Yearly Traffic Safety Analysis

171 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Cherokee County, total traffic crashes decreased by 13.6% from 198 in 2019 to 171 in 2020. Despite this overall reduction in collisions, the severity of crashes worsened significantly. The most notable year-over-year shift was the increase in fatalities from one to four, and a 36% rise in total injuries from 50 to 68.

171

▼ -13.6%was 198

Total Crash Events

4

▲ 300.0%was 1

Persons Killed

68

▲ 36.0%was 50

Persons Injured

4

▲ 300.0%was 1

Fatal Crash Events

Note: "Persons Killed" (4) 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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

While the total number of crashes in Cherokee County showed a downward trend, falling from 198 in 2019 to 171 in 2020, the outcomes of these incidents became more severe. The number of people injured increased by 36% year-over-year, from 50 to 68. Most alarmingly, fatalities quadrupled from one in the prior period to four in the current period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 1↑300.0%

0

Other Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

66

Motorists Injured

Prior: 49↑34.7%

1

Other Injured

Prior: 0→%

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 daily and hourly patterns of crashes shifted between the two periods. In 2020, the peak day for crashes was Wednesday with 32 incidents, a shift from Tuesday in 2019 which had 33 crashes. A more significant change occurred in the peak hour, which moved from the evening commute at 7 p.m. (16 crashes) in 2019 to the morning commute at 7 a.m. (17 crashes) in 2020.

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 markedly from 2019 to 2020. The number of fatal crashes rose from one to four, and the fatal crash rate increased from 0.51 to 2.34 per 100 crashes. While the count of serious injury crashes decreased from nine to three, minor injury crashes more than doubled, increasing from 14 to 39. Consequently, the proportion of crashes resulting in no injuries fell from 79.8% in 2019 to 66.7% in 2020.

Outcome by Severity (Crash Events)

Fatal4fatal crashes2.3%
↑300.0%prior 1
Serious Injury3serious injury crashes1.8%
↓-66.7%prior 9
Minor Injury39minor injury crashes22.8%
↑178.6%prior 14
Possible Injury11possible injury crashes6.4%
↓-31.3%prior 16
No Injury114no injury crashes66.7%
↓-27.8%prior 158

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

Collisions involving an animal remained the top contributing factor in both years, though the count decreased from 54 in 2019 to 47 in 2020. 'Driving too fast for conditions' was the second-leading cause in both periods, with its count dropping from 15 to 11. Notably, crashes attributed to 'Failure to yield right-of-way from a stop sign' saw a significant reduction, falling from 12 incidents in 2019 to just four in 2020.

Officer-Reported Primary Contributing Cause

Animal47 (27.5%)↓-13.0%prior 54
Driving too fast for conditions11 (6.4%)↓-26.7%prior 15
Lost Control10 (5.8%)↓-9.1%prior 11
Ran off road - left9 (5.3%)↑80.0%prior 5
Ran off road - straight9 (5.3%)↑28.6%prior 7
Made improper turn7 (4.1%)
Followed too close7 (4.1%)→0.0%prior 7
Ran Stop Sign6 (3.5%)↓-14.3%prior 7
FTYROW: Making left turn5 (2.9%)→0.0%prior 5
Improper Backing5 (2.9%)↓-44.4%prior 9

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

Road & Environmental Conditions

Comparing conditions year-over-year, a larger proportion of crashes in 2020 occurred in clear weather and on dry roads. Crashes in clear weather constituted 57.9% of the total in 2020, up from 47.0% in 2019. Similarly, 52.0% of crashes happened on dry road surfaces in 2020, compared to 46.0% in the previous year. The share of crashes occurring in cloudy weather decreased from 14.6% to 8.8%.

Weather

Clear99 (72.8%)
↑6.5%prior 93
Cloudy15 (11.0%)
↓-48.3%prior 29
Snow6 (4.4%)
↓-40.0%prior 10
Freezing rain/drizzle6 (4.4%)
→0.0%prior 6
Rain4 (2.9%)
↓-33.3%prior 6
Blowing Snow4 (2.9%)
Fog, smoke, smog2 (1.5%)

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

Lighting

Daylight88 (64.7%)
↓-14.6%prior 103
Dark - roadway not lighted32 (23.5%)
↑6.7%prior 30
Dark - roadway lighted9 (6.6%)
↓-25.0%prior 12
Dawn5 (3.7%)
→0.0%prior 5
Dusk2 (1.5%)

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

Road Surface

Dry89 (65.9%)
↓-2.2%prior 91
Ice/frost16 (11.9%)
↓-15.8%prior 19
Gravel11 (8.1%)
↑120.0%prior 5
Snow10 (7.4%)
↓-28.6%prior 14
Wet6 (4.4%)
↓-66.7%prior 18
Mud, dirt3 (2.2%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford and Chevrolet vehicles leading in both periods. However, the absolute count of these vehicles in crashes decreased; Ford vehicles dropped from 66 to 44, and Chevrolet vehicles (combining 'CHEV' and 'CHEVROLET') decreased from 60 to 52. Regarding driver demographics, the 26-34 age group was the most represented in both years. The number of persons aged 65 and older involved in crashes saw a notable decrease from 68 in 2019 to 53 in 2020.

Top Vehicle Makes (258 vehicles)

1
FORD44 (17.1%)
↓-33.3%prior 66
2
CHEV33 (12.8%)
↓-5.7%prior 35
3
CHEVROLET19 (7.4%)
↓-24.0%prior 25
4
DODG19 (7.4%)
↑26.7%prior 15
5
GMC18 (7%)
↑50.0%prior 12
6
JEEP17 (6.6%)
↓-19.0%prior 21
7
BUIC11 (4.3%)
↑22.2%prior 9
8
DODGE9 (3.5%)
↑28.6%prior 7
9
CHRY9 (3.5%)
↑50.0%prior 6
10
TOYT7 (2.7%)
↑16.7%prior 6

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

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

Sex Distribution (243 persons with recorded sex)

Male146 (60.1%)
↓-7.6%prior 158
Female97 (39.9%)
↓-16.4%prior 116

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: 171
  • Total persons involved: 383
  • Total vehicles involved: 258

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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