Yearly Traffic Safety Analysis

59 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Ringgold County, total crashes increased by 13.5%, from 52 in 2019 to 59 in 2020. While there were no fatalities in either period, the number of people injured rose by 66.7%, from 12 to 20. The most significant change in crash causation was a more than twofold increase in collisions involving animals, which jumped from 8 incidents in 2019 to 19 in 2020.

59

▲ 13.5%was 52

Total Crash Events

0

Persons Killed

20

▲ 66.7%was 12

Persons Injured

0

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

Trend Summary

The overall trend in Ringgold County shows a rise in traffic incidents from 2019 to 2020. Total crashes increased by 13.5% from 52 to 59. The number of people injured saw a more substantial increase of 66.7%, rising from 12 to 20, even as fatalities remained at zero for both years.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

20

Motorists Injured

Prior: 12↑66.7%

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 timing of crashes showed some shifts between the two periods, though the peak hour for crashes remained 5 PM in both years. Friday continued to be the day with the most crashes, increasing from 11 in 2019 to 15 in 2020. Notably, crashes on Thursdays nearly doubled, increasing from 7 in 2019 to 13 in 2020, making it the second-most frequent day for incidents.

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

While Ringgold County recorded zero fatal crashes in both 2019 and 2020, the severity of non-fatal crashes increased. The number of crashes involving a serious injury tripled from 1 to 3, and the total number of people injured grew from 12 to 20. Consequently, the proportion of crashes resulting in any level of injury rose from 17.3% of all crashes in 2019 to 30.5% in 2020.

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes5.1%
↑200.0%prior 1
Minor Injury6minor injury crashes10.2%
↑100.0%prior 3
Possible Injury9possible injury crashes15.3%
↑80.0%prior 5
No Injury41no injury crashes69.5%
↓-4.7%prior 43

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

The leading contributing factors for crashes shifted significantly year-over-year. Collisions with animals became far more prevalent, with the count more than doubling from 8 crashes in 2019 to 19 in 2020, making it the top factor by a wide margin. Crashes attributed to 'Lost Control' also increased, rising from 3 to 7 incidents. Conversely, a general 'Other' factor, which was a top factor with 8 crashes in 2019, accounted for only 1 crash in 2020.

Officer-Reported Primary Contributing Cause

Animal19 (32.2%)↑137.5%prior 8
Lost Control7 (11.9%)
Ran off road - straight5 (8.5%)
Other (explain in narrative): No improper action3 (5.1%)
FTYROW: Making left turn2 (3.4%)
Driver Distraction: Other interior distraction2 (3.4%)
Driving too fast for conditions2 (3.4%)
FTYROW: From parked position2 (3.4%)
Illegally Parked/Unattended2 (3.4%)
Ran Stop Sign2 (3.4%)

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 saw a notable change in lighting, while weather and road surface factors remained more stable. The number of crashes occurring in daylight dropped from 39 in 2019 to 26 in 2020, despite an overall increase in total crashes. Crashes on clear days and dry roads remained the most common scenarios in both years, with counts of 39 and 36 respectively in 2020, compared to 41 and 34 in 2019.

Weather

Clear39 (84.8%)
↓-4.9%prior 41
Cloudy3 (6.5%)
Rain2 (4.3%)
Snow2 (4.3%)

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

Lighting

Daylight26 (56.5%)
↓-33.3%prior 39
Dark - roadway not lighted13 (28.3%)
↑8.3%prior 12
Dark - roadway lighted3 (6.5%)
Dusk3 (6.5%)
Dark - unknown roadway lighting1 (2.2%)

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

Road Surface

Dry36 (80.0%)
↑5.9%prior 34
Wet5 (11.1%)
→0.0%prior 5
Ice/frost2 (4.4%)
Gravel1 (2.2%)
↓-80.0%prior 5
Slush1 (2.2%)

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

Vehicles & Demographics

While the top vehicle makes involved in crashes, Chevrolet and Ford, remained consistent across both years, the demographics of persons involved showed a distinct shift. The 35-44 age group saw the largest change, increasing from 5 individuals involved in crashes in 2019 to 24 in 2020, becoming the most represented group. This replaced the 55-64 age group, which was the largest cohort in 2019 with 18 individuals.

Top Vehicle Makes (81 vehicles)

1
CHEV16 (19.8%)
→0.0%prior 16
2
FORD11 (13.6%)
↓-15.4%prior 13
3
CHEVROLET9 (11.1%)
↑28.6%prior 7
4
DODGE5 (6.2%)
5
DODG5 (6.2%)
6
GMC5 (6.2%)
→0.0%prior 5
7
TOYT3 (3.7%)
8
PETERBILT2 (2.5%)
9
BUICK2 (2.5%)
10
BUIC2 (2.5%)

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

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

Sex Distribution (71 persons with recorded sex)

Male49 (69.0%)
↑8.9%prior 45
Female22 (31.0%)
↓-12.0%prior 25

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: 59
  • Total persons involved: 120
  • Total vehicles involved: 81

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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