Yearly Traffic Safety Analysis

223 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Buena Vista County recorded 223 total crashes, a 24.1% decrease from the 294 crashes reported in 2019. The most significant year-over-year change was the reduction in traffic fatalities, which fell from 3 in 2019 to 0 in 2020. Total injuries also declined from 98 to 67 during the same period.

223

▼ -24.1%was 294

Total Crash Events

0

▼ -100.0%was 3

Persons Killed

67

▼ -31.6%was 98

Persons Injured

0

▼ -100.0%was 3

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

Traffic crashes in Buena Vista County showed a notable downward trend year-over-year. The total number of crashes decreased by 24.1%, from 294 in 2019 to 223 in 2020. This decline was accompanied by a 31.6% reduction in total injuries, from 98 to 67, and the elimination of crash-related fatalities, which dropped from 3 to 0.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 3↓-100.0%

1

Pedestrians Injured

Prior: 0→%

2

Cyclists Injured

Prior: 2→0.0%

64

Motorists Injured

Prior: 96↓-33.3%

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 showed some shifts between 2019 and 2020. While the peak hour for crashes remained 3 PM in both years, the number of incidents during this hour fell from 26 to 19. The peak day for crashes moved from Thursday, with 51 crashes in 2019, to Friday, with 47 crashes in 2020. Overall, crashes in 2020 were more concentrated on Fridays, whereas in 2019 they were more evenly distributed throughout the week.

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 improved significantly, with fatal crashes dropping from 3 in 2019 to 0 in 2020. While the total number of injury-related crashes decreased, the proportion of crashes resulting in serious or minor injuries increased. Serious injury crashes rose from 5 (1.7% share) to 6 (2.7% share), and minor injury crashes increased from 24 (8.2% share) to 30 (13.5% share). Conversely, crashes resulting in possible injuries saw a notable drop in count from 43 to 30.

Outcome by Severity (Crash Events)

Serious Injury6serious injury crashes2.7%
↑20.0%prior 5
Minor Injury30minor injury crashes13.5%
↑25.0%prior 24
Possible Injury30possible injury crashes13.5%
↓-30.2%prior 43
No Injury157no injury crashes70.4%
↓-28.3%prior 219

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 an animal remained the top contributing factor in both years, though the count decreased from 43 in 2019 to 35 in 2020. Several other factors saw significant shifts in count; crashes attributed to 'Ran Stop Sign' more than doubled, increasing from 5 to 12 incidents. In contrast, incidents involving 'Followed too close' saw a sharp decline from 17 crashes in 2019 to just 4 in 2020.

Officer-Reported Primary Contributing Cause

Animal35 (15.7%)↓-18.6%prior 43
Driving too fast for conditions17 (7.6%)↑30.8%prior 13
Other (explain in narrative): Other16 (7.2%)↓-42.9%prior 28
FTYROW: From stop sign16 (7.2%)↓-20.0%prior 20
Driver Distraction: Other interior distraction15 (6.7%)↑66.7%prior 9
Ran off road - left14 (6.3%)↑27.3%prior 11
Ran Stop Sign12 (5.4%)↑140.0%prior 5
Lost Control11 (4.9%)↓-35.3%prior 17
Ran off road - straight9 (4%)↓-10.0%prior 10
Other (explain in narrative): No improper action8 (3.6%)↓-20.0%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

Crashes under clear weather and on dry road surfaces decreased in absolute numbers but remained proportionally consistent, accounting for roughly 70% and 65% of incidents respectively in both 2019 and 2020. Crashes in adverse conditions saw a general decline, with incidents on snowy surfaces falling from 26 to 14 and on icy surfaces from 23 to 15. Similarly, crashes in darkness on unlit roadways decreased from 55 in 2019 to 44 in 2020.

Weather

Clear157 (76.2%)
↓-21.1%prior 199
Cloudy24 (11.7%)
↓-41.5%prior 41
Snow12 (5.8%)
↑20.0%prior 10
Rain4 (1.9%)
↓-60.0%prior 10
Fog, smoke, smog4 (1.9%)
Freezing rain/drizzle2 (1.0%)
↓-71.4%prior 7
Blowing Snow2 (1.0%)
↓-71.4%prior 7
Other (explain in narrative)1 (0.5%)

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

Lighting

Daylight140 (67.3%)
↓-25.1%prior 187
Dark - roadway not lighted44 (21.2%)
↓-20.0%prior 55
Dark - roadway lighted15 (7.2%)
↓-37.5%prior 24
Dawn4 (1.9%)
↓-33.3%prior 6
Dusk3 (1.4%)
↓-62.5%prior 8
Dark - unknown roadway lighting2 (1.0%)

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

Road Surface

Dry146 (70.2%)
↓-24.4%prior 193
Wet18 (8.7%)
↓-28.0%prior 25
Ice/frost15 (7.2%)
↓-34.8%prior 23
Snow14 (6.7%)
↓-46.2%prior 26
Gravel13 (6.3%)
Other (explain in narrative)1 (0.5%)
Mud, dirt1 (0.5%)

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

Vehicles & Demographics

The number of vehicles involved in crashes dropped from 485 in 2019 to 363 in 2020. Chevrolet and Ford were the two most common vehicle makes in collisions for both periods, with their counts decreasing from 99 and 98 respectively in 2019, to 69 and 68 in 2020. The age distribution of persons involved in crashes saw a general decline across all brackets, consistent with the overall reduction in incidents, with no significant shifts in the proportional representation of any single age group.

Top Vehicle Makes (363 vehicles)

1
FORD68 (18.7%)
↓-30.6%prior 98
2
CHEV47 (12.9%)
↓-29.9%prior 67
3
CHEVROLET22 (6.1%)
↓-31.3%prior 32
4
DODG22 (6.1%)
↓-21.4%prior 28
5
JEEP18 (5%)
↑50.0%prior 12
6
TOYO14 (3.9%)
↑40.0%prior 10
7
TOYT13 (3.6%)
↓-27.8%prior 18
8
GMC12 (3.3%)
↓-57.1%prior 28
9
HOND11 (3%)
↑83.3%prior 6
10
DODGE11 (3%)
↓-26.7%prior 15

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

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

Sex Distribution (315 persons with recorded sex)

Male187 (59.4%)
↓-28.9%prior 263
Female128 (40.6%)
↓-23.4%prior 167

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: 223
  • Total persons involved: 488
  • Total vehicles involved: 363

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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