Yearly Traffic Safety Analysis

100 CRASHES IN
IOWA, IA
2015

In 2015, Calhoun County recorded 100 traffic crashes, resulting in 1 fatality and 34 injuries. A notable finding from the data is that collisions with animals were the most frequently cited contributing factor, accounting for 23% of all crashes.

100

Total Crash Events

1

Persons Killed

34

Persons Injured

1

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 · 2015-01-01 to 2015-12-31 · Aggregate counts from crash, person, and vehicle records

Vulnerable Road User Casualties

In 2015, all crash fatalities and injuries in Calhoun County involved motor vehicle occupants. One motorist was killed and 34 were injured. There were no recorded fatalities or injuries involving pedestrians or cyclists during this period.

1

Motorists Killed

34

Motorists Injured

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Crashes in Calhoun County occurred most frequently on Tuesdays, which saw 18 incidents. The single busiest hour was 2 p.m., with 13 crashes recorded. Overall, a majority of crashes, 60 out of 100, happened during daylight hours.

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Of the 100 crashes, 74% resulted in no injuries, categorized as property-damage-only. The remaining 26 crashes involved injuries, with 5 classified as serious injury, 10 as minor injury, and 10 as possible injury. One crash was fatal, resulting in one death.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1%
Serious Injury5serious injury crashes5%
Minor Injury10minor injury crashes10%
Possible Injury10possible injury crashes10%
No Injury74no injury crashes74%

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor for crashes was an animal in the roadway, cited in 23 incidents, or 23% of the total. The second most common factor was a driver losing control of their vehicle, which occurred in 16 crashes (16%). Other significant factors included failure to yield the right-of-way at uncontrolled intersections (6 crashes) and from stop signs (5 crashes).

Officer-Reported Primary Contributing Cause

Animal23 (23%)
Lost Control16 (16%)
FTYROW: At uncontrolled intersection6 (6%)
Ran off road - left5 (5%)
Driving too fast for conditions5 (5%)
FTYROW: From stop sign5 (5%)
Other (explain in narrative): Other4 (4%)
Other (explain in narrative): No improper action3 (3%)
Ran Stop Sign3 (3%)
Ran off road - straight3 (3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

A majority of crashes occurred in favorable driving conditions, with 60% taking place in daylight and 51% on dry road surfaces. Clear weather was reported for 49 of the 100 total crashes. Conversely, snow was a factor in 10 crashes, and an icy or frosty road surface was noted in 10 incidents.

Weather

Clear49 (66.2%)
Cloudy14 (18.9%)
Snow10 (13.5%)
Freezing rain/drizzle1 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Weather condition at time of crash

Lighting

Daylight60 (76.9%)
Dark - roadway not lighted7 (9.0%)
Dawn6 (7.7%)
Dark - roadway lighted4 (5.1%)
Dusk1 (1.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Lighting condition field

Road Surface

Dry51 (66.2%)
Ice/frost10 (13.0%)
Snow7 (9.1%)
Wet3 (3.9%)
Slush2 (2.6%)
Other (explain in narrative)2 (2.6%)
Gravel2 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Road surface condition field

Vehicles & Demographics

Analysis of persons involved in crashes shows the 55-64 age group was the most represented, with 29 individuals, followed by the 65+ age group with 27. Among vehicle makes involved in crashes, Chevrolet was the most frequent with 31 vehicles (combining 'CHEVROLET' and 'CHEV'), followed by Ford with 29 vehicles. Dodge was third with 11 vehicles (combining 'DODGE' and 'DODG').

Top Vehicle Makes (147 vehicles)

1
FORD29 (19.7%)
2
CHEVROLET22 (15%)
3
CHEV9 (6.1%)
4
DODGE8 (5.4%)
5
PONTIAC7 (4.8%)
6
LINCOLN5 (3.4%)
7
PONT5 (3.4%)
8
BUICK5 (3.4%)
9
TOYOTA5 (3.4%)
10
HONDA5 (3.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

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

Sex Distribution (129 persons with recorded sex)

Male89 (69.0%)
Female40 (31.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Person-level records linked to crash events

Major Cause

The most frequently recorded major cause of crashes was an 'Animal', contributing to 23% of incidents. 'Lost Control' was the second-leading cause, accounting for 16% of crashes. Failure to yield right-of-way was also a significant factor, with 6 crashes at uncontrolled intersections and 5 from a stop sign.

