Yearly Traffic Safety Analysis

208 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Appanoose County, total traffic crashes decreased by 8.4% from 227 in 2019 to 208 in 2020. Despite this overall reduction in collisions, the number of people injured increased significantly from 44 to 74, a 68.2% rise. The number of fatalities also doubled, increasing from one to two year-over-year.

208

▼ -8.4%was 227

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

74

▲ 68.2%was 44

Persons Injured

2

▲ 100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 Appanoose County showed a downward trend, falling from 227 to 208 year-over-year, the severity of these incidents worsened. The number of total injuries rose by 68.2% from 44 to 74, and fatalities increased from one to two. This indicates that although collisions were less frequent, they resulted in more severe outcomes in 2020 compared to 2019.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 1↑100.0%

3

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

70

Motorists Injured

Prior: 44↑59.1%

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 temporal patterns of crashes shifted between the two periods. In 2020, the peak day for crashes was Thursday with 38 incidents, a change from 2019 when Wednesday was the peak day with 45 crashes. The peak hour for collisions also moved earlier, from 8 p.m. in 2019 (22 crashes) to 6 p.m. in 2020 (20 crashes).

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 from 2019 to 2020. The number of fatal crashes doubled from one to two, and the fatal crash rate rose from 0.44% to 0.96%. The proportion of crashes resulting in any injury increased, with the share of minor injury crashes growing from 6.6% of all collisions in 2019 to 12.5% in 2020. Consequently, property-damage-only crashes decreased as a share of the total, from 81.9% to 74%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1%
↑100.0%prior 1
Serious Injury8serious injury crashes3.8%
↑14.3%prior 7
Minor Injury26minor injury crashes12.5%
↑73.3%prior 15
Possible Injury18possible injury crashes8.7%
→0.0%prior 18
No Injury154no injury crashes74%
↓-17.2%prior 186

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 with animals remained the leading contributing factor in both years, though the count of such incidents decreased by 22.2% from 99 in 2019 to 77 in 2020. 'Lost Control' was the second most common factor in both periods, with its count remaining stable at 18 and 19 crashes, respectively. Notably, crashes attributed to 'Ran Stop Sign' increased by 75% in count, from 4 incidents in 2019 to 7 in 2020.

Officer-Reported Primary Contributing Cause

Animal77 (37%)↓-22.2%prior 99
Lost Control19 (9.1%)↑5.6%prior 18
Other (explain in narrative): Other12 (5.8%)↑50.0%prior 8
Followed too close9 (4.3%)↑50.0%prior 6
Driving too fast for conditions8 (3.8%)↑60.0%prior 5
Ran off road - straight8 (3.8%)↓-20.0%prior 10
Ran Stop Sign7 (3.4%)
Other (explain in narrative): No improper action7 (3.4%)
Operating vehicle in an reckless, erratic, careless, negligent manner7 (3.4%)
FTYROW: From stop sign6 (2.9%)

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

Road & Environmental Conditions

Year-over-year, the proportion of crashes occurring on unlit dark roadways decreased, with the count falling from 46 to 27. While the majority of crashes in both periods occurred on dry roads, the share of incidents on wet surfaces increased from 6.6% in 2019 to 9.6% in 2020. The proportion of crashes in clear weather decreased from 51.1% to 44.7%, while those in cloudy conditions saw a corresponding increase.

Weather

Clear93 (64.1%)
↓-19.8%prior 116
Cloudy36 (24.8%)
↑33.3%prior 27
Rain8 (5.5%)
Snow4 (2.8%)
↓-55.6%prior 9
Freezing rain/drizzle2 (1.4%)
↓-66.7%prior 6
Other (explain in narrative)1 (0.7%)
Severe Winds1 (0.7%)

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

Lighting

Daylight89 (61.8%)
↓-3.3%prior 92
Dark - roadway not lighted27 (18.8%)
↓-41.3%prior 46
Dark - roadway lighted18 (12.5%)
↓-5.3%prior 19
Dusk5 (3.5%)
Dawn3 (2.1%)
↓-57.1%prior 7
Dark - unknown roadway lighting2 (1.4%)

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

Road Surface

Dry109 (75.2%)
↓-8.4%prior 119
Wet20 (13.8%)
↑33.3%prior 15
Ice/frost7 (4.8%)
↓-30.0%prior 10
Gravel4 (2.8%)
↓-42.9%prior 7
Snow3 (2.1%)
↓-80.0%prior 15
Mud, dirt1 (0.7%)
Slush1 (0.7%)

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

Vehicles & Demographics

The top three vehicle makes involved in crashes—Chevrolet, Ford, and Dodge—remained consistent across both years, although the total number of vehicles from these makes involved in crashes declined. An analysis of persons involved in crashes reveals a shift in age demographics; the number of individuals aged 45-54 increased from 50 to 78, while the 55-64 age group saw a decrease from 64 to 43. Additionally, involvement for the 16-20 age group rose from 55 to 70.

Top Vehicle Makes (294 vehicles)

1
FORD54 (18.4%)
↑1.9%prior 53
2
CHEV44 (15%)
↓-27.9%prior 61
3
CHEVROLET24 (8.2%)
↓-4.0%prior 25
4
DODG19 (6.5%)
↓-17.4%prior 23
5
JEEP16 (5.4%)
↑166.7%prior 6
6
TOYT10 (3.4%)
↓-16.7%prior 12
7
CHRY9 (3.1%)
↓-10.0%prior 10
8
GMC9 (3.1%)
↑12.5%prior 8
9
NISSAN7 (2.4%)
↑40.0%prior 5
10
NISS7 (2.4%)
↓-12.5%prior 8

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

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

Sex Distribution (268 persons with recorded sex)

Male173 (64.6%)
↑19.3%prior 145
Female95 (35.4%)
↓-33.1%prior 142

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: 208
  • Total persons involved: 456
  • Total vehicles involved: 294

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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