Yearly Traffic Safety Analysis

214 CRASHES IN
IOWA, IA
2015

In 2015, Clarke County, Iowa, recorded 214 traffic crashes, which resulted in 75 injuries and no fatalities. A significant portion of these incidents, 29.4%, were attributed to collisions with animals. Overall, property damage was the most common outcome, with 72.4% of crashes involving no injuries.

214

Total Crash Events

0

Persons Killed

75

Persons Injured

0

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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, there were no fatalities recorded for any road user type. A total of 75 individuals sustained injuries. The majority of these were motorists, with 71 injured. Additionally, 2 pedestrians and 1 cyclist were injured in traffic crashes during this period.

0

Pedestrians Killed

0

Cyclists Killed

0

Motorists Killed

0

Other Killed

2

Pedestrians Injured

1

Cyclists Injured

71

Motorists Injured

1

Other 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 Clarke County during 2015 peaked on Wednesdays and Saturdays, with 34 incidents reported on each of those days. The most common time for a crash was the 5 p.m. hour, which saw 21 crashes. While 96 crashes (44.9%) occurred in daylight, crashes in darkness were more frequent on unlighted roadways (37 crashes) than on lighted ones (21 crashes).

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 214 total crashes, 155 (72.4%) resulted in no injuries, making them property-damage-only events. The remaining 59 crashes involved injuries, with 10 (4.7%) classified as serious injury crashes, 22 (10.3%) as minor injury crashes, and 27 (12.6%) as possible injury crashes. There were no fatal crashes recorded in 2015.

Outcome by Severity (Crash Events)

Serious Injury10serious injury crashes4.7%
Minor Injury22minor injury crashes10.3%
Possible Injury27possible injury crashes12.6%
No Injury155no injury crashes72.4%

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 predominant contributing factor to crashes was contact with an animal, cited in 63 of the 214 incidents (29.4%). The next most common factors were vehicles running off a straight road (15 crashes, 7.0%) and drivers losing control (13 crashes, 6.1%). Failure to yield from a stop sign, exceeding the authorized speed, and following too closely each accounted for 10 crashes (4.7% each).

Officer-Reported Primary Contributing Cause

Animal63 (29.4%)
Ran off road - straight15 (7%)
Lost Control13 (6.1%)
Exceeded authorized speed10 (4.7%)
FTYROW: From stop sign10 (4.7%)
Followed too close10 (4.7%)
Ran off road - left9 (4.2%)
Driving too fast for conditions9 (4.2%)
Operating vehicle in an reckless, erratic, careless, negligent manner8 (3.7%)
FTYROW: Making left turn7 (3.3%)

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 substantial number of crashes occurred in what appeared to be ideal driving conditions. Specifically, 99 crashes (46.3%) happened in clear weather, and 106 (49.5%) occurred on dry road surfaces. Daylight was the lighting condition for 96 crashes (44.9%). In contrast, 21 crashes (9.8%) took place during rain, and 30 (14.0%) occurred on wet roads.

Weather

Clear99 (58.9%)
Cloudy34 (20.2%)
Rain21 (12.5%)
Snow9 (5.4%)
Fog, smoke, smog3 (1.8%)
Freezing rain/drizzle1 (0.6%)
Severe Winds1 (0.6%)

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

Lighting

Daylight96 (56.8%)
Dark - roadway not lighted37 (21.9%)
Dark - roadway lighted21 (12.4%)
Dawn10 (5.9%)
Dark - unknown roadway lighting3 (1.8%)
Dusk2 (1.2%)

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

Road Surface

Dry106 (62.7%)
Wet30 (17.8%)
Gravel14 (8.3%)
Snow10 (5.9%)
Slush6 (3.6%)
Mud, dirt2 (1.2%)
Ice/frost1 (0.6%)

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 highest representation among the 16-20 age group (59 people), 26-34 age group (58 people), and 21-25 age group (51 people). The most frequent vehicle makes involved were Ford (49 vehicles) and Chevrolet (a combined 67 vehicles listed as 'CHEV' and 'CHEVROLET'). Dodge vehicles were also common, with a combined 37 vehicles involved.

Top Vehicle Makes (300 vehicles)

1
FORD49 (16.3%)
2
CHEV46 (15.3%)
3
DODG23 (7.7%)
4
CHEVROLET21 (7%)
5
DODGE14 (4.7%)
6
TOYOTA13 (4.3%)
7
JEEP12 (4%)
8
BUIC9 (3%)
9
HONDA7 (2.3%)
10
NR7 (2.3%)

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

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

Sex Distribution (249 persons with recorded sex)

Male149 (59.8%)
Female100 (40.2%)

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 cited major cause for crashes was 'Animal,' accounting for 63 incidents. 'Ran off road - straight' was the second-leading cause with 15 crashes, followed by 'Lost Control' with 13 crashes. Other notable causes included 'Exceeded authorized speed,' 'FTYROW: From stop sign,' and 'Followed too close,' each contributing to 10 crashes.