Major Cause

1
Animal23 (23.5%)
2
Lost Control16 (16.3%)
3
FTYROW: At uncontrolled intersection6 (6.1%)
4
Ran off road - left5 (5.1%)
5
Driving too fast for conditions5 (5.1%)
6
FTYROW: From stop sign5 (5.1%)
7
Other (explain in narrative): Other4 (4.1%)
8
Other (explain in narrative): No improper action3 (3.1%)
9
Ran Stop Sign3 (3.1%)

Showing top 9 of 25 reported. 16 additional (28 total) not shown: Ran off road - straight, Followed too close, Driver Distraction: Adjusting devices (radio, climate), Driver Distraction: Exterior distraction, Failed to keep in proper lane, FTYROW: Making left turn, Improper Backing, Operating vehicle in an reckless, erratic, careless, negligent manner, Passing: Other passing (explain in narrative), Ran off road - right, Crossed centerline (undivided), Driver Distraction: Other electronic device activity, Aggressive driving/road rage, Disregarded RR Signal, FTYROW: From driveway, Driver Distraction: Other interior distraction.

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

First Harmful Event

The most common first harmful event was a collision with another vehicle in traffic, occurring in 30 crashes. The second most frequent event was a collision with an animal, which happened in 23 crashes. Single-vehicle events were also common, including 11 overturns or rollovers and 5 crashes where the first harmful event was hitting a ditch.

First Harmful Event

1
Collision with: Vehicle in traffic30 (31.9%)
2
Collision with: Animal23 (24.5%)
3
Non-collision events: Overturn/rollover11 (11.7%)
4
Collision with: Parked motor vehicle8 (8.5%)
5
Collision with fixed object: Ditch5 (5.3%)
6
Collision with fixed object: Utility pole/light support3 (3.2%)
7
Collision with fixed object: Fence2 (2.1%)
8
Collision with fixed object: Wall1 (1.1%)
9
Collision with: Railway vehicle/train1 (1.1%)

Showing top 9 of 19 reported. 10 additional (10 total) not shown: Collision with fixed object: Culvert/pipe opening, Miscellaneous events: Fire/explosion, Miscellaneous events: Hit and run, Non-collision events: Fell/jumped from vehicle, Non-collision events: Non-contact vehicle (phantom), Non-collision events: Other non-collision (explain in narrative), Collision with fixed object: Bridge overhead structure, Other (explain in narrative), Collision with fixed object: Ground, Collision with fixed object: Other post/pole/support (explain in narrative).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Roadway Junction / Feature

Crashes were most likely to occur on non-junction road segments, which accounted for 35 incidents. Four-way intersections were the most common junction type for crashes, with 24 incidents occurring at these locations. An additional 5 crashes happened at T-intersections.

Roadway Junction / Feature

1
Non-intersection: Non-junction/no special feature35 (46.7%)
2
Intersection: Four-way intersection24 (32%)
3
Intersection: T-intersection5 (6.7%)
4
Non-intersection: Driveway access (related, not in)3 (4%)
5
Non-intersection: Driveway access (within)2 (2.7%)
6
Interchange-related: Off-ramp, diverge area1 (1.3%)
7
Non-intersection: Crossover-related1 (1.3%)
8
Non-intersection: Other non-intersection (explain in narrative)1 (1.3%)
9
Non-intersection: Railroad grade crossing1 (1.3%)

Showing top 9 of 11 reported. 2 additional (2 total) not shown: Intersection: Five points or more, Intersection: Other intersection (explain in narrative).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Vehicle Type

Passenger cars were the most common vehicle type involved in crashes, with 66 vehicles recorded. Light trucks and SUVs were also frequently involved, with 26 pickups and 23 sport utility vehicles, respectively. Twelve tractor-trailers and two motorcycles were also involved in crashes during this period.

Vehicle Type

"Other" combines 5 smaller categories (6 records): Single-unit truck (>= 3 axles) (2), Cargo/panel van (1), Farm equipment (explain in narrative) (1), Train (1), All-terrain vehicle (ATV) (1).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

Traffic Control Device

The vast majority of crashes involved vehicles where no traffic controls were present, with this situation recorded 102 times. In cases where traffic controls were present, stop signs were the most common, noted in 10 instances. Only 2 instances involved yield signs and 1 involved a railway crossing device.

Traffic Control Device

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

Most Damaged Area

Frontal impacts were the most common, with the primary damage area being the front of the vehicle in 22 cases, the front-driver-side corner in 13 cases, and the front-passenger-side corner in 12 cases. Side impacts were also frequent, with the driver's side middle being the most damaged area in 13 instances. Ten crashes resulted in primary damage to the rear of the vehicle.

