
↑7.7%prior 26
Rain15 (5.6%)
↑15.4%prior 13
Freezing rain/drizzle5 (1.9%)
↓-61.5%prior 13
Fog, smoke, smog4 (1.5%)
↓-20.0%prior 5
Blowing Snow3 (1.1%)
Severe Winds1 (0.4%)

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

Lighting

Daylight164 (61.7%)
↓-6.8%prior 176
Dark - roadway not lighted70 (26.3%)
↑2.9%prior 68
Dark - roadway lighted17 (6.4%)
↑21.4%prior 14
Dawn8 (3.0%)
↓-11.1%prior 9
Dusk6 (2.3%)
↓-14.3%prior 7
Dark - unknown roadway lighting1 (0.4%)

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

Road Surface

Dry180 (67.4%)
↑2.3%prior 176
Wet32 (12.0%)
→0.0%prior 32
Snow31 (11.6%)
↑19.2%prior 26
Gravel18 (6.7%)
↑63.6%prior 11
Ice/frost4 (1.5%)
↓-81.8%prior 22
Other (explain in narrative)1 (0.4%)
Mud, dirt1 (0.4%)

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

Vehicles & Demographics

The primary vehicle makes involved in crashes remained consistent, with Ford and Chevrolet models being the most common in both 2017 and 2016. The age demographics of persons involved in crashes also showed little change; the 26-34 age group was the most represented in both 2017 (98 people) and 2016 (96 people). The 16-20 age group saw a slight increase in involvement, from 70 individuals in 2016 to 75 in 2017.

Top Vehicle Makes (464 vehicles)

1
FORD74 (15.9%)
↓-17.8%prior 90
2
CHEV70 (15.1%)
↑29.6%prior 54
3
CHEVROLET40 (8.6%)
↓-34.4%prior 61
4
DODG21 (4.5%)
↑75.0%prior 12
5
JEEP16 (3.4%)
↑45.5%prior 11
6
DODGE15 (3.2%)
↓-16.7%prior 18
7
KIA14 (3%)
8
GMC13 (2.8%)
↓-23.5%prior 17
9
PONT11 (2.4%)
↑10.0%prior 10
10
TOYT10 (2.2%)
↑25.0%prior 8

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

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

Sex Distribution (359 persons with recorded sex)

Male218 (60.7%)
↓-0.5%prior 219
Female141 (39.3%)
↓-7.8%prior 153

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: 318
  • Total persons involved: 536
  • Total vehicles involved: 464

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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