Yearly Traffic Safety Analysis

282 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Buena Vista County recorded 282 total crashes, representing a 3.4% decrease from the 292 crashes documented in 2016. Concurrently, total fatalities fell from two to one. The most notable year-over-year shift was a 23.9% reduction in total injuries, which decreased from 113 in 2016 to 86 in 2017.

282

▼ -3.4%was 292

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

86

▼ -23.9%was 113

Persons Injured

1

▼ -50.0%was 2

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall traffic safety trend in Buena Vista County was positive from 2016 to 2017. Total crashes declined by 3.4% (from 292 to 282), while total injuries saw a more significant drop of 23.9% (from 113 to 86). The number of fatalities was also halved, decreasing from two in the prior year to one in the current year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 2↓-50.0%

2

Pedestrians Injured

Prior: 2→0.0%

2

Cyclists Injured

Prior: 1↑100.0%

82

Motorists Injured

Prior: 110↓-25.5%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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 the two periods. While Friday remained the peak day for crashes in both 2017 (53 crashes) and 2016 (61 crashes), the peak hour moved earlier in the day. In 2017, the highest number of crashes occurred at 3 p.m. (25 crashes), a shift from the 6 p.m. peak observed in 2016 (24 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity outcomes improved from 2016 to 2017. The number of fatal crashes decreased from two to one, and the proportion of crashes resulting in no injuries increased from 69.2% in 2016 to 72.7% in 2017. Correspondingly, the share of crashes involving any level of injury (serious, minor, or possible) fell from 30.1% of all crashes in 2016 to 26.9% in 2017.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
↓-50.0%prior 2
Serious Injury3serious injury crashes1.1%
→0.0%prior 3
Minor Injury30minor injury crashes10.6%
↓-11.8%prior 34
Possible Injury43possible injury crashes15.2%
↓-15.7%prior 51
No Injury205no injury crashes72.7%
↑1.5%prior 202

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes remained consistent year-over-year. In both 2017 and 2016, collisions involving an 'Animal' was the top factor, increasing in count from 38 to 42 incidents. 'Failure to yield right of way from a stop sign' was the second most common factor in both periods, with its count rising from 27 to 31. 'Driving too fast for conditions' saw a decrease, falling from 18 incidents in 2016 to 14 in 2017.

Officer-Reported Primary Contributing Cause

Animal42 (14.9%)↑10.5%prior 38
FTYROW: From stop sign31 (11%)↑14.8%prior 27
Other (explain in narrative): Other26 (9.2%)↑23.8%prior 21
Lost Control19 (6.7%)↑5.6%prior 18
Followed too close16 (5.7%)↓-5.9%prior 17
Driving too fast for conditions14 (5%)↓-22.2%prior 18
Ran off road - straight14 (5%)↑16.7%prior 12
Ran off road - left14 (5%)↑40.0%prior 10
Made improper turn11 (3.9%)↑83.3%prior 6
FTYROW: Making left turn11 (3.9%)↑10.0%prior 10

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Comparing crash conditions, the proportion of incidents occurring in daylight increased from 59.6% in 2016 to 65.6% in 2017. Conversely, crashes in 'Dark - roadway not lighted' conditions decreased proportionally, accounting for 18.5% of crashes in 2016 and 13.1% in 2017. Regarding road surface, the share of crashes on dry roads grew from 61.6% to 66.3%, while crashes on wet surfaces fell from 10.6% to 6.4%.

Weather

Clear197 (77.0%)
↓-2.0%prior 201
Cloudy30 (11.7%)
↓-16.7%prior 36
Snow16 (6.3%)
↓-5.9%prior 17
Rain6 (2.3%)
↓-40.0%prior 10
Freezing rain/drizzle3 (1.2%)
Blowing Snow2 (0.8%)
Other (explain in narrative)1 (0.4%)
Fog, smoke, smog1 (0.4%)
↓-83.3%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight185 (71.2%)
↑6.3%prior 174
Dark - roadway not lighted37 (14.2%)
↓-31.5%prior 54
Dark - roadway lighted28 (10.8%)
↓-3.4%prior 29
Dawn6 (2.3%)
Dark - unknown roadway lighting2 (0.8%)
Dusk2 (0.8%)
↓-75.0%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry187 (73.0%)
↑3.9%prior 180
Snow21 (8.2%)
↓-12.5%prior 24
Wet18 (7.0%)
↓-41.9%prior 31
Ice/frost14 (5.5%)
↓-17.6%prior 17
Gravel11 (4.3%)
↓-15.4%prior 13
Slush3 (1.2%)
↓-40.0%prior 5
Mud, dirt1 (0.4%)
Sand1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes, including Chevrolet, Ford, and Dodge, maintained their relative rankings between 2016 and 2017. Examining the age of persons involved, the most significant shift was in the 65 and older age group, which saw its involvement increase from 50 individuals in 2016 to 62 in 2017. Meanwhile, involvement for the 16-20 age group decreased slightly from 73 to 69 persons.

Top Vehicle Makes (461 vehicles)

1
FORD80 (17.4%)
↑14.3%prior 70
2
CHEV62 (13.4%)
↑40.9%prior 44
3
CHEVROLET38 (8.2%)
↓-26.9%prior 52
4
DODG29 (6.3%)
↓-3.3%prior 30
5
GMC19 (4.1%)
↓-24.0%prior 25
6
BUIC17 (3.7%)
↑88.9%prior 9
7
DODGE17 (3.7%)
↓-15.0%prior 20
8
TOYT15 (3.3%)
↑87.5%prior 8
9
JEEP14 (3%)
↑75.0%prior 8
10
CHRY13 (2.8%)
↑8.3%prior 12

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (357 persons with recorded sex)

Male206 (57.7%)
↓-1.4%prior 209
Female151 (42.3%)
↑2.0%prior 148

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 282
  • Total persons involved: 513
  • Total vehicles involved: 461

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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