Yearly Traffic Safety Analysis

97 CRASHES IN
IOWA, IA
2015

In 2015, Worth County recorded 97 traffic crashes, resulting in 2 fatalities and 33 injuries. A significant statistical finding is the high prevalence of animal-related incidents, which were cited as the primary contributing factor in 29.9% of all crashes. The majority of crashes involved a single vehicle and resulted in only property damage.

97

Total Crash Events

2

Persons Killed

33

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

All traffic fatalities and injuries in this period involved motor vehicle occupants. A total of 2 motorists were killed and 33 were injured. According to the data, there were no fatalities or injuries involving pedestrians or cyclists.

2

Motorists Killed

33

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 Worth County peaked on Sundays, with 20 incidents recorded on that day of the week. The data shows bimodal peaks during the day, with 7 crashes occurring in the 5 a.m. hour and another 7 in the 10 a.m. hour. Collision occurrences were almost evenly split between daylight hours (44 crashes) and dark or low-light conditions (38 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

The vast majority of crashes, 70 out of 97 (72.2%), resulted in no injuries and were classified as property-damage-only. The remaining 27 crashes involved some level of injury, including 4 with serious injuries and 2 fatal crashes. In this period, the 2 fatal crashes resulted in a total of 2 fatalities.

Outcome by Severity (Crash Events)

Fatal2fatal crashes2.1%
Serious Injury4serious injury crashes4.1%
Minor Injury10minor injury crashes10.3%
Possible Injury11possible injury crashes11.3%
No Injury70no injury crashes72.2%

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 most dominant contributing factor for crashes was animal involvement, cited in 29 incidents, or 29.9% of the total. Following this, losing control of the vehicle was a factor in 11 crashes (11.3%). Driving too fast for conditions and running off a straight road were each responsible for 8 crashes (8.2%).

Officer-Reported Primary Contributing Cause

Animal29 (29.9%)
Lost Control11 (11.3%)
Driving too fast for conditions8 (8.2%)
Ran off road - straight8 (8.2%)
Followed too close7 (7.2%)
Ran off road - left6 (6.2%)
FTYROW: From stop sign4 (4.1%)
Improper or erratic lane changing2 (2.1%)
Cargo/equipment loss or shift2 (2.1%)
Ran Stop Sign2 (2.1%)

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 significant portion of crashes occurred in ideal driving conditions, with 49 of 97 incidents (50.5%) on dry roads and 40 (41.2%) in clear weather. Forty-four crashes (45.4%) took place during daylight hours. Nevertheless, adverse conditions were also a factor, with 26 crashes occurring during snow or rain and 32 happening on roads that were not dry.

Weather

Clear40 (48.8%)
Cloudy16 (19.5%)
Snow13 (15.9%)
Rain5 (6.1%)
Freezing rain/drizzle4 (4.9%)
Blowing Snow2 (2.4%)
Fog, smoke, smog2 (2.4%)

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

Lighting

Daylight44 (53.7%)
Dark - roadway not lighted28 (34.1%)
Dusk6 (7.3%)
Dark - roadway lighted2 (2.4%)
Dawn2 (2.4%)

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

Road Surface

Dry49 (59.8%)
Snow13 (15.9%)
Wet9 (11.0%)
Ice/frost6 (7.3%)
Slush4 (4.9%)
Gravel1 (1.2%)

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

Vehicles & Demographics

The 45-54 age group was the most represented demographic among persons involved in crashes, accounting for 36 of the 174 individuals. An analysis of vehicles involved shows that Chevrolet (including abbreviated 'CHEV' entries) was the most frequent make with 38 vehicles, followed by Ford with 22 vehicles.

Top Vehicle Makes (139 vehicles)

1
CHEVROLET25 (18%)
2
FORD22 (15.8%)
3
CHEV13 (9.4%)
4
TOYOTA8 (5.8%)
5
BUIC6 (4.3%)
6
INTERNATIONA6 (4.3%)
7
TOYT4 (2.9%)
8
DODG4 (2.9%)
9
DODGE4 (2.9%)
10
NISSAN3 (2.2%)

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

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

Sex Distribution (110 persons with recorded sex)

Male79 (71.8%)
Female31 (28.2%)

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

Major Cause

The leading major cause identified for crashes was an animal, which was cited in 29 incidents (29.9%). The second most common cause was a driver losing control, contributing to 11 crashes (11.3%). Driving too fast for conditions and running off a straight road were tied for the third-leading cause, each accounting for 8 crashes.

