Yearly Traffic Safety Analysis

86 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Keokuk County, total vehicle crashes decreased from 104 in 2018 to 86 in 2019, representing a 17.3% reduction. While the number of fatalities remained stable at one, the number of injuries fell from 29 to 22. The most significant year-over-year change was a sharp decline in serious injury crashes, which dropped from 7 in the prior period to just 1 in the current period.

86

▼ -17.3%was 104

Total Crash Events

1

Persons Killed

22

▼ -24.1%was 29

Persons Injured

1

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

Overall traffic crash trends in Keokuk County showed a decline year-over-year. Total crashes fell by 17.3%, from 104 in 2018 to 86 in 2019. This downward trend was also reflected in injuries, which decreased by 24.1% from 29 to 22, while fatalities held steady with one death recorded in each year.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 1→0.0%

22

Motorists Injured

Prior: 28↓-21.4%

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. Friday remained the most frequent day for crashes, with the count increasing from 19 in 2018 to 22 in 2019. The peak hour for collisions shifted slightly later, moving from 5 p.m. in the prior year (8 crashes) to 6 p.m. in the current year (7 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 improved year-over-year, with a notable reduction in serious injuries. While the number of fatal crashes was unchanged at one, the proportion of crashes resulting in no injuries increased from 75.0% in 2018 to 79.1% in 2019. Most significantly, serious injury crashes fell from 7 incidents (6.7% of all crashes) in 2018 to 1 incident (1.2% of all crashes) in 2019.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.2%
→0.0%prior 1
Serious Injury1serious injury crashes1.2%
↓-85.7%prior 7
Minor Injury9minor injury crashes10.5%
↑12.5%prior 8
Possible Injury7possible injury crashes8.1%
↓-30.0%prior 10
No Injury68no injury crashes79.1%
↓-12.8%prior 78

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 animals remained the top contributing factor in both years, though the count decreased from 33 incidents in 2018 to 28 in 2019. "Lost Control" was the second-most cited factor in both periods, with its count increasing from 10 to 13. The number of crashes attributed to "Ran Stop Sign" more than doubled, rising from 2 in 2018 to 5 in 2019.

Officer-Reported Primary Contributing Cause

Animal28 (32.6%)↓-15.2%prior 33
Lost Control13 (15.1%)↑30.0%prior 10
Ran off road - straight7 (8.1%)↓-12.5%prior 8
Ran Stop Sign5 (5.8%)
Other (explain in narrative): Other4 (4.7%)↓-33.3%prior 6
FTYROW: From parked position2 (2.3%)
FTYROW: From stop sign2 (2.3%)
Driver Distraction: Adjusting devices (radio, climate)2 (2.3%)
Driver Distraction: Inattentive/lost in thought2 (2.3%)
Driver Distraction: Other interior distraction2 (2.3%)

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 distribution of crashes across different environmental conditions remained largely consistent year-over-year. Crashes in daylight accounted for 51.2% of incidents in 2019, compared to 51.0% in 2018, and collisions on dry roads made up 48.8% and 51.0% in the respective periods. One notable shift was in road surface conditions, where the number of crashes on icy or frosty roads doubled from 3 in 2018 to 6 in 2019.

Weather

Clear43 (71.7%)
↓-12.2%prior 49
Cloudy11 (18.3%)
↓-31.3%prior 16
Rain3 (5.0%)
Snow2 (3.3%)
Blowing Snow1 (1.7%)

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

Lighting

Daylight44 (73.3%)
↓-17.0%prior 53
Dark - roadway not lighted9 (15.0%)
↓-18.2%prior 11
Dark - roadway lighted6 (10.0%)
Dawn1 (1.7%)

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

Road Surface

Dry42 (70.0%)
↓-20.8%prior 53
Wet7 (11.7%)
↓-30.0%prior 10
Ice/frost6 (10.0%)
Snow3 (5.0%)
↓-40.0%prior 5
Gravel2 (3.3%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes shifted between periods. In 2019, Ford was the most common make with 29 vehicles, surpassing Chevrolet, which saw its involvement decrease from 26 vehicles in 2018 to 14 in 2019. The age distribution of persons involved showed a decrease for the 26-34 age group (from 35 to 29 individuals) and slight increases for the 45-54 age group (from 27 to 29) and the 65+ age group (from 19 to 22).

Top Vehicle Makes (116 vehicles)

1
FORD29 (25%)
↑38.1%prior 21
2
CHEV14 (12.1%)
↓-46.2%prior 26
3
DODG8 (6.9%)
↓-27.3%prior 11
4
GMC7 (6%)
→0.0%prior 7
5
TOYT5 (4.3%)
↓-37.5%prior 8
6
CHEVROLET5 (4.3%)
↓-44.4%prior 9
7
KIA3 (2.6%)
↓-50.0%prior 6
8
DODGE3 (2.6%)
↓-40.0%prior 5
9
NISS3 (2.6%)
10
NISSAN3 (2.6%)

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

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

Sex Distribution (106 persons with recorded sex)

Male64 (60.4%)
↓-7.2%prior 69
Female42 (39.6%)
↓-4.5%prior 44

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: 86
  • Total persons involved: 173
  • 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: 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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