Yearly Traffic Safety Analysis

945 CRASHES IN
IOWA, IA
2015

In 2015, Clinton County recorded 945 traffic crashes, which resulted in 3 fatalities and 394 injuries. A significant portion of these incidents, 13.4% or 127 crashes, were attributed to collisions involving animals, making it the most frequently cited contributing factor in the county.

945

Total Crash Events

3

Persons Killed

394

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

In 2015, motorists accounted for all 3 fatalities and the vast majority of injuries, with 376 motorists injured in crashes. There were no fatalities among vulnerable road users. However, 11 cyclists and 7 pedestrians sustained injuries in traffic collisions during this period.

0

Pedestrians Killed

0

Cyclists Killed

3

Motorists Killed

7

Pedestrians Injured

11

Cyclists Injured

376

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

Crash frequency in Clinton County peaked on Fridays, with 162 incidents reported in 2015. The most common time for crashes was the 5 p.m. hour, which saw 77 collisions. Analysis of lighting conditions shows that a majority of crashes, 593 in total (62.8%), 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

Of the 945 total crashes, 68.3% (645 incidents) resulted in no injuries, while the remaining 31.7% involved at least one possible, minor, serious, or fatal injury. There were 3 fatal crashes recorded, which resulted in a total of 3 fatalities. Additionally, 22 crashes were classified as causing serious injuries and 96 resulted in minor injuries.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.3%
Serious Injury22serious injury crashes2.3%
Minor Injury96minor injury crashes10.2%
Possible Injury179possible injury crashes18.9%
No Injury645no injury crashes68.3%

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 common contributing factor identified in Clinton County crashes was the presence of an animal on the roadway, cited in 127 incidents (13.4% of the total). Other leading factors included failure to yield the right-of-way from a stop sign (88 crashes), losing control of the vehicle (76 crashes), and following too closely (63 crashes). Various forms of driver distraction were noted in a smaller number of cases, including other interior distraction (14 cases) and manual operation of an electronic device (5 cases).

Officer-Reported Primary Contributing Cause

Animal127 (13.4%)
FTYROW: From stop sign88 (9.3%)
Other (explain in narrative): Other78 (8.3%)
Lost Control76 (8%)
Followed too close63 (6.7%)
FTYROW: Making left turn51 (5.4%)
Ran off road - straight34 (3.6%)
Ran off road - left31 (3.3%)
Driving too fast for conditions31 (3.3%)
Operating vehicle in an reckless, erratic, careless, negligent manner29 (3.1%)

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

Road & Environmental Conditions

The majority of crashes in 2015 occurred in ideal driving conditions, with 62.8% (593 crashes) happening in daylight, 62.2% (588 crashes) on dry road surfaces, and 56.1% (530 crashes) in clear weather. Adverse weather was a factor in a smaller subset of incidents, with rain present in 61 crashes and snow in 29. Similarly, non-dry road surfaces were recorded in a significant minority of crashes, including 112 on wet roads and 55 on snow-covered roads.

Weather

Clear530 (64.2%)
Cloudy174 (21.1%)
Rain61 (7.4%)
Snow29 (3.5%)
Freezing rain/drizzle9 (1.1%)
Sleet, hail7 (0.8%)
Fog, smoke, smog7 (0.8%)
Blowing Snow6 (0.7%)
Severe Winds2 (0.2%)

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

Lighting

Daylight593 (70.9%)
Dark - roadway lighted113 (13.5%)
Dark - roadway not lighted97 (11.6%)
Dusk21 (2.5%)
Dawn11 (1.3%)
Dark - unknown roadway lighting1 (0.1%)

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

Road Surface

Dry588 (70.5%)
Wet112 (13.4%)
Snow55 (6.6%)
Ice/frost41 (4.9%)
Slush19 (2.3%)
Gravel17 (2.0%)
Other (explain in narrative)1 (0.1%)
Mud, dirt1 (0.1%)

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

Vehicles & Demographics

Among all 2,012 persons involved in crashes, the 26-34 age group was the most represented, with 290 individuals. The 35-44 age group (254 individuals) and the 65+ age group (248 individuals) were also frequently involved. Analysis of the 1,596 vehicles in these crashes shows that Chevrolet (364 vehicles), Ford (236 vehicles), and Dodge (130 vehicles) were the most common makes involved.