Major Cause

1
Animal29 (30.2%)
2
Lost Control11 (11.5%)
3
Driving too fast for conditions8 (8.3%)
4
Ran off road - straight8 (8.3%)
5
Followed too close7 (7.3%)
6
Ran off road - left6 (6.3%)
7
FTYROW: From stop sign4 (4.2%)
8
Improper or erratic lane changing2 (2.1%)
9
Cargo/equipment loss or shift2 (2.1%)

Showing top 9 of 25 reported. 16 additional (19 total) not shown: Ran Stop Sign, Driver Distraction: Exterior distraction, Other (explain in narrative): Vision obstructed, FTYROW: From parked position, Driver Distraction: Inattentive/lost in thought, FTYROW: Making left turn, FTYROW: Other (explain in narrative), Operating vehicle in an reckless, erratic, careless, negligent manner, Other (explain in narrative): Other, Crossed centerline (undivided), Swerving/Evasive Action, Driver Distraction: Talking on a hands free device, Driver Distraction: Reaching for object(s)/fallen object(s), Driver Distraction: Other interior distraction, FTYROW: At uncontrolled intersection, FTYROW: From driveway.

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

First Harmful Event

The most frequent initial event in a crash sequence was a collision with another vehicle in traffic, which occurred in 33 of the 97 crashes. This was followed closely by collision with an animal, the first harmful event in 29 crashes. A non-collision event, specifically an overturn or rollover, was the primary event in 9 incidents.

First Harmful Event

1
Collision with: Vehicle in traffic33 (34%)
2
Collision with: Animal29 (29.9%)
3
Non-collision events: Overturn/rollover9 (9.3%)
4
Collision with fixed object: Ditch4 (4.1%)
5
Collision with fixed object: Other post/pole/support (explain in narrative)3 (3.1%)
6
Collision with fixed object: Embankment3 (3.1%)
7
Collision with fixed object: Utility pole/light support3 (3.1%)
8
Collision with fixed object: Bridge/bridge rail parapet2 (2.1%)
9
Collision with: Other non-fixed object (explain in narrative)2 (2.1%)

Showing top 9 of 16 reported. 7 additional (9 total) not shown: Collision with: Re-entering roadway, Collision with: Thrown or falling object, Collision with fixed object: Fence, Collision with fixed object: Guardrail - end, Collision with fixed object: Traffic sign support, Collision with fixed object: Tree, Collision with fixed object: Guardrail - face.

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

Roadway Junction / Feature

The majority of collisions, 57 out of 97 (58.8%), occurred on non-intersection segments of the roadway. Crashes at intersections were less common, with 7 occurring at four-way intersections and 3 at T-intersections. Another 4 crashes were related to driveway access points.

Roadway Junction / Feature

1
Non-intersection: Non-junction/no special feature57 (69.5%)
2
Intersection: Four-way intersection7 (8.5%)
3
Intersection: Other intersection (explain in narrative)4 (4.9%)
4
Non-intersection: Driveway access (related, not in)4 (4.9%)
5
Intersection: T-intersection3 (3.7%)
6
Non-intersection: Other non-intersection (explain in narrative)2 (2.4%)
7
Interchange-related: Off-ramp1 (1.2%)
8
Interchange-related: Other interchange (explain in narrative)1 (1.2%)
9
Non-intersection: Driveway access (within)1 (1.2%)

Showing top 9 of 11 reported. 2 additional (2 total) not shown: Interchange-related: On-ramp merge area, Interchange-related: Off-ramp, diverge area.

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, accounting for 58 of the 139 vehicles. Sport utility vehicles (25) and four-tire light trucks or pickups (21) were the next most common. Ten tractor/semi-trailers and 3 motorcycles were also involved in collisions during this period.

Vehicle Type

"Other" combines 5 smaller categories (9 records): Single unit truck (2-axle, 6-tire) (3), Single-unit truck (>= 3 axles) (2), Motor home/recreational vehicle (2), Maintenance/construction vehicle (1), Truck/trailer (1).

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

Traffic Control Device

Data shows that the majority of vehicles involved in crashes were in areas without traffic controls. Of the 139 vehicles in collisions, 101 were in locations where no controls were present. For vehicles where a control was noted, stop signs were the most common, applying to 12 vehicles.

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 type of damage sustained by vehicles. A total of 49 vehicles had their most significant damage to the front or a front corner. In contrast, damage to the rear or a rear corner, often associated with rear-end collisions, was noted as the primary impact area for 16 vehicles.

