ThatCarHitMe.com
An Injuria.ai Company
CRASH INTELLIGENCE REPORT · IOWA, IA · 2015
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/iowa/statewide/2015-annual-report
Yearly Traffic Safety Analysis
459 CRASHES IN
IOWA, IA
2015
In 2015, Sioux County recorded 459 total traffic crashes, resulting in 3 fatalities and 207 injuries. A notable finding from the data is that collisions with animals were the leading contributing factor, accounting for 65 crashes, or 14.2% of the total.
459
Total Crash Events
3
Persons Killed
207
Persons Injured
3
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 · 2015-01-01 to 2015-12-31 · Aggregate counts from crash, person, and vehicle records
Vulnerable Road User Casualties
An analysis of killed and injured individuals shows that vehicle occupants were the most affected group, with 2 motorists killed and 202 injured. There were no pedestrian or cyclist fatalities recorded in 2015. However, 2 pedestrians and 3 cyclists sustained injuries in traffic crashes.
0
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
1
Other Killed
2
Pedestrians Injured
3
Cyclists Injured
202
Motorists Injured
0
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
Crash analysis by time reveals distinct patterns, with incidents peaking on Thursdays, which saw 91 crashes. The most common time for a crash was the 3 p.m. hour, which recorded 39 incidents. The data also shows that a majority of crashes, 277 in total, occurred 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
The majority of crashes in Sioux County in 2015 did not result in injury, with 312 property-damage-only incidents accounting for 68% of the total. The remaining 32% of crashes involved some level of injury. There were 3 fatal crashes recorded, which resulted in a total of 3 fatalities.
Outcome by Severity (Crash Events)
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
An analysis of contributing factors indicates that collisions involving an animal were the most common cause, cited in 65 crashes (14.2%). This was closely followed by 'Followed too close,' which was a factor in 63 incidents (13.7%). Other significant factors included 'Driving too fast for conditions' and 'Lost Control,' each contributing to 31 crashes (6.8%).
Officer-Reported Primary Contributing Cause
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The data indicates that most crashes occurred in ideal driving conditions. A total of 277 crashes (60.3%) happened in daylight, 227 (49.5%) in clear weather, and 265 (57.7%) on dry road surfaces. Crashes in adverse conditions were less frequent, with 102 incidents occurring in cloudy weather and 44 on snowy roads.
Weather
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Weather condition at time of crash
Lighting
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Lighting condition field
Road Surface
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Road surface condition field
Vehicles & Demographics
Among the 956 people involved in crashes, the 16-20 age group was the most represented, with 178 individuals, followed by the 26-34 age group with 121 individuals. An analysis of the 743 vehicles involved shows that Chevrolet (190 vehicles), Ford (145 vehicles), and Dodge (42 vehicles) were the most frequently recorded makes.
Top Vehicle Makes (743 vehicles)
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records
45 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (669 persons with recorded sex)
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Person-level records linked to crash events
Major Cause
According to the major cause data, collisions with an animal were the leading factor in crashes, accounting for 65 incidents. 'Followed too close' was the second most frequent cause, cited in 63 crashes. Other primary causes included 'Driving too fast for conditions' and 'Lost Control,' each attributed to 31 crashes, and 'Failure to yield right of way from a stop sign,' noted in 30 crashes.
Major Cause
Showing top 9 of 43 reported. 34 additional (142 total) not shown: FTYROW: Making left turn, FTYROW: From driveway, Other (explain in narrative): Other, Improper Backing, Made improper turn, Operating vehicle in an reckless, erratic, careless, negligent manner, Driver Distraction: Other interior distraction, Swerving/Evasive Action, Driver Distraction: Adjusting devices (radio, climate), Exceeded authorized speed, Crossed centerline (undivided), FTYROW: From yield sign, Other (explain in narrative): No improper action, Driver Distraction: Talking on a hand-held device, FTYROW: From parked position, FTYROW: Other (explain in narrative), Failed to keep in proper lane, Driver Distraction: Exterior distraction, Ran off road - right, Other (explain in narrative): Vision obstructed, Driver Distraction: Passenger, Ran Traffic Signal, Separation of units, Driver Distraction: Inattentive/lost in thought, Cargo/equipment loss or shift, Traveling wrong way or on wrong side of road, Equipment failure, Driver Distraction: Other electronic device activity, Passing: On wrong side, Passing: Other passing (explain in narrative), Passing: Through/around barrier, FTYROW: To pedestrian, Driver Distraction: Manual operation of an electronic communication device, Driver Distraction: Reaching for object(s)/fallen object(s).
