Yearly Traffic Safety Analysis

146 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Clarke County, total crashes decreased by 29.8% from 208 in 2019 to 146 in 2020. This positive trend was highlighted by the elimination of traffic fatalities, which dropped from two in the prior year to zero in the current year. The number of total injuries also saw a reduction, falling from 59 to 44.

146

▼ -29.8%was 208

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

44

▼ -25.4%was 59

Persons Injured

0

▼ -100.0%was 2

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

Crash data for Clarke County shows a significant downward trend year-over-year. Total collisions fell by nearly 30%, from 208 in 2019 to 146 in 2020. Correspondingly, the number of people injured decreased by 25.4% from 59 to 44, and the two fatalities recorded in 2019 were not repeated in 2020.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 2↓-100.0%

1

Cyclists Injured

Prior: 0→%

43

Motorists Injured

Prior: 59↓-27.1%

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 daily and hourly patterns of crashes shifted between the two periods. In 2019, crashes peaked on Thursdays and Fridays (36 each), while in 2020, Mondays and Fridays were the peak days (26 each). The busiest hour for crashes also moved slightly earlier, from the 2 p.m. hour in 2019 with 17 crashes to the 1 p.m. hour in 2020 with 19 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 saw a notable improvement year-over-year. Fatal crashes were eliminated, dropping from two incidents with two fatalities in 2019 to zero in 2020. The number of serious injury crashes also fell by 60%, from 10 in 2019 to 4 in 2020. While the total number of injuries decreased from 59 to 44, the proportion of crashes resulting in any level of injury remained relatively stable, accounting for 23.9% of crashes in 2020 versus 22.6% in 2019.

Outcome by Severity (Crash Events)

Serious Injury4serious injury crashes2.7%
↓-60.0%prior 10
Minor Injury12minor injury crashes8.2%
↓-40.0%prior 20
Possible Injury19possible injury crashes13%
↑26.7%prior 15
No Injury111no injury crashes76%
↓-31.1%prior 161

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 periods, though the count decreased from 44 in 2019 to 36 in 2020. Other major factors saw more significant reductions; crashes attributed to 'Lost Control' dropped by 65.5% from a count of 29 to 10, and those related to 'Driving too fast for conditions' fell by 56.3% from a count of 16 to 7. As a result of these shifts, 'Ran off road - straight' became the third-most frequent factor in 2020 with 11 incidents.

Officer-Reported Primary Contributing Cause

Animal36 (24.7%)↓-18.2%prior 44
Other (explain in narrative): Other12 (8.2%)↓-25.0%prior 16
Ran off road - straight11 (7.5%)↑10.0%prior 10
Lost Control10 (6.8%)↓-65.5%prior 29
Driving too fast for conditions7 (4.8%)↓-56.3%prior 16
FTYROW: From stop sign6 (4.1%)↓-57.1%prior 14
Ran off road - left6 (4.1%)↑20.0%prior 5
Other (explain in narrative): No improper action6 (4.1%)↓-14.3%prior 7
FTYROW: Making left turn5 (3.4%)↓-28.6%prior 7
Followed too close4 (2.7%)↓-60.0%prior 10

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

Road & Environmental Conditions

While total crashes decreased across all conditions, the proportion of crashes occurring in adverse weather increased from the prior year. In 2019, 53% of crashes occurred in clear weather, which dropped to a 45% share in 2020. Conversely, the proportion of crashes happening in daylight increased from 53% in 2019 to 60% in 2020, as collisions in dark conditions saw a larger relative decrease.

Weather

Clear66 (56.9%)
↓-40.5%prior 111
Cloudy29 (25.0%)
↓-3.3%prior 30
Rain6 (5.2%)
↓-25.0%prior 8
Snow6 (5.2%)
↓-40.0%prior 10
Freezing rain/drizzle5 (4.3%)
↓-16.7%prior 6
Fog, smoke, smog3 (2.6%)
Severe Winds1 (0.9%)

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

Lighting

Daylight87 (74.4%)
↓-20.9%prior 110
Dark - roadway not lighted20 (17.1%)
↓-44.4%prior 36
Dark - roadway lighted8 (6.8%)
↓-50.0%prior 16
Dawn1 (0.9%)
Dusk1 (0.9%)
↓-80.0%prior 5

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

Road Surface

Dry76 (65.5%)
↓-35.0%prior 117
Wet18 (15.5%)
↑5.9%prior 17
Gravel5 (4.3%)
→0.0%prior 5
Ice/frost5 (4.3%)
↓-66.7%prior 15
Slush4 (3.4%)
Snow4 (3.4%)
↓-71.4%prior 14
Other (explain in narrative)2 (1.7%)
Mud, dirt1 (0.9%)
Sand1 (0.9%)

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

Vehicles & Demographics

The demographic profile of persons involved in crashes shifted between periods. In 2019, the 45-54 and 55-64 age groups were most represented, with 69 and 68 individuals respectively. In 2020, the most represented groups were younger, with the 26-34 age group (53 people) and 35-44 age group (48 people) being the largest. Ford and Chevrolet vehicles were the most common makes involved in crashes in both years, with their counts decreasing in line with the overall reduction in collisions.

Top Vehicle Makes (208 vehicles)

1
FORD34 (16.3%)
↓-32.0%prior 50
2
CHEV24 (11.5%)
↓-38.5%prior 39
3
CHEVROLET16 (7.7%)
↓-23.8%prior 21
4
DODGE10 (4.8%)
↓-9.1%prior 11
5
JEEP10 (4.8%)
↑11.1%prior 9
6
KIA8 (3.8%)
↑33.3%prior 6
7
HYUN7 (3.4%)
8
BUICK7 (3.4%)
9
GMC6 (2.9%)
↓-40.0%prior 10
10
NISSAN6 (2.9%)
↑20.0%prior 5

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

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

Sex Distribution (196 persons with recorded sex)

Male123 (62.8%)
↓-34.2%prior 187
Female73 (37.2%)
↓-32.4%prior 108

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: 146
  • Total persons involved: 288
  • Total vehicles involved: 208

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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