Yearly Traffic Safety Analysis

85 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Pocahontas County recorded 85 total crashes, a 23.2% increase from the 69 crashes reported in 2019. This period also saw a rise in crash severity, with total fatalities doubling from one to two and total injuries increasing from 37 to 42. The most significant change in contributing factors was a 100% increase in the count of crashes attributed to 'Lost Control', rising from 5 to 10 incidents.

85

▲ 23.2%was 69

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

42

▲ 13.5%was 37

Persons Injured

2

▲ 100.0%was 1

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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Pocahontas County shows an upward trend year-over-year. Total crashes rose by 23.2%, from 69 in 2019 to 85 in 2020. Concurrently, the number of people injured increased by 13.5% from 37 to 42, and fatalities doubled from one to two.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 1↑100.0%

1

Pedestrians Injured

Prior: 0→%

41

Motorists Injured

Prior: 37↑10.8%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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 both consistency and change between the two periods. Tuesday remained the peak day for crashes in both 2019 and 2020, with 15 incidents recorded each year. However, the peak hour for crashes shifted later in the day, from the 3 p.m. hour in 2019 (7 crashes) to the 6 p.m. hour in 2020 (9 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased in 2020 compared to the prior year. The fatal crash rate rose from 1.45 to 2.35 per 100 crashes, with the number of fatal incidents doubling from one to two. The proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) also grew, accounting for 36.5% of all crashes in 2020, up from 33.3% in 2019.

Outcome by Severity (Crash Events)

Fatal2fatal crashes2.4%
↑100.0%prior 1
Serious Injury10serious injury crashes11.8%
↑11.1%prior 9
Minor Injury11minor injury crashes12.9%
↑57.1%prior 7
Possible Injury10possible injury crashes11.8%
↑42.9%prior 7
No Injury52no injury crashes61.2%
↑15.6%prior 45

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Top Contributing Factors

While collisions involving an animal remained the top contributing factor in both periods, their count decreased from 15 in 2019 to 13 in 2020. Conversely, several other factors saw significant increases in count; 'Lost Control' incidents doubled from 5 to 10, and crashes attributed to 'Ran off road - straight' increased from 4 to 9. Crashes where a driver ran a stop sign also doubled in count from 3 to 6 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal13 (15.3%)↓-13.3%prior 15
Lost Control10 (11.8%)↑100.0%prior 5
Ran off road - straight9 (10.6%)
Ran Stop Sign6 (7.1%)
FTYROW: From stop sign5 (5.9%)↓-28.6%prior 7
Driver Distraction: Manual operation of an electronic communication device4 (4.7%)
Driving too fast for conditions4 (4.7%)
Other (explain in narrative): Other4 (4.7%)
Made improper turn3 (3.5%)
Ran off road - left3 (3.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions were broadly similar year-over-year, with the majority of incidents in both periods occurring on dry roads (65.9% in 2020 vs. 65.2% in 2019) and in clear weather. The proportion of crashes happening in daylight was unchanged at 56.5% for both years. However, there was an increase in the share of crashes occurring in dark, unlit roadway conditions, which rose from 21.7% of all crashes in 2019 to 28.2% in 2020.

Weather

Clear56 (70.9%)
↑33.3%prior 42
Cloudy19 (24.1%)
↑72.7%prior 11
Fog, smoke, smog1 (1.3%)
Rain1 (1.3%)
Severe Winds1 (1.3%)
Snow1 (1.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight48 (60.8%)
↑23.1%prior 39
Dark - roadway not lighted24 (30.4%)
↑60.0%prior 15
Dark - roadway lighted5 (6.3%)
Dawn2 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry56 (70.9%)
↑24.4%prior 45
Wet6 (7.6%)
↑20.0%prior 5
Gravel5 (6.3%)
Snow5 (6.3%)
Ice/frost5 (6.3%)
Mud, dirt1 (1.3%)
Slush1 (1.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Chevrolet and Ford vehicles being the most common in both 2020 and 2019. However, the age demographics of people involved in crashes shifted significantly. The number of individuals in the 16-20 age group increased from 17 to 29, and the 35-44 age group grew from 24 to 35 persons. Conversely, the 55-64 and 65+ age groups, which were the most represented in 2019 with 27 individuals each, saw their involvement decrease to 14 and 19 people, respectively, in 2020.

Top Vehicle Makes (116 vehicles)

1
FORD21 (18.1%)
↑5.0%prior 20
2
CHEV16 (13.8%)
↑14.3%prior 14
3
CHEVROLET7 (6%)
↓-41.7%prior 12
4
DODGE7 (6%)
↑40.0%prior 5
5
KIA4 (3.4%)
6
JEEP4 (3.4%)
7
GMC4 (3.4%)
8
TOYT4 (3.4%)
9
MACK3 (2.6%)
10
DODG3 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

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

Sex Distribution (111 persons with recorded sex)

Male87 (78.4%)
↑40.3%prior 62
Female24 (21.6%)
↓-33.3%prior 36

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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: 2020-01-01 through 2020-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 85
  • Total persons involved: 171
  • Total vehicles involved: 116

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: 2020." Published September 9, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2020-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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