Yearly Traffic Safety Analysis

69 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Pocahontas County, total traffic crashes decreased from 83 in 2018 to 69 in 2019, a 16.9% reduction. Despite the drop in overall incidents, the most notable year-over-year shift was a significant decrease in fatalities from 6 to 1, contrasted by an increase in total injuries from 26 to 37.

69

▼ -16.9%was 83

Total Crash Events

1

▼ -83.3%was 6

Persons Killed

37

▲ 42.3%was 26

Persons Injured

1

▼ -66.7%was 3

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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

The overall trend in Pocahontas County shows a decrease in the total number of crashes, which fell by 16.9% from 83 in 2018 to 69 in 2019. While total fatalities dropped from 6 to 1, the number of people injured in crashes increased by 42.3%, rising from 26 to 37 over the same period.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 6↓-83.3%

37

Motorists Injured

Prior: 25↑48.0%

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 timing of crashes shifted between the two periods. In 2019, the peak day for crashes was Tuesday with 15 incidents, and the peak hour was 3 p.m. with 7 incidents. This contrasts with 2018, when the peak day was Friday (15 crashes) and the peak hour was 11 a.m. (11 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

While total crashes declined, the severity profile changed significantly. The number of fatal crashes decreased from 3 in 2018 to 1 in 2019, and total fatalities fell from 6 to 1. Conversely, crashes resulting in serious injuries tripled, increasing from 3 incidents in 2018 to 9 in 2019. Overall, the number of people injured rose from 26 to 37.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.4%
↓-66.7%prior 3
Serious Injury9serious injury crashes13%
↑200.0%prior 3
Minor Injury7minor injury crashes10.1%
↓-22.2%prior 9
Possible Injury7possible injury crashes10.1%
↓-30.0%prior 10
No Injury45no injury crashes65.2%
↓-22.4%prior 58

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

In 2019, collisions involving an animal became the leading contributing factor, with the count of such incidents increasing from 10 to 15. "Lost Control," which was the top factor in 2018 with 12 crashes, saw its count decrease to 5 in 2019. Incidents where a driver failed to yield the right-of-way from a stop sign more than doubled in count, rising from 3 in 2018 to 7 in 2019.

Officer-Reported Primary Contributing Cause

Animal15 (21.7%)↑50.0%prior 10
FTYROW: From stop sign7 (10.1%)
Lost Control5 (7.2%)↓-58.3%prior 12
Ran off road - straight4 (5.8%)
Failed to keep in proper lane3 (4.3%)
Driver Distraction: Other interior distraction3 (4.3%)
Driver Distraction: Inattentive/lost in thought3 (4.3%)
Made improper turn3 (4.3%)
Crossed centerline (undivided)3 (4.3%)
Ran Stop Sign3 (4.3%)

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 conditions under which crashes occurred were largely consistent year-over-year, with the majority in both 2019 (60.9%) and 2018 (65.1%) happening in clear weather on dry roads. Crashes on roads with ice or frost decreased from 10 incidents in 2018 to 4 in 2019. The proportion of crashes in daylight conditions fell from 67.5% in 2018 to 56.5% in 2019.

Weather

Clear42 (67.7%)
↓-22.2%prior 54
Cloudy11 (17.7%)
↓-15.4%prior 13
Rain4 (6.5%)
Blowing Snow2 (3.2%)
Freezing rain/drizzle2 (3.2%)
Snow1 (1.6%)

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

Lighting

Daylight39 (62.9%)
↓-30.4%prior 56
Dark - roadway not lighted15 (24.2%)
→0.0%prior 15
Dusk4 (6.5%)
Dawn2 (3.2%)
Dark - roadway lighted2 (3.2%)

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

Road Surface

Dry45 (73.8%)
↓-13.5%prior 52
Wet5 (8.2%)
↓-28.6%prior 7
Ice/frost4 (6.6%)
↓-60.0%prior 10
Gravel4 (6.6%)
Snow2 (3.3%)
↓-66.7%prior 6
Mud, dirt1 (1.6%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes were consistent, with Ford and Chevrolet leading in both years; Ford-involved crashes decreased from 23 to 20 and Chevrolet from 29 to 26. The age demographics of people involved also remained stable. The 55-64 and 65+ age groups were the most represented in both periods, with counts of 25 each in 2018 and 27 each in 2019.

Top Vehicle Makes (102 vehicles)

1
FORD20 (19.6%)
↓-13.0%prior 23
2
CHEV14 (13.7%)
↑27.3%prior 11
3
CHEVROLET12 (11.8%)
↓-33.3%prior 18
4
PETERBILT7 (6.9%)
5
DODGE5 (4.9%)
↓-16.7%prior 6
6
DODG3 (2.9%)
↓-40.0%prior 5
7
GMC3 (2.9%)
↓-50.0%prior 6
8
FREIGHTLINER3 (2.9%)
↓-50.0%prior 6
9
RAM3 (2.9%)
10
CHRYSLER2 (2%)

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

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

Sex Distribution (98 persons with recorded sex)

Male62 (63.3%)
↑8.8%prior 57
Female36 (36.7%)
↓-2.7%prior 37

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: 69
  • Total persons involved: 149
  • Total vehicles involved: 102

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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