Yearly Traffic Safety Analysis

348 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Poweshiek County recorded 348 total vehicle crashes, a 9.4% increase from the 318 crashes documented in 2017. While the total number of incidents rose, fatalities decreased from 7 to 3 year-over-year. The most significant shift was the number of people injured, which increased by 39.5% from 81 in 2017 to 113 in 2018.

348

▲ 9.4%was 318

Total Crash Events

3

▼ -57.1%was 7

Persons Killed

113

▲ 39.5%was 81

Persons Injured

3

▼ -25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety data for Poweshiek County indicates a rising trend in the total number of crashes, which increased by 9.4% from 318 to 348 year-over-year. This increase in collisions was accompanied by a 39.5% rise in total injuries, from 81 to 113. In contrast, the number of fatalities resulting from these crashes fell from 7 in 2017 to 3 in 2018.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 6↓-50.0%

2

Pedestrians Injured

Prior: 1↑100.0%

1

Cyclists Injured

Prior: 2↓-50.0%

110

Motorists Injured

Prior: 77↑42.9%

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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. The peak day for collisions moved from Friday (66 incidents) in 2017 to Monday (65 incidents) in 2018. The single hour with the most crashes also changed, shifting from 5 p.m. in the prior year (25 crashes) to 1 p.m. in the current year (34 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity outcomes showed a mixed trend year-over-year. The number of fatal crashes decreased from 4 in 2017 to 3 in 2018, with the share of fatal crashes dropping from 1.3% to 0.9% of all incidents. However, the total count of crashes resulting in any level of injury rose from 66 to 79. Consequently, the proportion of crashes with no reported injuries decreased from 78.0% in 2017 to 76.4% in 2018.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.9%
↓-25.0%prior 4
Serious Injury7serious injury crashes2%
↓-12.5%prior 8
Minor Injury22minor injury crashes6.3%
↓-8.3%prior 24
Possible Injury50possible injury crashes14.4%
↑47.1%prior 34
No Injury266no injury crashes76.4%
↑7.3%prior 248

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in both years, with the count increasing from 60 incidents in 2017 to 69 in 2018. The most significant change was the rise of crashes attributed to 'Driving too fast for conditions,' which jumped from 27 to 40 incidents, becoming the second-most common factor in 2018. Conversely, crashes due to 'Lost Control' decreased in count from 33 to 28.

Officer-Reported Primary Contributing Cause

Animal69 (19.8%)↑15.0%prior 60
Driving too fast for conditions40 (11.5%)↑48.1%prior 27
Ran off road - straight32 (9.2%)↓-5.9%prior 34
Lost Control28 (8%)↓-15.2%prior 33
FTYROW: From stop sign23 (6.6%)↑91.7%prior 12
Ran off road - left19 (5.5%)↑46.2%prior 13
Followed too close18 (5.2%)↑63.6%prior 11
Other (explain in narrative): Other11 (3.2%)↓-56.0%prior 25
Ran Stop Sign11 (3.2%)↓-8.3%prior 12
FTYROW: Making left turn10 (2.9%)↑25.0%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in daylight increased, accounting for 59.8% of incidents in 2018 compared to 51.6% in 2017. While crashes on dry roads increased from 180 to 193, the number of crashes in clear weather decreased from 166 to 147. This was offset by a rise in crashes during cloudy conditions, which increased from 45 to 70.

Weather

Clear147 (50.2%)
↓-11.4%prior 166
Cloudy70 (23.9%)
↑55.6%prior 45
Snow26 (8.9%)
↓-7.1%prior 28
Rain19 (6.5%)
↑26.7%prior 15
Freezing rain/drizzle14 (4.8%)
↑180.0%prior 5
Blowing Snow6 (2.0%)
Sleet, hail5 (1.7%)
Fog, smoke, smog3 (1.0%)
Severe Winds2 (0.7%)
Other (explain in narrative)1 (0.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash

Lighting

Daylight208 (71.0%)
↑26.8%prior 164
Dark - roadway not lighted56 (19.1%)
↓-20.0%prior 70
Dark - roadway lighted10 (3.4%)
↓-41.2%prior 17
Dawn8 (2.7%)
→0.0%prior 8
Dusk7 (2.4%)
↑16.7%prior 6
Dark - unknown roadway lighting4 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field

Road Surface

Dry193 (65.9%)
↑7.2%prior 180
Wet32 (10.9%)
→0.0%prior 32
Snow27 (9.2%)
↓-12.9%prior 31
Ice/frost16 (5.5%)
Slush13 (4.4%)
Gravel11 (3.8%)
↓-38.9%prior 18
Other (explain in narrative)1 (0.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Road surface condition field

Vehicles & Demographics

An analysis of persons involved in crashes shows a notable shift in age distribution, with the 55-64 age group's involvement increasing from 51 individuals in 2017 to 97 in 2018. Regarding vehicle makes, Chevrolet and Ford remained the two most frequently involved brands in both years. The number of Toyota vehicles in crashes more than doubled from 19 to 41, while the count of Dodge vehicles involved decreased from 36 to 31.

Top Vehicle Makes (536 vehicles)

1
FORD93 (17.4%)
↑25.7%prior 74
2
CHEV71 (13.2%)
↑1.4%prior 70
3
CHEVROLET34 (6.3%)
↓-15.0%prior 40
4
JEEP21 (3.9%)
↑31.3%prior 16
5
DODG18 (3.4%)
↓-14.3%prior 21
6
TOYO18 (3.4%)
↑100.0%prior 9
7
TOYOTA15 (2.8%)
↑50.0%prior 10
8
FREIGHTLINER15 (2.8%)
↑66.7%prior 9
9
GMC14 (2.6%)
↑7.7%prior 13
10
NISSAN13 (2.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records

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

Sex Distribution (440 persons with recorded sex)

Male277 (63.0%)
↑27.1%prior 218
Female163 (37.0%)
↑15.6%prior 141

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 348
  • Total persons involved: 654
  • Total vehicles involved: 536

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: 2018." Published September 9, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2018-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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