Top Vehicle Makes (1,596 vehicles)

1
FORD236 (14.8%)
2
CHEV218 (13.7%)
3
CHEVROLET146 (9.1%)
4
DODG68 (4.3%)
5
DODGE62 (3.9%)
6
GMC58 (3.6%)
7
PONT53 (3.3%)
8
TOYT53 (3.3%)
9
TOYOTA48 (3%)
10
JEEP42 (2.6%)

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

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

Sex Distribution (1,323 persons with recorded sex)

Male726 (54.9%)
Female597 (45.1%)

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 of crashes as coded by Iowa DOT was 'Animal,' which was attributed to 127 incidents. Failure to yield the right-of-way was a significant issue, with 88 crashes caused by failing to yield from a stop sign and 51 by failing to yield while making a left turn. Other prominent causes included drivers losing control of their vehicle (76 crashes) and following too closely (63 crashes).

Major Cause

1
Animal127 (14.7%)
2
FTYROW: From stop sign88 (10.2%)
3
Other (explain in narrative): Other78 (9%)
4
Lost Control76 (8.8%)
5
Followed too close63 (7.3%)
6
FTYROW: Making left turn51 (5.9%)
7
Ran off road - straight34 (3.9%)
8
Ran off road - left31 (3.6%)
9
Driving too fast for conditions31 (3.6%)

Showing top 9 of 47 reported. 38 additional (285 total) not shown: Operating vehicle in an reckless, erratic, careless, negligent manner, Ran Stop Sign, Ran Traffic Signal, Made improper turn, Improper Backing, Other (explain in narrative): No improper action, Driver Distraction: Other interior distraction, FTYROW: From driveway, FTYROW: Other (explain in narrative), Improper or erratic lane changing, Failed to keep in proper lane, Exceeded authorized speed, Driver Distraction: Reaching for object(s)/fallen object(s), Ran off road - right, Swerving/Evasive Action, FTYROW: From parked position, Driver Distraction: Exterior distraction, Other (explain in narrative): Vision obstructed, FTYROW: From yield sign, Driver Distraction: Manual operation of an electronic communication device, FTYROW: To pedestrian, Driver Distraction: Inattentive/lost in thought, Driver Distraction: Adjusting devices (radio, climate), Driver Distraction: Other electronic device activity, Equipment failure, Operator inexperience, Passing: Through/around barrier, Disregarded RR Signal, Crossed centerline (undivided), Failed to yield to emergency vehicle, Driver Distraction: Passenger, Aggressive driving/road rage, Passing: Where prohibited by signs/markings, FTYROW: Making right turn on red signal, FTYROW: At uncontrolled intersection, Passing: On wrong side, Passing: Other passing (explain in narrative), Other (explain in narrative): Improper operation.

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, which occurred in 515 crashes. Collisions with animals were the second most frequent event, recorded in 127 incidents. Run-off-road events were also common, with the first harmful event being a collision with a ditch in 60 crashes and an overturn or rollover in 31 crashes.

First Harmful Event

1
Collision with: Vehicle in traffic515 (54.8%)
2
Collision with: Animal127 (13.5%)
3
Collision with fixed object: Ditch60 (6.4%)
4
Collision with: Parked motor vehicle56 (6%)
5
Non-collision events: Overturn/rollover31 (3.3%)
6
Collision with fixed object: Utility pole/light support17 (1.8%)
7
Collision with: Non-motorist (see non-motorist section - NOT a unit)15 (1.6%)
8
Miscellaneous events: Hit and run13 (1.4%)
9
Collision with fixed object: Guardrail - face12 (1.3%)

Showing top 9 of 33 reported. 24 additional (93 total) not shown: Collision with: Re-entering roadway, Other (explain in narrative), Collision with fixed object: Traffic sign support, Non-collision events: Other non-collision (explain in narrative), Collision with fixed object: Tree, Collision with fixed object: Curb/island/raised median, Collision with fixed object: Mailbox, Collision with: Other non-fixed object (explain in narrative), Collision with fixed object: Embankment, Collision with fixed object: Other fixed object (explain in narrative), Collision with fixed object: Fire hydrant, Collision with fixed object: Fence, Collision with fixed object: Culvert/pipe opening, Non-collision events: Fell/jumped from vehicle, Collision with fixed object: Building, Collision with fixed object: Bridge/bridge rail parapet, Non-collision events: Vehicle went airborne, Collision with fixed object: Ground, Collision with: Struck/struck by object/cargo/person from other vehicle, Collision with fixed object: Other post/pole/support (explain in narrative), Collision with fixed object: Guardrail - end, Collision with fixed object: Wall, Collision with fixed object: Concrete traffic barrier (median or right side), Collision with: Work zone maintenance equipment.

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 460 incidents (48.7% of all crashes). Four-way intersections were the most common crash location among junctions, representing 239 crashes (25.3% of the total). T-intersections accounted for another 52 crashes (5.5%).

