Yearly Traffic Safety Analysis

88 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Worth County recorded 88 total crashes, a 6.4% decrease from the 94 crashes reported in 2019. Total injuries also saw a significant decline, falling from 32 to 18. The most notable change was the reduction in traffic fatalities, which dropped from two in 2019 to zero in 2020.

88

▼ -6.4%was 94

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

18

▼ -43.8%was 32

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

Overall, traffic crashes in Worth County showed a downward trend from 2019 to 2020. The total number of crashes decreased by 6.4%, from 94 to 88. This decline was accompanied by a 43.8% reduction in total injuries (from 32 to 18) and a complete elimination of fatalities, which fell from two to zero.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 1↓-100.0%

18

Motorists Injured

Prior: 32↓-43.8%

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 shifted between the two periods. In 2020, the peak day for crashes was Tuesday with 19 incidents, compared to Wednesday with 20 incidents in 2019. The peak hour for crashes also changed significantly, moving from the 5 a.m. hour in 2019 (9 crashes) to the 6 p.m. hour in 2020 (10 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 decreased notably from 2019 to 2020. The number of fatal crashes dropped from two in 2019 to zero in 2020. The proportion of crashes resulting in any level of injury fell from 24.5% of all crashes in 2019 (23 incidents) to 17.0% in 2020 (15 incidents). While the count of serious injury crashes increased from one to two, no-injury crashes constituted a larger share of the total in 2020 (83%) compared to 2019 (73.4%).

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes2.3%
↑100.0%prior 1
Minor Injury9minor injury crashes10.2%
↓-30.8%prior 13
Possible Injury4possible injury crashes4.5%
↓-55.6%prior 9
No Injury73no injury crashes83%
↑5.8%prior 69

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 primary contributing factors remained consistent across both years, though their counts changed. Collisions involving an animal were the leading factor in both periods, with the count of such crashes increasing from 27 in 2019 to 32 in 2020. Conversely, crashes attributed to 'Driving too fast for conditions' decreased from 18 to 12, and incidents involving 'Lost Control' fell from 13 to 8. The top three factors remained the same in both years.

Officer-Reported Primary Contributing Cause

Animal32 (36.4%)↑18.5%prior 27
Driving too fast for conditions12 (13.6%)↓-33.3%prior 18
Lost Control8 (9.1%)↓-38.5%prior 13
Ran off road - straight7 (8%)
FTYROW: From stop sign4 (4.5%)
Ran off road - left4 (4.5%)
Operating vehicle in an reckless, erratic, careless, negligent manner3 (3.4%)
Other (explain in narrative): Other2 (2.3%)
Followed too close2 (2.3%)
FTYROW: At uncontrolled intersection1 (1.1%)

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 2019 and 2020. In 2020, a larger percentage of crashes happened in 'Clear' weather (51.1%) and on 'Dry' road surfaces (45.5%), compared to 33.0% and 35.1% respectively in 2019. The share of crashes occurring during 'Daylight' decreased from 48.9% in 2019 to 37.5% in 2020, while crashes in 'Dark - roadway not lighted' conditions increased from 26.6% to 30.7% of the total.

Weather

Clear45 (66.2%)
↑45.2%prior 31
Cloudy7 (10.3%)
↓-58.8%prior 17
Fog, smoke, smog4 (5.9%)
Blowing Snow4 (5.9%)
↓-63.6%prior 11
Snow3 (4.4%)
Sleet, hail2 (2.9%)
Freezing rain/drizzle2 (2.9%)
Rain1 (1.5%)
↓-83.3%prior 6

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

Lighting

Daylight33 (48.5%)
↓-28.3%prior 46
Dark - roadway not lighted27 (39.7%)
↑8.0%prior 25
Dark - roadway lighted3 (4.4%)
Dawn2 (2.9%)
Dusk2 (2.9%)
Dark - unknown roadway lighting1 (1.5%)

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

Road Surface

Dry40 (58.8%)
↑21.2%prior 33
Ice/frost16 (23.5%)
↑23.1%prior 13
Snow6 (8.8%)
↓-62.5%prior 16
Wet3 (4.4%)
↓-70.0%prior 10
Slush2 (2.9%)
Gravel1 (1.5%)

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

Vehicles & Demographics

An analysis of vehicles and persons involved shows both consistency and change. Ford remained the most common vehicle make involved in crashes, with its count increasing from 21 in 2019 to 24 in 2020. The top three age groups of persons involved in crashes were 26-34, 35-44, and 45-54 in both years, although the total number of people in these groups decreased from 94 in 2019 to 73 in 2020. The 35-44 age group saw the most significant decline in involvement, from 36 individuals in 2019 to 23 in 2020.

Top Vehicle Makes (110 vehicles)

1
FORD24 (21.8%)
↑14.3%prior 21
2
CHEV9 (8.2%)
↓-10.0%prior 10
3
HONDA8 (7.3%)
↑33.3%prior 6
4
CHEVROLET7 (6.4%)
↓-12.5%prior 8
5
FREIGHTLINER6 (5.5%)
↓-14.3%prior 7
6
DODGE4 (3.6%)
↓-50.0%prior 8
7
BUIC4 (3.6%)
8
JEEP4 (3.6%)
↓-20.0%prior 5
9
SUBA3 (2.7%)
10
KENWORTH3 (2.7%)
↓-40.0%prior 5

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

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

Sex Distribution (106 persons with recorded sex)

Male68 (64.2%)
↓-12.8%prior 78
Female38 (35.8%)
↓-5.0%prior 40

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: 88
  • Total persons involved: 151
  • Total vehicles involved: 110

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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