Yearly Traffic Safety Analysis

198 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Cherokee County, total crashes increased by 11.9% from 177 in 2018 to 198 in 2019. While total fatalities decreased from 2 to 1, the most notable shift was a tripling in the number of serious injury crashes, which grew from 3 in the prior year to 9 in the current year. This rise in crash severity occurred even as the total number of people injured fell slightly from 55 to 50.

198

▲ 11.9%was 177

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

50

▼ -9.1%was 55

Persons Injured

1

▼ -50.0%was 2

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

Traffic crashes in Cherokee County trended upward in 2019, with a total of 198 incidents compared to 177 in 2018, marking an 11.9% increase. Despite the rise in total crashes, key outcomes showed a mixed trend: total injuries fell from 55 to 50, and fatalities were halved from 2 to 1.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 2↓-50.0%

1

Pedestrians Injured

Prior: 1→0.0%

49

Motorists Injured

Prior: 54↓-9.3%

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 Tuesday remained the peak day for crashes in both 2018 (31 crashes) and 2019 (33 crashes), the peak hour for incidents moved from 3 p.m. in 2018 (16 crashes) to 7 p.m. in 2019 (16 crashes). Crashes during the winter months were consistently high across both years, with February seeing 24 incidents in 2018 and 27 in 2019.

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 worsened year-over-year, even as the number of fatal crashes decreased from 2 to 1. The number of crashes resulting in serious injuries tripled, increasing from 3 in 2018 to 9 in 2019. Consequently, the share of all incidents classified as 'Serious Injury' rose from 1.7% in 2018 to 4.5% in 2019. Crashes resulting in minor or possible injuries saw a combined decrease.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
↓-50.0%prior 2
Serious Injury9serious injury crashes4.5%
↑200.0%prior 3
Minor Injury14minor injury crashes7.1%
↓-26.3%prior 19
Possible Injury16possible injury crashes8.1%
↓-27.3%prior 22
No Injury158no injury crashes79.8%
↑20.6%prior 131

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

Collisions with an animal remained the top contributing factor in both periods, with the count increasing from 49 crashes in 2018 to 54 in 2019. 'Driving too fast for conditions' was the second-leading cause in both years, though its count fell from 17 to 15. A significant change was observed in crashes due to 'Improper Backing', which increased from 2 incidents in 2018 to 9 in 2019, while crashes from 'Followed too close' were halved from 14 to 7.

Officer-Reported Primary Contributing Cause

Animal54 (27.3%)↑10.2%prior 49
Driving too fast for conditions15 (7.6%)↓-11.8%prior 17
FTYROW: From stop sign12 (6.1%)↑20.0%prior 10
Lost Control11 (5.6%)↑22.2%prior 9
Other (explain in narrative): Other10 (5.1%)↓-16.7%prior 12
Improper Backing9 (4.5%)
Followed too close7 (3.5%)↓-50.0%prior 14
FTYROW: From driveway7 (3.5%)
Ran off road - straight7 (3.5%)↓-22.2%prior 9
Ran Stop Sign7 (3.5%)

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

Road & Environmental Conditions

While crashes in clear weather and on dry roads were predominant in both 2018 and 2019, there was an increase in crashes occurring under adverse conditions. Incidents on snowy roads increased from 8 to 14, and those on icy or frosty surfaces rose from 16 to 19. Crashes in darkness on unlighted roadways also saw an increase, rising from 23 incidents in 2018 to 30 in 2019.

Weather

Clear93 (61.6%)
↓-7.0%prior 100
Cloudy29 (19.2%)
↑52.6%prior 19
Snow10 (6.6%)
↑66.7%prior 6
Rain6 (4.0%)
↓-25.0%prior 8
Freezing rain/drizzle6 (4.0%)
Blowing Snow2 (1.3%)
Fog, smoke, smog2 (1.3%)
Other (explain in narrative)2 (1.3%)
Severe Winds1 (0.7%)

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

Lighting

Daylight103 (67.3%)
↓-1.0%prior 104
Dark - roadway not lighted30 (19.6%)
↑30.4%prior 23
Dark - roadway lighted12 (7.8%)
↑33.3%prior 9
Dawn5 (3.3%)
Dusk3 (2.0%)

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

Road Surface

Dry91 (60.3%)
↓-3.2%prior 94
Ice/frost19 (12.6%)
↑18.8%prior 16
Wet18 (11.9%)
↑20.0%prior 15
Snow14 (9.3%)
↑75.0%prior 8
Gravel5 (3.3%)
↓-16.7%prior 6
Slush4 (2.6%)

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

Vehicles & Demographics

The vehicle makes involved in crashes saw a shift in rankings; Ford-involved vehicles increased from 42 to 66, becoming the most common make in 2019, while Chevrolet-involved vehicles remained relatively stable (64 in 2018 vs. 60 in 2019). An analysis of persons involved in crashes reveals a significant increase in the 16-20 age group, which grew from 29 individuals in 2018 to 50 in 2019. The 65+ age group also saw a notable increase in involvement, rising from 50 to 68 persons year-over-year.

Top Vehicle Makes (298 vehicles)

1
FORD66 (22.1%)
↑57.1%prior 42
2
CHEV35 (11.7%)
↓-23.9%prior 46
3
CHEVROLET25 (8.4%)
↑38.9%prior 18
4
JEEP21 (7%)
↑133.3%prior 9
5
DODG15 (5%)
↓-11.8%prior 17
6
GMC12 (4%)
↓-25.0%prior 16
7
BUIC9 (3%)
↓-18.2%prior 11
8
KIA8 (2.7%)
9
PETERBILT7 (2.3%)
↑40.0%prior 5
10
HOND7 (2.3%)

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

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

Sex Distribution (274 persons with recorded sex)

Male158 (57.7%)
↑36.2%prior 116
Female116 (42.3%)
↑23.4%prior 94

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: 198
  • Total persons involved: 419
  • Total vehicles involved: 298

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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