Most Damaged Area

"Other" combines 9 smaller categories (37 records): Driver side - rear (7), Top (7), Rear - driver side corner (6), Passenger side - middle (5), Rear - passenger side corner (5), Passenger side - rear (3), Undercarriage (2), Other (explain in narrative) (1), Non-collision/no damage (1).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

Crashes by City

Crash data within municipal limits shows Manson had the highest volume with 24 crashes. Lake City followed with 19 crashes, and Rockwell City had 14. Together, these three municipalities accounted for 57 of the 71 crashes that occurred within a city's boundaries.

Crashes by City

1
MANSON24 (33.8%)
2
LAKE CITY19 (26.8%)
3
ROCKWELL CITY14 (19.7%)
4
LYTTON5 (7%)
5
YETTER4 (5.6%)
6
POMEROY3 (4.2%)
7
LOHRVILLE1 (1.4%)
8
KNIERIM1 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Paved vs Unpaved Road

Of the crashes where the road surface type was specified, 92 occurred on paved roads. Seven crashes were recorded on unpaved surfaces, such as gravel or dirt roads, accounting for approximately 7% of these incidents.

Paved vs Unpaved Road

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Roadway Contributing Factor

In the minority of crashes where a roadway factor was cited as a contributor, 'Surface condition' was the most common, noted in 17 incidents. These conditions typically refer to wet or icy surfaces. No other roadway factor was cited more than once.

Roadway Contributing Factor

1
Surface condition (e.g.wet, icy)17 (89.5%)
2
Shoulders (none, low, soft, high)1 (5.3%)
3
Traffic backup, regular congestion1 (5.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Property Damage

The most common estimated property damage cost was in the $1,500 to $7,500 range, which applied to 70 crashes. Eight crashes resulted in high-end damage estimates of $25,000 or more. An additional 18 crashes had damage estimated between $7,500 and $25,000.

Property Damage

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Manner of Collision

Single-vehicle, non-collision events were the most frequent manner of collision, accounting for 50 of the 100 crashes. Among multi-vehicle crashes, broadside (front-to-side) collisions were the most common type, with 16 incidents. Rear-end collisions were the next most frequent, with 10 incidents recorded.

Manner of Collision

"Other" combines 3 smaller categories (5 records): Sideswipe, opposite direction (2), Head-on (front to front) (2), Angle, oncoming left turn (1).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Pre-Crash Driver Action

For vehicles involved in crashes, the most common pre-crash action was 'Movement essentially straight,' recorded for 85 vehicles. The next most frequent actions were 'Legally Parked' and 'Turning left,' each recorded for 11 vehicles. Six vehicles were backing up prior to the collision.

Pre-Crash Driver Action

1
Movement essentially straight85 (63%)
2
Legally Parked11 (8.1%)
3
Turning left11 (8.1%)
4
Backing6 (4.4%)
5
Other (explain in narrative)5 (3.7%)
6
Slowing/stopping (deceleration)5 (3.7%)
7
Overtaking/passing4 (3%)
8
Turning right3 (2.2%)
9
Negotiating a curve2 (1.5%)

Showing top 9 of 12 reported. 3 additional (3 total) not shown: Leaving traffic lane, Leaving a parked position, Changing lanes.

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

Person Type

Of the 182 people involved in crashes, the vast majority, 176, were drivers. The remaining 6 individuals were passengers. No pedestrians or cyclists were involved in any of the recorded crashes.

Person Type

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Person Injury Severity

Across all 182 people involved in crashes, 1 person sustained a fatal injury and 34 sustained non-fatal injuries. The injuries included 5 serious, 15 minor, and 14 possible injuries. The remaining individuals were not injured.

Person Injury Severity

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Occupant Safety Equipment

Based on the limited available data where safety equipment use was recorded, 22 individuals used a shoulder and lap belt. Three individuals were recorded as using no safety restraints. Two motorcycle riders were noted as wearing a DOT-compliant helmet.

Occupant Safety Equipment

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Person-level records linked to crash events

Vehicles Per Crash

Slightly over half of the incidents, 54 out of 100, were single-vehicle crashes. Two-vehicle crashes were also very common, accounting for 45 incidents. One crash involved three vehicles.

Vehicles Per Crash

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

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: 2015-01-01 through 2015-12-31
  • Report generated: September 10, 2026

Data Coverage

  • Reporting period: 2015-01-01 through 2015-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 100
  • Total persons involved: 182
  • Total vehicles involved: 147

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: 2015." Published September 10, 2026. Reporting period: 2015-01-01 to 2015-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2015-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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