Yearly Traffic Safety Analysis

120 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Greene County recorded 120 total vehicle crashes, a 14.3% decrease from the 140 crashes reported in 2019. While the number of fatalities remained constant at one for both years, total injuries fell from 49 to 39. A notable change was the decrease in serious injury crashes, which dropped from 5 in 2019 to 2 in 2020.

120

▼ -14.3%was 140

Total Crash Events

1

Persons Killed

39

▼ -20.4%was 49

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

Trend Summary

Overall, traffic crashes in Greene County showed a downward trend from 2019 to 2020. The total number of crashes decreased by 14.3%, from 140 to 120. Similarly, the number of people injured in these incidents declined by 20.4%, from 49 in 2019 to 39 in 2020.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Motorists Killed

Prior: 1↓-100.0%

0

Pedestrians Injured

Prior: 0→0.0%

39

Motorists Injured

Prior: 49↓-20.4%

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 in Greene County were largely consistent year-over-year. Thursday remained the peak day for crashes in both 2020 (28 crashes) and 2019 (27 crashes). The 5 PM hour was a peak time in both periods, with 10 crashes in 2020 and tied for the most with 11 crashes in 2019. While overall daily and hourly distributions were similar, crashes on Tuesdays saw a notable decrease from 25 in 2019 to 17 in 2020.

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 the number of fatal crashes remained unchanged at one in both 2020 and 2019, the fatal crash rate increased slightly from 0.7% to 0.8% due to the lower total crash volume in 2020. There was a positive shift in injury severity, with serious injury crashes decreasing from 5 in 2019 to 2 in 2020. Consequently, the proportion of crashes resulting in no injuries increased from 75.7% in 2019 to 77.5% in 2020.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.8%
→0.0%prior 1
Serious Injury2serious injury crashes1.7%
↓-60.0%prior 5
Minor Injury14minor injury crashes11.7%
↓-6.7%prior 15
Possible Injury10possible injury crashes8.3%
↓-23.1%prior 13
No Injury93no injury crashes77.5%
↓-12.3%prior 106

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 involving animals were the leading contributing factor in both periods, though the count decreased slightly from 47 crashes in 2019 to 44 in 2020. The ranking of other top factors shifted, with 'Ran off road - straight' becoming the second-most cited factor in 2020 with 10 crashes, an increase from 6 in the prior year. Conversely, crashes attributed to 'Lost Control' fell from 11 to 7, and those related to 'Driving too fast for conditions' dropped from 7 to 2.

Officer-Reported Primary Contributing Cause

Animal44 (36.7%)↓-6.4%prior 47
Ran off road - straight10 (8.3%)↑66.7%prior 6
FTYROW: At uncontrolled intersection8 (6.7%)↓-20.0%prior 10
Lost Control7 (5.8%)↓-36.4%prior 11
Other (explain in narrative): Other7 (5.8%)↑40.0%prior 5
Driver Distraction: Other interior distraction5 (4.2%)
Followed too close5 (4.2%)
Ran Stop Sign4 (3.3%)
Ran off road - left3 (2.5%)↓-40.0%prior 5
Other (explain in narrative): Vision obstructed3 (2.5%)

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

Road & Environmental Conditions

The conditions under which crashes occurred showed some shifts between the two periods. In 2020, a higher proportion of incidents happened in clear weather (55.8% of crashes) and on dry roads (59.2%) compared to 2019 (44.3% and 51.4%, respectively). Correspondingly, the number of crashes in adverse conditions decreased; incidents in cloudy weather fell from 29 to 15, and crashes on wet roads dropped from 13 to 6. Lighting conditions remained proportionally stable year-over-year, with daylight crashes accounting for approximately 49% in both periods.

Weather

Clear67 (74.4%)
↑8.1%prior 62
Cloudy15 (16.7%)
↓-48.3%prior 29
Rain3 (3.3%)
↓-66.7%prior 9
Freezing rain/drizzle2 (2.2%)
Snow2 (2.2%)
Fog, smoke, smog1 (1.1%)

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

Lighting

Daylight59 (64.8%)
↓-14.5%prior 69
Dark - roadway not lighted23 (25.3%)
↓-4.2%prior 24
Dawn4 (4.4%)
Dark - roadway lighted4 (4.4%)
↓-55.6%prior 9
Dusk1 (1.1%)

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

Road Surface

Dry71 (78.0%)
↓-1.4%prior 72
Wet6 (6.6%)
↓-53.8%prior 13
Gravel5 (5.5%)
→0.0%prior 5
Snow5 (5.5%)
↓-54.5%prior 11
Ice/frost4 (4.4%)
↓-33.3%prior 6

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

Vehicles & Demographics

Chevrolet and Ford vehicles were the most frequently involved in crashes in both years, although the number of vehicles from both makes decreased in 2020. The age demographics of persons involved in crashes showed a notable shift. In 2019, the 55-64 age group was the most represented with 52 individuals, but this number fell to 24 in 2020. In 2020, the 16-20 age group became the most frequently involved demographic with 41 individuals, down from 49 in the prior year.

Top Vehicle Makes (168 vehicles)

1
CHEV39 (23.2%)
↓-18.8%prior 48
2
FORD20 (11.9%)
↓-42.9%prior 35
3
GMC15 (8.9%)
↑200.0%prior 5
4
CHEVROLET10 (6%)
↓-33.3%prior 15
5
DODG8 (4.8%)
↑33.3%prior 6
6
TOYT8 (4.8%)
7
CHRY8 (4.8%)
→0.0%prior 8
8
HOND6 (3.6%)
9
HYUN4 (2.4%)
10
BUIC4 (2.4%)

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

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

Sex Distribution (155 persons with recorded sex)

Male94 (60.6%)
↓-19.7%prior 117
Female61 (39.4%)
↓-14.1%prior 71

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: 120
  • Total persons involved: 246
  • Total vehicles involved: 168

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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