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 a vehicle in traffic,' which occurred in 244 crashes, representing 53.2% of all incidents. Collisions with animals were the second most frequent event, with 65 cases. Single-vehicle run-off-road events were also significant, including 42 overturns or rollovers and 31 collisions with a ditch.
First Harmful Event
Showing top 9 of 29 reported. 20 additional (43 total) not shown: Collision with fixed object: Utility pole/light support, Collision with fixed object: Guardrail - face, Collision with fixed object: Traffic sign support, Collision with fixed object: Other fixed object (explain in narrative), Non-collision events: Jackknife, Collision with: Other non-fixed object (explain in narrative), Other (explain in narrative), Collision with fixed object: Tree, Collision with fixed object: Curb/island/raised median, Collision with fixed object: Mailbox, Collision with fixed object: Snow bank, Collision with: Struck/struck by object/cargo/person from other vehicle, Non-collision events: Other non-collision (explain in narrative), Collision with fixed object: Culvert/pipe opening, Non-collision events: Fell/jumped from vehicle, Collision with fixed object: Building, Collision with fixed object: Bridge pier or support, Non-collision events: Vehicle went airborne, Collision with fixed object: Ground, Collision with fixed object: Bridge overhead structure.
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records
Roadway Junction / Feature
The data shows that more crashes occurred at non-junction locations than at intersections. A total of 252 crashes (54.9%) happened on road segments away from junctions, including 27 related to driveway access. In contrast, 154 crashes (33.5%) occurred within or related to an intersection, with four-way intersections being the most common type, accounting for 119 incidents.
Roadway Junction / Feature
Showing top 9 of 11 reported. 2 additional (2 total) not shown: Intersection: L-intersection, Non-intersection: Alley.
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 288 units recorded, followed by four-tire light trucks (180) and sport utility vehicles (143). Commercial vehicles, such as tractor-trailers, were involved in 34 crashes, while motorcycles were involved in 7.
Vehicle Type
"Other" combines 10 smaller categories (34 records): Farm tractor (8), Motorcycle (7), Truck/trailer (4), Moped (4), Cargo/panel van (4), School bus (seats > 15) (2), Passenger van (seats 9-15) (2), Small school bus (seats 9-15) (1), Motor home/recreational vehicle (1), Farm equipment (explain in narrative) (1).
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records
Traffic Control Device
The data shows that 496 vehicles involved in crashes were in areas with 'No controls present'. Among vehicles at controlled locations, stop signs were the most frequent type, associated with 84 vehicles, followed by traffic signals, which were present for 72 vehicles.
Traffic Control Device
"Other" combines 4 smaller categories (6 records): Railway crossing device (3), Traffic director (person) (1), Work zone sign (1), Flashing traffic control signal (1).
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 vehicle damage, with the front area being the most damaged point on 170 vehicles. Including corner impacts, frontal damage was noted on 290 vehicles in total. Rear-end collisions were indicated by 78 vehicles with primary damage to the rear, while side impacts were the main damage area for 72 vehicles.
Most Damaged Area
"Other" combines 10 smaller categories (209 records): Driver side - front (32), Rear - passenger side corner (31), Rear - driver side corner (31), Driver side - rear (27), Passenger side - front (26), Other (explain in narrative) (24), Passenger side - rear (20), Non-collision/no damage (9), Undercarriage (6), Cargo loss (3).
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records
Impairment (Alcohol / Drugs)
The data indicates that 16 crashes, or 3.5% of the total, involved a driver under the influence. Among the 23 total instances of impairment recorded for individuals involved in crashes, alcohol was cited in 20 cases, drugs in 2 cases, and a combination of alcohol and drugs in 1 case.