Major Cause

1
Animal63 (31%)
2
Ran off road - straight15 (7.4%)
3
Lost Control13 (6.4%)
4
Exceeded authorized speed10 (4.9%)
5
FTYROW: From stop sign10 (4.9%)
6
Followed too close10 (4.9%)
7
Ran off road - left9 (4.4%)
8
Driving too fast for conditions9 (4.4%)
9
Operating vehicle in an reckless, erratic, careless, negligent manner8 (3.9%)

Showing top 9 of 33 reported. 24 additional (56 total) not shown: FTYROW: Making left turn, Other (explain in narrative): Other, Improper Backing, Driver Distraction: Other interior distraction, Ran Stop Sign, Made improper turn, Swerving/Evasive Action, Driver Distraction: Passenger, Other (explain in narrative): No improper action, FTYROW: From driveway, Crossed centerline (undivided), Other (explain in narrative): Vision obstructed, Driver Distraction: Manual operation of an electronic communication device, Driver Distraction: Reaching for object(s)/fallen object(s), Driver Distraction: Unrestrained animal, Failed to keep in proper lane, FTYROW: From parked position, FTYROW: To pedestrian, Driver Distraction: Other electronic device activity, Operator inexperience, Passing: Other passing (explain in narrative), Ran off road - right, Driver Distraction: Inattentive/lost in thought, Ran Traffic Signal.

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: Animal,' which occurred in 63 crashes. The second most frequent event was a 'Collision with: Vehicle in traffic,' recorded in 58 incidents. Single-vehicle events were also prominent, including 18 overturn/rollover crashes and 16 crashes where the first harmful event was a collision with a ditch.

First Harmful Event

1
Collision with: Animal63 (29.6%)
2
Collision with: Vehicle in traffic58 (27.2%)
3
Non-collision events: Overturn/rollover18 (8.5%)
4
Collision with fixed object: Ditch16 (7.5%)
5
Collision with: Parked motor vehicle8 (3.8%)
6
Non-collision events: Other non-collision (explain in narrative)7 (3.3%)
7
Collision with: Re-entering roadway6 (2.8%)
8
Collision with fixed object: Utility pole/light support5 (2.3%)
9
Collision with fixed object: Cable barrier5 (2.3%)

Showing top 9 of 23 reported. 14 additional (27 total) not shown: Collision with: Non-motorist (see non-motorist section - NOT a unit), Collision with fixed object: Tree, Collision with fixed object: Embankment, Collision with fixed object: Guardrail - face, Collision with fixed object: Other post/pole/support (explain in narrative), Collision with fixed object: Curb/island/raised median, Non-collision events: Non-contact vehicle (phantom), Collision with fixed object: Concrete traffic barrier (median or right side), Collision with fixed object: Bridge/bridge rail parapet, Miscellaneous events: Hit and run, Other (explain in narrative), Collision with fixed object: Guardrail - end, Collision with: Other non-fixed object (explain in narrative), Collision with: Thrown or falling object.

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

Roadway Junction / Feature

The majority of crashes, 97 out of 214, occurred on non-intersection road segments. For crashes at intersections, four-way intersections were the most common location with 19 incidents, followed by T-intersections with 12 incidents. An additional 14 crashes were related to driveway access.

Roadway Junction / Feature

1
Non-intersection: Non-junction/no special feature97 (57.1%)
2
Intersection: Four-way intersection19 (11.2%)
3
Intersection: T-intersection12 (7.1%)
4
Non-intersection: Driveway access (related, not in)7 (4.1%)
5
Non-intersection: Driveway access (within)7 (4.1%)
6
Non-intersection: Other non-intersection (explain in narrative)5 (2.9%)
7
Non-intersection: Alley4 (2.4%)
8
Interchange-related: Off-ramp4 (2.4%)
9
Intersection: Other intersection (explain in narrative)3 (1.8%)

Showing top 9 of 17 reported. 8 additional (12 total) not shown: Interchange-related: On-ramp merge area, Intersection: Intersection with ramp, Intersection: Five points or more, Intersection: L-intersection, Intersection: Traffic circle, Interchange-related: On-ramp, Interchange-related: Other interchange (explain in narrative), Interchange-related: Mainline, between ramps.

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

Vehicle Type

Passenger cars were the most prevalent vehicle type involved in crashes, with 149 units recorded. Four-tire light trucks or pickups were the second most common with 61 units, followed by Sport Utility Vehicles with 47 units. There were 13 tractor/semi-trailers involved in crashes during this period.

Vehicle Type

"Other" combines 5 smaller categories (7 records): Motorcycle (2), Truck tractor (bobtail) (2), Single-unit truck (>= 3 axles) (1), Truck/trailer (1), Tractor/doubles (1).

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

Traffic Control Device

A significant majority of crashes, 201 out of 214, occurred at locations with no traffic controls present. Where traffic controls were in place, stop signs were the most common type, associated with 26 crashes. Traffic signals were present at the location of 12 crashes.