Roadway Junction / Feature

1
Non-intersection: Non-junction/no special feature460 (55%)
2
Intersection: Four-way intersection239 (28.6%)
3
Intersection: T-intersection52 (6.2%)
4
Non-intersection: Driveway access (related, not in)34 (4.1%)
5
Intersection: Other intersection (explain in narrative)16 (1.9%)
6
Interchange-related: On-ramp merge area6 (0.7%)
7
Non-intersection: Alley6 (0.7%)
8
Non-intersection: Driveway access (within)5 (0.6%)
9
Intersection: Y-intersection4 (0.5%)

Showing top 9 of 15 reported. 6 additional (15 total) not shown: Non-intersection: Crossover-related, Non-intersection: Railroad grade crossing, Non-intersection: Other non-intersection (explain in narrative), Interchange-related: On-ramp, Interchange-related: Off-ramp, diverge area, Interchange-related: Off-ramp.

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, accounting for 744 of the vehicles recorded. Light trucks and SUVs were also frequently involved, with 289 pickups or panel trucks and 279 sport utility vehicles documented. The data also includes 38 motorcycles and 36 tractor/semi-trailers among the vehicles involved in collisions.

Vehicle Type

"Other" combines 12 smaller categories (55 records): Single-unit truck (>= 3 axles) (12), Moped (8), Cargo/panel van (7), Other bus (seats > 15) (6), Passenger van (seats 9-15) (4), Truck tractor (bobtail) (4), Motor home/recreational vehicle (4), School bus (seats > 15) (3), Maintenance/construction vehicle (3), Other light truck (<=10000 lbs) (2), Farm tractor (1), Truck/trailer (1).

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

Traffic Control Device

In a majority of incidents, no traffic controls were present, a situation recorded for 937 vehicles involved in crashes. Where traffic controls were present, traffic signals were the most common, noted for 276 vehicles. Stop signs were the next most frequent control device, present for 197 vehicles.

Traffic Control Device

"Other" combines 5 smaller categories (13 records): Work zone sign (5), Railway crossing device (4), Traffic director (person) (2), Warning sign (1), School zone signs (1).

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 impact, with 386 vehicles sustaining primary damage to the front. Front-corner impacts were also frequent, with 135 vehicles damaged on the front passenger side corner and 113 on the front driver side corner. Damage to the rear, indicative of rear-end collisions, was noted on 146 vehicles.

Most Damaged Area

"Other" combines 9 smaller categories (362 records): Driver side - front (75), Rear - driver side corner (64), Driver side - rear (58), Passenger side - rear (53), Top (35), Rear - passenger side corner (28), Other (explain in narrative) (24), Non-collision/no damage (14), Undercarriage (11).

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

Impairment (Alcohol / Drugs)

A total of 27 crashes, representing 2.9% of all incidents in Clinton County, involved a driver suspected of being under the influence. Of the drivers specifically flagged for impairment in the dataset, alcohol was suspected in 21 cases and drugs were suspected in 2 cases. These figures represent a minimum count, as impairment may not be detected or recorded in all relevant crashes.

Crashes by City

Crash distribution across municipalities shows that the city of Clinton had the highest volume, with 599 recorded incidents. De Witt followed with 83 crashes, and Camanche had 25 crashes. Other towns such as Wheatland, Grand Mound, and Goose Lake each recorded 3 or fewer crashes during this period.

Crashes by City

1
CLINTON599 (83.1%)
2
DE WITT83 (11.5%)
3
CAMANCHE25 (3.5%)
4
WHEATLAND3 (0.4%)
5
GRAND MOUND2 (0.3%)
6
GOOSE LAKE2 (0.3%)
7
LOW MOOR2 (0.3%)
8
WELTON2 (0.3%)
9
TORONTO1 (0.1%)

Showing top 9 of 11 reported. 2 additional (2 total) not shown: LOST NATION, ANDOVER.

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 occurred on paved roadways, which accounted for 894 incidents. A smaller number of crashes, 41 in total, took place on unpaved gravel or dirt roads. This represents 4.4% of crashes where the 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 contributing to a crash, 'Surface condition' such as wet or icy roads was the most cited factor, noted in 93 incidents. Other less common factors included a slippery or worn surface (6 incidents) and conditions related to a work zone (5 incidents). A specific roadway factor was identified in only a minority of the total crashes.