Crashes by City
Within Sioux County, crash incidents were most concentrated in Sioux Center, which recorded 95 crashes in 2015. Orange City had the second-highest number with 54 crashes, followed by Rock Valley with 24 crashes. Other municipalities with notable crash counts include Hull (18) and Hawarden (14).
Crashes by City
Showing top 9 of 13 reported. 4 additional (6 total) not shown: MAURICE, GRANVILLE, CHATSWORTH, IRETON.
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records
Paved vs Unpaved Road
The vast majority of crashes, 422 in total, occurred on paved roadways. A smaller number of incidents, 35 crashes, took place on unpaved surfaces such as gravel or dirt roads, accounting for approximately 7.7% of crashes where road surface type was specified.
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 cases where a roadway factor was identified as a contributor, 'Surface condition,' such as wet or icy roads, was the most common, cited in 70 crashes. Other roadway factors were noted infrequently, with 'Shoulders' being a factor in 2 crashes and 'Work Zone' and 'Debris' each contributing to a single crash.
Roadway Contributing Factor
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, being 'Under the influence of alcohol' was the most frequent, noted for 15 drivers. Driver fatigue was also a factor, with 5 drivers recorded as 'Asleep/fatigued'. Medical conditions were cited for 3 drivers.
Driver Condition
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records
Property Damage
Officer-estimated property damage costs show that the most common range was '$1,500 - $7,500,' which applied to 288 crashes. A significant number of crashes, 144, resulted in damages between '$7,500 - $25,000'. High-damage crashes, with costs exceeding $25,000, accounted for 18 incidents, or 3.9% of the total.
Property Damage
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records
Manner of Collision
The most frequent type of crash was a non-collision event involving a single vehicle, such as running off the road or an overturn, which accounted for 176 incidents (38.3%). Among multi-vehicle crashes, rear-end collisions were the most common, with 96 occurrences (20.9%), followed by broadside collisions, which happened in 86 crashes (18.7%).
Manner of Collision
"Other" combines 3 smaller categories (16 records): Other (explain in narrative) (7), Rear to rear (5), Head-on (front to front) (4).
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records
Pre-Crash Driver Action
An analysis of pre-crash actions for the 743 vehicles involved shows that the majority, 450 vehicles (60.6%), were moving straight ahead. The next most common action was turning left, which was the maneuver for 62 vehicles. Slowing or stopping was the recorded action for 38 vehicles prior to impact.
Pre-Crash Driver Action
Showing top 9 of 18 reported. 9 additional (18 total) not shown: Entering traffic lane (merging), Leaving a parked position, Overtaking/passing, Accelerating in road, Leaving traffic lane, Starting in road, Illegally Parked/Unattended, Changing lanes, Making U-turn.
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records
Person Type
Of the 956 individuals involved in crashes, the vast majority were drivers, accounting for 902 people or 94.3% of the total. Passengers made up the next largest group with 48 individuals. A small number of non-motorists were also involved, including 3 bicyclists and 2 pedestrians.
Person Type
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records
Person Injury Severity
Out of the 956 people involved in crashes, 3 sustained fatal injuries and 207 sustained some level of injury. This indicates that approximately 22% of all individuals involved in crashes were either killed or injured. The specific breakdown of injuries includes 19 serious, 97 minor, and 91 possible injuries.
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 168 vehicle occupants for whom safety equipment use was documented, 145 were recorded as using a shoulder and lap belt. A total of 19 individuals, or 11.3% of this subset, were noted as using 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
The data shows that two-vehicle collisions were the most common scenario, accounting for 251 crashes. Single-vehicle crashes were also frequent, with 192 incidents, representing 41.8% of the total. Crashes involving three or more vehicles were rare, with only 15 three-vehicle crashes and one four-vehicle crash recorded.
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: 459
- Total persons involved: 956
- Total vehicles involved: 743
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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Iowa Crash Data · ArcGIS
Period: 2015-01-01 – 2015-12-31
Generated: September 9, 2026 · All rights reserved