Yearly Traffic Safety Analysis

294 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Buena Vista County recorded 294 total crashes, an 8.5% increase from the 271 crashes documented in 2018. While overall crashes increased, the most significant year-over-year change was in crash fatalities, which rose from one person in 2018 to three in 2019.

294

▲ 8.5%was 271

Total Crash Events

3

▲ 200.0%was 1

Persons Killed

98

▲ 15.3%was 85

Persons Injured

3

▲ 200.0%was 1

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Buena Vista County showed an upward trajectory year-over-year. Total crashes rose by 8.5% from 271 in 2018 to 294 in 2019. Correspondingly, the number of people injured increased by 15.3% from 85 to 98, and total fatalities increased from 1 to 3.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 1↑200.0%

2

Cyclists Injured

Prior: 1↑100.0%

96

Motorists Injured

Prior: 83↑15.7%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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. The peak day for crashes moved from Monday (55 crashes) in 2018 to Thursday (51 crashes) in 2019. A more pronounced change occurred in the peak hour, which shifted from the 7 a.m. hour in 2018 (26 crashes) to the 3 p.m. hour in 2019 (26 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes shifted year-over-year. Fatal crashes increased from 1 to 3, with the fatal crash rate rising from 0.4% to 1.0% of all incidents. Crashes resulting in serious injuries decreased from 8 to 5, and minor injury crashes fell from 33 to 24. In contrast, crashes involving possible injuries increased notably from 28 incidents in 2018 to 43 in 2019.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1%
↑200.0%prior 1
Serious Injury5serious injury crashes1.7%
↓-37.5%prior 8
Minor Injury24minor injury crashes8.2%
↓-27.3%prior 33
Possible Injury43possible injury crashes14.6%
↑53.6%prior 28
No Injury219no injury crashes74.5%
↑9.0%prior 201

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes showed some changes between 2018 and 2019. Collisions involving an animal became the top-ranked factor, with the count of such incidents increasing from 33 to 43. Crashes attributed to 'Driving too fast for conditions' were nearly halved, dropping from 26 incidents in 2018 to 13 in 2019. Incidents involving 'Followed too close' increased from 12 to 17, while 'Failure to yield from a stop sign' also rose from 17 to 20 incidents.

Officer-Reported Primary Contributing Cause

Animal43 (14.6%)↑30.3%prior 33
Other (explain in narrative): Other28 (9.5%)↓-20.0%prior 35
FTYROW: From stop sign20 (6.8%)↑17.6%prior 17
Followed too close17 (5.8%)↑41.7%prior 12
Lost Control17 (5.8%)↓-10.5%prior 19
Driving too fast for conditions13 (4.4%)↓-50.0%prior 26
Improper Backing11 (3.7%)↑37.5%prior 8
Ran off road - left11 (3.7%)↑10.0%prior 10
Other (explain in narrative): No improper action10 (3.4%)↑25.0%prior 8
Ran off road - straight10 (3.4%)↓-28.6%prior 14

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

In both periods, most crashes occurred in clear weather and on dry roads. In 2019, 67.7% of crashes (199 incidents) occurred in clear weather, a similar proportion to 2018's 67.9% (184 incidents). Despite an overall increase in total crashes, the number of incidents on adverse road surfaces like snow, ice, or wet pavement decreased from 100 in 2018 to 85 in 2019. Crashes during daylight hours increased from 160 to 187, while those in darkness on unlighted roads fell slightly from 60 to 55.

Weather

Clear199 (71.6%)
↑8.2%prior 184
Cloudy41 (14.7%)
↑64.0%prior 25
Rain10 (3.6%)
↓-28.6%prior 14
Snow10 (3.6%)
↓-23.1%prior 13
Blowing Snow7 (2.5%)
↑40.0%prior 5
Freezing rain/drizzle7 (2.5%)
↓-12.5%prior 8
Fog, smoke, smog2 (0.7%)
↓-60.0%prior 5
Other (explain in narrative)1 (0.4%)
Severe Winds1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight187 (66.5%)
↑16.9%prior 160
Dark - roadway not lighted55 (19.6%)
↓-8.3%prior 60
Dark - roadway lighted24 (8.5%)
↑4.3%prior 23
Dusk8 (2.8%)
↑33.3%prior 6
Dawn6 (2.1%)
↓-14.3%prior 7
Dark - unknown roadway lighting1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry193 (69.4%)
↑22.2%prior 158
Snow26 (9.4%)
↑18.2%prior 22
Wet25 (9.0%)
↓-16.7%prior 30
Ice/frost23 (8.3%)
↓-36.1%prior 36
Slush7 (2.5%)
Gravel4 (1.4%)
↓-42.9%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

The number of vehicles involved in crashes increased from 430 to 485 year-over-year. Ford and Chevrolet remained the two most frequently involved vehicle makes, with both seeing an increase in their incident counts. The demographics of persons involved in crashes showed a notable increase in younger age groups; the number of individuals aged 16-20 rose from 63 to 86, and those aged 21-25 increased from 47 to 72.

Top Vehicle Makes (485 vehicles)

1
FORD98 (20.2%)
↑19.5%prior 82
2
CHEV67 (13.8%)
↑9.8%prior 61
3
CHEVROLET32 (6.6%)
↑39.1%prior 23
4
DODG28 (5.8%)
↑16.7%prior 24
5
GMC28 (5.8%)
↑86.7%prior 15
6
TOYT18 (3.7%)
↑20.0%prior 15
7
KIA17 (3.5%)
↑21.4%prior 14
8
PONT15 (3.1%)
↑66.7%prior 9
9
DODGE15 (3.1%)
↑87.5%prior 8
10
JEEP12 (2.5%)
↑50.0%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (430 persons with recorded sex)

Male263 (61.2%)
↑30.2%prior 202
Female167 (38.8%)
↑21.0%prior 138

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 294
  • Total persons involved: 648
  • Total vehicles involved: 485

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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