Yearly Traffic Safety Analysis

52 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Ringgold County recorded 52 vehicle crashes, an 8.3% increase from the 48 crashes reported in 2018. Despite the rise in total incidents, the number of resulting injuries fell significantly by 45.5%, from 22 in 2018 to 12 in 2019. Additionally, there were no fatal crashes in 2019, a decrease from one fatal crash in the prior year.

52

▲ 8.3%was 48

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

12

▼ -45.5%was 22

Persons Injured

0

▼ -100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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 crash volume in Ringgold County saw a slight increase in 2019, rising by 8.3% from 48 to 52 incidents year-over-year. In contrast to the rising crash count, the severity of these incidents decreased, with total injuries declining by 45.5% and fatalities dropping from one in 2018 to zero in 2019.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 1↓-100.0%

12

Motorists Injured

Prior: 22↓-45.5%

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 2018 and 2019. While Friday remained the peak day for crashes in both years, the number of incidents on that day decreased from 13 to 11. The 5 p.m. hour became the sole peak hour in 2019 with 8 incidents, an increase from 6 crashes during that same hour in 2018. Notably, crashes on Sunday increased from 2 in 2018 to 10 in 2019, while Saturday crashes decreased from 12 to 5.

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 in Ringgold County decreased in 2019 compared to the previous year. There were no fatal crashes, down from one fatal incident that accounted for 2.1% of crashes in 2018. The proportion of crashes resulting in any level of injury also fell, from 35.4% (17 crashes) in 2018 to 17.3% (9 crashes) in 2019. Consequently, crashes resulting in no injuries increased their share from 62.5% of all incidents in 2018 to 82.7% in 2019.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1.9%
↓-66.7%prior 3
Minor Injury3minor injury crashes5.8%
↓-50.0%prior 6
Possible Injury5possible injury crashes9.6%
↓-37.5%prior 8
No Injury43no injury crashes82.7%
↑43.3%prior 30

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

The leading contributing factors shifted between the two periods. In 2019, collisions involving an 'Animal' and 'Other' factors were the most cited causes, each involved in 8 crashes. While the count for animal-related incidents was unchanged from 2018, crashes attributed to 'Lost Control' decreased from 8 incidents in 2018 to 3 in 2019. 'Failure to Yield Right of Way: From stop sign' also saw a decrease from 4 crashes in 2018 to 2 in 2019.

Officer-Reported Primary Contributing Cause

Animal8 (15.4%)→0.0%prior 8
Other (explain in narrative): Other8 (15.4%)
Lost Control3 (5.8%)↓-62.5%prior 8
FTYROW: From parked position3 (5.8%)
Ran off road - straight3 (5.8%)
Driver Distraction: Inattentive/lost in thought2 (3.8%)
Equipment failure2 (3.8%)
Followed too close2 (3.8%)
FTYROW: From stop sign2 (3.8%)
FTYROW: Other (explain in narrative)2 (3.8%)

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 environmental conditions for crashes remained broadly similar year-over-year, with the majority of incidents in both periods occurring in clear weather and during daylight hours. In 2019, 75% of crashes happened in daylight, compared to 68.8% in 2018. Regarding road conditions, crashes on dry surfaces increased from 29 to 34, while incidents on roads with ice or frost decreased from 6 in 2018 to 1 in 2019.

Weather

Clear41 (82.0%)
↑5.1%prior 39
Rain3 (6.0%)
Cloudy2 (4.0%)
Snow2 (4.0%)
Other (explain in narrative)1 (2.0%)
Blowing Snow1 (2.0%)

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

Lighting

Daylight39 (76.5%)
↑18.2%prior 33
Dark - roadway not lighted12 (23.5%)
↑33.3%prior 9

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

Road Surface

Dry34 (66.7%)
↑17.2%prior 29
Gravel5 (9.8%)
→0.0%prior 5
Wet5 (9.8%)
→0.0%prior 5
Snow4 (7.8%)
Mud, dirt2 (3.9%)
Ice/frost1 (2.0%)
↓-83.3%prior 6

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

Vehicles & Demographics

Analysis of vehicles and persons involved shows shifts in both make and age demographics. Chevrolet-branded vehicles (CHEV/CHEVROLET) remained the most frequently involved, though their total count decreased from 26 in 2018 to 23 in 2019, while the number of Ford vehicles in crashes increased from 7 to 13. For persons involved, there was a notable decrease in the 16-20 age group (from 24 to 16 persons) and a significant increase in the 55-64 age group (from 6 to 18 persons).

Top Vehicle Makes (84 vehicles)

1
CHEV16 (19%)
↓-23.8%prior 21
2
FORD13 (15.5%)
↑85.7%prior 7
3
CHEVROLET7 (8.3%)
↑40.0%prior 5
4
GMC5 (6%)
5
TOYOTA4 (4.8%)
6
DODG4 (4.8%)
↓-33.3%prior 6
7
HONDA3 (3.6%)
8
BUIC3 (3.6%)
9
DEER3 (3.6%)
10
JEEP2 (2.4%)

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

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

Sex Distribution (70 persons with recorded sex)

Male45 (64.3%)
↑32.4%prior 34
Female25 (35.7%)
↓-7.4%prior 27

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: 52
  • Total persons involved: 105
  • Total vehicles involved: 84

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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