Yearly Traffic Safety Analysis

324 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Poweshiek County recorded 324 total crashes, a 6.9% decrease from the 348 crashes reported in 2018. While overall collisions declined, crashes involving driving under the influence (DUI) increased by 50%, rising from 8 incidents in 2018 to 12 in 2019. The number of people injured also fell from 113 to 105, while fatalities remained constant at 3.

324

▼ -6.9%was 348

Total Crash Events

3

Persons Killed

105

▼ -7.1%was 113

Persons Injured

3

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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Poweshiek County saw a downward trend, decreasing from 348 in 2018 to 324 in 2019, a 6.9% reduction in total incidents. This trend was mirrored in injuries, which fell by 7.1% from 113 to 105. The number of fatalities remained unchanged at 3 for both years.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 3→0.0%

2

Cyclists Injured

Prior: 1↑100.0%

103

Motorists Injured

Prior: 110↓-6.4%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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. While Monday remained the day with the highest number of crashes in both 2019 (57 crashes) and 2018 (65 crashes), the peak hour for incidents moved. In 2019, the most crashes occurred at 3 p.m. with 31 incidents, a shift from the 1 p.m. peak observed in 2018, which saw 34 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity distributions showed mixed changes year-over-year. The number of fatal crashes was stable at 3 for both 2019 and 2018, though the fatal crash rate as a percentage of all crashes increased slightly from 0.86% to 0.93%. The proportion of minor injury crashes grew from 6.3% of all crashes in 2018 to 9.9% in 2019, while the share of serious injury crashes declined from 2.0% to 1.5%.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.9%
→0.0%prior 3
Serious Injury5serious injury crashes1.5%
↓-28.6%prior 7
Minor Injury32minor injury crashes9.9%
↑45.5%prior 22
Possible Injury38possible injury crashes11.7%
↓-24.0%prior 50
No Injury246no injury crashes75.9%
↓-7.5%prior 266

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes remained consistent, though their frequencies shifted. Collisions involving an animal were the top factor in both years but decreased in count by 31.9%, from 69 incidents in 2018 to 47 in 2019. Conversely, crashes attributed to 'driving too fast for conditions' increased by 12.5% from a count of 40 to 45. The third-ranked factor, 'ran off road - straight,' saw a small increase from 32 to 34 incidents.

Officer-Reported Primary Contributing Cause

Animal47 (14.5%)↓-31.9%prior 69
Driving too fast for conditions45 (13.9%)↑12.5%prior 40
Ran off road - straight34 (10.5%)↑6.3%prior 32
Lost Control23 (7.1%)↓-17.9%prior 28
FTYROW: From stop sign18 (5.6%)↓-21.7%prior 23
Other (explain in narrative): Other16 (4.9%)↑45.5%prior 11
Ran off road - left15 (4.6%)↓-21.1%prior 19
Followed too close13 (4%)↓-27.8%prior 18
FTYROW: Making left turn9 (2.8%)↓-10.0%prior 10
Improper or erratic lane changing9 (2.8%)→0.0%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in daylight remained stable at approximately 59% in both 2019 and 2018. However, there was a notable shift in reported road surface conditions, with the share of crashes on dry surfaces decreasing from 55.5% in 2018 to 48.8% in 2019. Concurrently, incidents on snow-covered roads rose from 27 to 41, and crashes on icy or frosty roads more than doubled from 16 in 2018 to 38 in 2019.

Weather

Clear151 (54.3%)
↑2.7%prior 147
Cloudy51 (18.3%)
↓-27.1%prior 70
Snow28 (10.1%)
↑7.7%prior 26
Blowing Snow24 (8.6%)
↑300.0%prior 6
Rain12 (4.3%)
↓-36.8%prior 19
Freezing rain/drizzle8 (2.9%)
↓-42.9%prior 14
Severe Winds2 (0.7%)
Sleet, hail1 (0.4%)
↓-80.0%prior 5
Fog, smoke, smog1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight191 (68.5%)
↓-8.2%prior 208
Dark - roadway not lighted53 (19.0%)
↓-5.4%prior 56
Dark - roadway lighted22 (7.9%)
↑120.0%prior 10
Dawn6 (2.2%)
↓-25.0%prior 8
Dusk6 (2.2%)
↓-14.3%prior 7
Dark - unknown roadway lighting1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry158 (56.8%)
↓-18.1%prior 193
Snow41 (14.7%)
↑51.9%prior 27
Ice/frost38 (13.7%)
↑137.5%prior 16
Wet27 (9.7%)
↓-15.6%prior 32
Gravel7 (2.5%)
↓-36.4%prior 11
Other (explain in narrative)2 (0.7%)
Slush2 (0.7%)
↓-84.6%prior 13
Mud, dirt2 (0.7%)
Sand1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Chevrolet and Ford leading in both years. In 2019, a combined 106 Chevrolet vehicles and 68 Ford vehicles were involved, compared to 105 Chevrolet and 93 Ford vehicles in 2018. An analysis of persons involved in crashes shows a shift in the dominant age demographic; the 26-34 age group was the most represented in 2019 with 121 individuals, whereas the 35-44 age group was the largest in 2018 with 113 individuals.

Top Vehicle Makes (489 vehicles)

1
FORD68 (13.9%)
↓-26.9%prior 93
2
CHEV68 (13.9%)
↓-4.2%prior 71
3
CHEVROLET38 (7.8%)
↑11.8%prior 34
4
DODG24 (4.9%)
↑33.3%prior 18
5
TOYO18 (3.7%)
→0.0%prior 18
6
DODGE16 (3.3%)
↑23.1%prior 13
7
FREIGHTLINER15 (3.1%)
→0.0%prior 15
8
GMC15 (3.1%)
↑7.1%prior 14
9
BUIC14 (2.9%)
↑133.3%prior 6
10
NISSAN12 (2.5%)
↓-7.7%prior 13

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (456 persons with recorded sex)

Male266 (58.3%)
↓-4.0%prior 277
Female190 (41.7%)
↑16.6%prior 163

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 324
  • Total persons involved: 664
  • Total vehicles involved: 489

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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