Most Damaged Area

"Other" combines 10 smaller categories (36 records): Top (5), Other (explain in narrative) (5), Passenger side - rear (5), Driver side - rear (5), Rear - passenger side corner (4), Rear - driver side corner (4), Undercarriage (3), Non-collision/no damage (2), Driver side - middle (2), Cargo loss (1).

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

Crashes by City

Within Worth County, the city of Northwood had the highest number of crashes, with 18 incidents reported. Following Northwood, Manly recorded 6 crashes, Fertile had 5, and both Grafton and Hanlontown reported 4 crashes each. A significant number of crashes occurred in unincorporated areas and are not reflected in this city-specific breakdown.

Crashes by City

1
NORTHWOOD18 (45%)
2
MANLY6 (15%)
3
FERTILE5 (12.5%)
4
GRAFTON4 (10%)
5
HANLONTOWN4 (10%)
6
KENSETT2 (5%)
7
JOICE1 (2.5%)

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

Paved vs Unpaved Road

The overwhelming majority of crashes, 93 out of 97, took place on paved road surfaces. Crashes on unpaved roads, such as gravel or dirt, were infrequent, accounting for only 4 incidents (4.1%) in this period.

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 deemed a contributor, adverse surface conditions like wet or icy roads were the primary issue, cited in 17 incidents. Other factors such as debris on the roadway, a slippery surface, or a traffic backup from a prior crash were each noted in 2 crashes.

Roadway Contributing Factor

1
Surface condition (e.g.wet, icy)17 (73.9%)
2
Debris2 (8.7%)
3
Slippery, loose or worn surface2 (8.7%)
4
Traffic backup, prior crash2 (8.7%)

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

Property Damage

The most common range for officer-estimated property damage was between $1,500 and $7,500, a category that included 60 of the 97 crashes. A smaller number of crashes, 34, resulted in damages estimated between $7,500 and $25,000. Only 2 crashes were reported to have damage costs exceeding $25,000.

Property Damage

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

Manner of Collision

Nearly half of all incidents, 47 of 97 (48.5%), were single-vehicle crashes, primarily classified as non-collision events like running off the road. Among crashes involving more than one vehicle, the most frequent type was a sideswipe between vehicles traveling in the same direction, which occurred 13 times. This was followed by broadside collisions, which accounted for 11 crashes.

Manner of Collision

"Other" combines 1 smaller categories (1 records): Head-on (front to front) (1).

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

Pre-Crash Driver Action

The data on vehicle actions prior to a collision shows that 95 of the 139 vehicles involved were simply moving straight ahead. Actions such as turning, changing lanes, or overtaking were significantly less common, with each of these maneuvers being the pre-crash action for only 4 vehicles respectively.

Pre-Crash Driver Action

1
Movement essentially straight95 (75.4%)
2
Changing lanes4 (3.2%)
3
Turning left4 (3.2%)
4
Overtaking/passing4 (3.2%)
5
Turning right3 (2.4%)
6
Illegally Parked/Unattended2 (1.6%)
7
Backing2 (1.6%)
8
Slowing/stopping (deceleration)2 (1.6%)
9
Starting in road2 (1.6%)

Showing top 9 of 17 reported. 8 additional (8 total) not shown: Entering traffic lane (merging), Leaving a parked position, Leaving traffic lane, Legally Parked, Making U-turn, Negotiating a curve, Other (explain in narrative), Stopped in traffic.

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

Person Type

Of the 174 individuals involved in traffic crashes, the vast majority, 166 people (95.4%), were drivers. The remaining 8 individuals were passengers in the vehicles. No pedestrians, cyclists, or other non-occupant types were involved in any reported crashes.

Person Type

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

Person Injury Severity

Out of 174 people involved in crashes, 35 sustained some level of injury or were killed. This total includes 2 fatalities, 5 serious injuries, and a combined 28 individuals with minor or possible injuries. The remaining 139 people 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

Among the 27 vehicle occupants for whom safety equipment usage was recorded, 21 were reported as using both a shoulder and lap belt. Notably, 4 individuals in this small subset were documented as using no safety restraint at all 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 predominant incident type, accounting for 57 of the 97 total crashes (58.8%). Collisions involving two vehicles were also common, with 38 such incidents (39.2%). Multi-vehicle pileups were rare, with only 2 crashes involving 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 9, 2026

Data Coverage

  • Reporting period: 2015-01-01 through 2015-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 97
  • Total persons involved: 174
  • Total vehicles involved: 139

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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