Roadway Contributing Factor

1
Surface condition (e.g.wet, icy)93 (82.3%)
2
Slippery, loose or worn surface6 (5.3%)
3
Work Zone (roadway-related)5 (4.4%)
4
Debris3 (2.7%)
5
Shoulders (none, low, soft, high)2 (1.8%)
6
Obstruction in roadway2 (1.8%)
7
Ruts/holes/bumps2 (1.8%)

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

Driver Condition

While most drivers were recorded as 'Apparently normal,' the most frequently noted alternative condition was driving under the influence of alcohol, recorded for 35 drivers. Fatigue was another key factor, with 12 drivers identified as asleep or fatigued. Medical issues were also present, including 9 drivers who experienced a seizure or other medical reaction.

Driver Condition

1
Under the influence of alcohol35 (50%)
2
Asleep/fatigued12 (17.1%)
3
Medical condition (seizure, reaction)9 (12.9%)
4
Emotional (e.g. depressed, angry)7 (10%)
5
Under the influence of drugs/meds4 (5.7%)
6
Illness/fainted2 (2.9%)
7
Walks with a cane/crutches1 (1.4%)

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

Property Damage

Officer-estimated property damage costs were most frequently in the $1,500 to $7,500 range, which applied to 691 crashes (73.1% of the total). A significant number of crashes, 213 incidents (22.5%), resulted in more substantial damage estimated between $7,500 and $25,000. Only a small fraction of crashes, 8 incidents (0.8%), resulted in damage 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

The most frequent type of crash was a non-collision event involving a single vehicle, such as running off the road or overturning, which accounted for 294 incidents (31.1%). Among multi-vehicle crashes, rear-end collisions were the most common, with 182 incidents (19.3%). Broadside, or front-to-side, collisions were also prevalent, making up 159 crashes (16.8%).

Manner of Collision

"Other" combines 3 smaller categories (35 records): Head-on (front to front) (19), Sideswipe, opposite direction (14), Rear to rear (2).

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

Pre-Crash Driver Action

The most common pre-crash action for vehicles was moving essentially straight, which was the case for 882 vehicles. Turning left was the second most frequent maneuver, recorded for 175 vehicles prior to impact. A notable number of vehicles, 116, were legally parked when they were struck, while another 64 were stopped in traffic.

Pre-Crash Driver Action

1
Movement essentially straight882 (59.6%)
2
Turning left175 (11.8%)
3
Legally Parked116 (7.8%)
4
Stopped in traffic64 (4.3%)
5
Turning right55 (3.7%)
6
Slowing/stopping (deceleration)45 (3%)
7
Backing41 (2.8%)
8
Negotiating a curve20 (1.4%)
9
Changing lanes17 (1.1%)

Showing top 9 of 19 reported. 10 additional (65 total) not shown: Other (explain in narrative), Entering traffic lane (merging), Accelerating in road, Overtaking/passing, Making U-turn, Starting in road, Illegally Parked/Unattended, Entering a parked position, Leaving traffic lane, Leaving a parked position.

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

Pedestrian/Cyclist Action

Among incidents involving pedestrians or cyclists, the most frequently recorded action was 'Entering or crossing roadway,' noted in 9 cases. Walking or riding along the roadway was the next most common circumstance, with 5 individuals moving with traffic and 2 moving against traffic.

Pedestrian/Cyclist Action

1
Entering or crossing roadway9 (52.9%)
2
Movement: Along roadway with traffic5 (29.4%)
3
Movement: Along roadway against traffic2 (11.8%)
4
Approaching or leaving vehicle1 (5.9%)

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

Person Type

Of the 2,012 individuals involved in crashes, the vast majority, 1,900 people (94.4%), were drivers of the vehicles. Passengers constituted a smaller group, with 94 individuals involved. The remaining involved persons were vulnerable road users, including 11 bicyclists and 7 pedestrians.

Person Type

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

Person Injury Severity

Across all 2,012 people involved in crashes, 3 individuals sustained fatal injuries and 26 had serious injuries. A larger number of people sustained less severe harm, including 122 with minor injuries and 246 with possible injuries. These numbers contribute to the 394 total injuries reported.

Person Injury Severity

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

Occupant Safety Equipment

Among participants for whom safety equipment use was documented, 251 were recorded as using a shoulder and lap belt. A total of 24 individuals were noted as not using any safety restraints at the time of their crash. For riders of motorcycles or similar vehicles, 13 were using a DOT-compliant helmet.

Occupant Safety Equipment

"Other" combines 1 smaller categories (1 records): Booster seat (1).

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 most common type of collision involved two vehicles, accounting for 582 crashes (61.6% of the total). Single-vehicle crashes were the next most frequent, with 330 incidents, representing 34.9% of all crashes. Crashes involving three or more vehicles were less common, with 33 such incidents 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: 945
  • Total persons involved: 2,012
  • Total vehicles involved: 1,596

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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