Traffic Control Device

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

Most Damaged Area

The front of the vehicle was the most common area of damage, reported in 60 cases. This was followed by damage to the driver's side middle area (23 cases), the front passenger side corner (20 cases), and the front driver side corner (19 cases). This distribution points to a prevalence of frontal and angle impacts.

Most Damaged Area

"Other" combines 9 smaller categories (75 records): Rear - driver side corner (15), Driver side - rear (12), Passenger side - front (11), Passenger side - middle (11), Passenger side - rear (8), Rear - passenger side corner (8), Non-collision/no damage (5), Other (explain in narrative) (4), Undercarriage (1).

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

Crashes by City

Of the crashes occurring within city limits in Clarke County, the vast majority were in Osceola, which recorded 93 incidents. The towns of Murray and Woodburn each had only one crash reported. This indicates that most municipal crashes in the county are concentrated in Osceola.

Crashes by City

1
OSCEOLA93 (97.9%)
2
MURRAY1 (1.1%)
3
WOODBURN1 (1.1%)

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

Paved vs Unpaved Road

Crashes predominantly occurred on paved roadways, accounting for 185 incidents. A notable minority of 26 crashes, representing 12.1% of the total, took place on unpaved surfaces such as gravel or dirt roads.

Paved vs Unpaved Road

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

Roadway Contributing Factor

When a roadway factor was identified as a contributor, the most common was 'Surface condition (e.g. wet, icy),' which was noted in 22 crashes. 'Slippery, loose or worn surface' was cited in 3 crashes, and roadway work zones were a factor in 2 incidents.

Roadway Contributing Factor

1
Surface condition (e.g.wet, icy)22 (78.6%)
2
Slippery, loose or worn surface3 (10.7%)
3
Work Zone (roadway-related)2 (7.1%)
4
Obstruction in roadway1 (3.6%)

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

Driver Condition

Among drivers for whom a condition other than 'apparently normal' was recorded, 8 were noted as being under the influence of alcohol. Additionally, 7 drivers were recorded as asleep or fatigued, and one was under the influence of drugs or medication.

Driver Condition

1
Under the influence of alcohol8 (44.4%)
2
Asleep/fatigued7 (38.9%)
3
Emotional (e.g. depressed, angry)2 (11.1%)
4
Under the influence of drugs/meds1 (5.6%)

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

Property Damage

The most common estimated cost of property damage fell within the $1,500 to $7,500 range, which described 167 crashes. A smaller number of crashes resulted in higher damage, with 36 incidents causing between $7,500 and $25,000 in damage, and 5 crashes exceeding $25,000 in damage.

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 crashes were the dominant crash type, accounting for 117 of the 214 incidents (54.7%). Among crashes involving multiple vehicles, rear-end collisions were the most frequent with 20 occurrences (9.3%), followed by broadside collisions with 17 occurrences (7.9%).

Manner of Collision

"Other" combines 3 smaller categories (9 records): Angle, oncoming left turn (4), Head-on (front to front) (4), Rear to rear (1).

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

Pre-Crash Driver Action

The most common action immediately preceding a crash was 'Movement essentially straight,' recorded for 155 vehicles. Other frequent pre-crash actions included 'Turning left' (21 vehicles), being 'Legally Parked' (19 vehicles), and 'Backing' (14 vehicles).

Pre-Crash Driver Action

1
Movement essentially straight155 (59.8%)
2
Turning left21 (8.1%)
3
Legally Parked19 (7.3%)
4
Backing14 (5.4%)
5
Turning right13 (5%)
6
Other (explain in narrative)10 (3.9%)
7
Negotiating a curve8 (3.1%)
8
Slowing/stopping (deceleration)7 (2.7%)
9
Overtaking/passing3 (1.2%)

Showing top 9 of 13 reported. 4 additional (9 total) not shown: Stopped in traffic, Entering traffic lane (merging), Changing lanes, Accelerating in road.

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

Person Type

Of the 379 individuals involved in crashes, the overwhelming majority, 353, were drivers. Passengers constituted the next largest group with 22 individuals. The remaining people involved included 2 pedestrians, 1 bicyclist, and 1 other non-motorist.

Person Type

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

Person Injury Severity

Across all 379 people involved in crashes, 11 sustained serious injuries, 33 suffered minor injuries, and 31 had possible injuries. This indicates that a total of 75 individuals were injured to some degree. The remaining individuals were either not injured or their injury status was not applicable.

Person Injury Severity

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

Occupant Safety Equipment

Among the individuals for whom safety equipment use was recorded, 45 were documented as using a shoulder and lap belt. In contrast, 13 individuals were recorded as having used no safety equipment at the time of the crash.

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

Single-vehicle crashes were the most common scenario, with 133 such incidents reported. Crashes involving two vehicles were also frequent, totaling 77 incidents. Multi-vehicle pile-ups were rare, with only 3 crashes involving three vehicles and one crash involving four 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 9, 2026

Data Coverage

  • Reporting period: 2015-01-01 through 2015-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 214
  • Total persons involved: 379
  • Total vehicles involved: 300

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 9, 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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