Yearly Traffic Safety Analysis

236 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Jones County recorded 236 total crashes, a 1.7% increase from the 232 crashes documented in 2024. While the overall crash volume remained relatively stable, the outcomes shifted significantly. The number of fatalities decreased by 50%, from 6 to 3, while the total number of injuries rose by 43.4%, from 53 to 76.

236

▲ 1.7%was 232

Total Crash Events

3

▼ -50.0%was 6

Persons Killed

76

▲ 43.4%was 53

Persons Injured

3

▼ -50.0%was 6

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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Jones County were largely stable year-over-year, with total incidents increasing by just four, from 232 in 2024 to 236 in 2025. However, this small change in volume masks a more significant rise in injury-related outcomes. The number of people injured in crashes increased from 53 to 76, a 43.4% rise from the previous year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 5↓-40.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

2

Cyclists Injured

Prior: 1↑100.0%

73

Motorists Injured

Prior: 50↑46.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal crash patterns showed consistency in the peak day but a shift in the peak hour. Wednesday remained the day with the most crashes in both periods, with incidents increasing from 40 in 2024 to 56 in 2025. The evening peak hour shifted slightly earlier, from 6 p.m. (18 crashes) in the prior year to a cluster of hours including 3 p.m., 4 p.m., and 5 p.m. (18 crashes each) in the current year. The 7 a.m. morning commute hour also saw an increase in crashes from 13 to 18.

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes changed notably between the two periods. The number of fatal crashes was halved, dropping from 6 in 2024 to 3 in 2025, which corresponded to a decrease in the fatal crash rate from 2.59 to 1.27 per 100 crashes. Conversely, crashes resulting in injuries became more frequent; serious injury crashes more than doubled from 5 to 11, and minor injury crashes increased from 16 to 28. As a result, the proportion of crashes involving no injuries fell from 79.3% to 74.2% of the total.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.3%
↓-50.0%prior 6
Serious Injury11serious injury crashes4.7%
↑120.0%prior 5
Minor Injury28minor injury crashes11.9%
↑75.0%prior 16
Possible Injury19possible injury crashes8.1%
↓-9.5%prior 21
No Injury175no injury crashes74.2%
↓-4.9%prior 184

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the top contributing factor in both years, though the count of such incidents decreased by 10.2% from 98 in 2024 to 88 in 2025. The second most common factor in 2024, 'Failure to yield from a stop sign,' saw its count drop by 26.7% from 15 to 11 crashes, moving it down in the rankings. Meanwhile, crashes attributed to 'Driving too fast for conditions' increased in count by 30% from 10 to 13 incidents, and 'Lost Control' crashes rose from 12 to 14.

Officer-Reported Primary Contributing Cause

Animal88 (37.3%)↓-10.2%prior 98
Lost Control14 (5.9%)↑16.7%prior 12
Driving too fast for conditions13 (5.5%)↑30.0%prior 10
Ran off road - straight13 (5.5%)↑85.7%prior 7
FTYROW: From stop sign11 (4.7%)↓-26.7%prior 15
Ran off road - left11 (4.7%)↑57.1%prior 7
Followed too close9 (3.8%)→0.0%prior 9
Other (explain in narrative): Other7 (3%)
FTYROW: Making left turn6 (2.5%)
FTYROW: From driveway5 (2.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Most crashes in both periods occurred in daylight and on dry roads. In 2025, crashes during daylight hours increased from 99 to 108, and their share of the total rose from 42.7% to 45.8%. Similarly, crashes on dry surfaces increased from 110 to 123. Incidents occurring on adverse road surfaces such as wet, snow, or ice showed a decrease, falling from 33 crashes in 2024 to 25 in 2025.

Weather

Clear99 (66.4%)
→0.0%prior 99
Cloudy31 (20.8%)
↑14.8%prior 27
Snow8 (5.4%)
Severe Winds4 (2.7%)
Blowing Snow2 (1.3%)
Fog, smoke, smog2 (1.3%)
↓-60.0%prior 5
Freezing rain/drizzle2 (1.3%)
Rain1 (0.7%)
↓-83.3%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight108 (70.6%)
↑9.1%prior 99
Dark - roadway not lighted24 (15.7%)
↓-7.7%prior 26
Dark - roadway lighted12 (7.8%)
→0.0%prior 12
Dark - unknown roadway lighting4 (2.6%)
Dusk3 (2.0%)
↓-40.0%prior 5
Dawn2 (1.3%)
↓-60.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry123 (82.6%)
↑11.8%prior 110
Snow12 (8.1%)
↑33.3%prior 9
Wet6 (4.0%)
↓-64.7%prior 17
Ice/frost6 (4.0%)
↑20.0%prior 5
Slush1 (0.7%)
Gravel1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet were the most common vehicle makes involved in crashes in both periods, with counts for both increasing year-over-year. An analysis of the age of persons involved in crashes reveals a growing representation of older individuals. The number of people in the 55-64 age group increased from 38 to 48, while the 65+ age group grew from 52 to 65. Combined, individuals aged 55 and older accounted for 32.7% of all persons involved in crashes in 2025, up from 27.3% in 2024.

Top Vehicle Makes (335 vehicles)

1
FORD76 (22.7%)
↑20.6%prior 63
2
CHEV53 (15.8%)
↑29.3%prior 41
3
HOND17 (5.1%)
↑240.0%prior 5
4
CHEVROLET15 (4.5%)
↑25.0%prior 12
5
GMC15 (4.5%)
↑66.7%prior 9
6
DODG10 (3%)
↓-37.5%prior 16
7
HYUN9 (2.7%)
↓-25.0%prior 12
8
KIA9 (2.7%)
↓-30.8%prior 13
9
NISS9 (2.7%)
↑80.0%prior 5
10
BUIC8 (2.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

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

Sex Distribution (152 persons with recorded sex)

Male88 (57.9%)
↑14.3%prior 77
Female64 (42.1%)
↓-8.6%prior 70

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 236
  • Total persons involved: 346
  • Total vehicles involved: 335

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: 2025." Published September 9, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2025-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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