Yearly Traffic Safety Analysis

82 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Van Buren County recorded 82 total crashes, a 2.5% increase from the 80 crashes reported in 2016. While total incidents remained relatively stable, the number of crashes attributed to animals saw a significant year-over-year increase of 70.6%, rising from 17 to 29 incidents. Despite a 50% reduction in fatalities from 2 to 1, the number of persons injured increased by 28.1%.

82

▲ 2.5%was 80

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

41

▲ 28.1%was 32

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

Overall traffic crashes in Van Buren County saw a slight increase of 2.5%, from 80 in 2016 to 82 in 2017. While the number of fatalities decreased from 2 to 1, the number of people injured in crashes rose by 28.1%, from 32 in the prior year to 41 in the current year.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 2↓-50.0%

0

Other Killed

Prior: 0→0.0%

39

Motorists Injured

Prior: 32↑21.9%

2

Other Injured

Prior: 0→%

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 shifted between the two periods. In 2017, the most frequent days for crashes were Wednesday and Friday, each with 15 incidents, a change from 2016 when Monday was the peak day with 18 crashes. The peak hour for crashes also moved from the 4 p.m. hour in 2016 (8 crashes) to a tie between the 11 a.m. and 5 p.m. hours in 2017, each with 7 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

The severity of crashes showed mixed changes year-over-year. The number of fatal crashes decreased from 2 in 2016 to 1 in 2017, with the fatal crash rate dropping from 2.5% to 1.2% of all crashes. Conversely, the proportion of crashes resulting in any level of injury (serious, minor, or possible) increased from 35% in 2016 (28 crashes) to 39% in 2017 (32 crashes).

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.2%
↓-50.0%prior 2
Serious Injury4serious injury crashes4.9%
↑33.3%prior 3
Minor Injury10minor injury crashes12.2%
↓-23.1%prior 13
Possible Injury18possible injury crashes22%
↑50.0%prior 12
No Injury49no injury crashes59.8%
↓-2.0%prior 50

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

Collisions involving animals were the leading contributing factor in both years, but their count increased significantly by 70.6%, from 17 incidents in 2016 to 29 in 2017. Consequently, the share of crashes attributed to this factor rose from 21.3% to 35.4%. In contrast, crashes due to 'Lost Control' decreased in count from 15 to 10, and incidents of 'Ran off road - straight' fell from 12 to 8.

Officer-Reported Primary Contributing Cause

Animal29 (35.4%)↑70.6%prior 17
Lost Control10 (12.2%)↓-33.3%prior 15
Ran off road - straight8 (9.8%)↓-33.3%prior 12
Driving too fast for conditions5 (6.1%)↓-37.5%prior 8
Ran off road - left4 (4.9%)
Other (explain in narrative): Other3 (3.7%)
Followed too close3 (3.7%)
Operating vehicle in an reckless, erratic, careless, negligent manner3 (3.7%)
Improper Backing2 (2.4%)
Ran off road - right2 (2.4%)

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

Road & Environmental Conditions

Crash conditions remained broadly consistent year-over-year, with the majority of incidents in both 2017 and 2016 occurring in clear weather (49 and 47 crashes, respectively) and during daylight hours (45 and 49 crashes). Dry road surfaces were reported in most crashes in both periods (52 in 2017 vs. 51 in 2016). There were no significant shifts in the proportions of crashes occurring under adverse weather, lighting, or road surface conditions.

Weather

Clear49 (70.0%)
↑4.3%prior 47
Cloudy13 (18.6%)
↓-13.3%prior 15
Rain5 (7.1%)
Snow3 (4.3%)

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

Lighting

Daylight45 (62.5%)
↓-8.2%prior 49
Dark - roadway not lighted15 (20.8%)
↓-6.3%prior 16
Dawn4 (5.6%)
Dusk4 (5.6%)
Dark - roadway lighted3 (4.2%)
Dark - unknown roadway lighting1 (1.4%)

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

Road Surface

Dry52 (73.2%)
↑2.0%prior 51
Wet9 (12.7%)
↑50.0%prior 6
Gravel5 (7.0%)
↓-28.6%prior 7
Snow4 (5.6%)
↓-20.0%prior 5
Slush1 (1.4%)

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

Vehicles & Demographics

The demographics of persons involved in crashes showed a shift, with the 16-20 age group increasing from 19 to 24 individuals and the 45-54 age group more than doubling from 10 to 22. Regarding vehicles, Chevrolet-branded models were involved in 28 crashes in 2017 (combining 'CHEV' and 'CHEVROLET'), up from 25 in 2016. In contrast, Ford vehicle involvements decreased from 18 to 13.

Top Vehicle Makes (106 vehicles)

1
CHEV16 (15.1%)
↑33.3%prior 12
2
FORD13 (12.3%)
↓-27.8%prior 18
3
CHEVROLET12 (11.3%)
↓-7.7%prior 13
4
DODGE7 (6.6%)
↑16.7%prior 6
5
JEEP6 (5.7%)
6
CHRYSLER4 (3.8%)
7
CHRY4 (3.8%)
8
TOYT4 (3.8%)
9
DODG4 (3.8%)
↓-55.6%prior 9
10
PONTIAC3 (2.8%)

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

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

Sex Distribution (68 persons with recorded sex)

Male36 (52.9%)
↓-23.4%prior 47
Female32 (47.1%)
↓-8.6%prior 35

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: 82
  • Total persons involved: 131
  • Total vehicles involved: 106

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

ThatCarHitMe.com · An Injuria.ai Company