Yearly Traffic Safety Analysis

526 CRASHES IN
IOWA, IA
2015

In 2015, Muscatine County recorded 526 total traffic crashes, resulting in 1 fatality and 250 injuries. The single most common contributing factor cited in these incidents was the presence of an animal on the roadway, which was noted in 99 crashes, accounting for 18.8% of the total. Single-vehicle, non-collision events were the most frequent manner of collision, representing 38% of all crashes.

526

Total Crash Events

1

Persons Killed

250

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Aggregate counts from crash, person, and vehicle records

Vulnerable Road User Casualties

Motorists comprised the vast majority of casualties, with 1 motorist killed and 243 injured in 2015. There were no pedestrian or cyclist fatalities recorded during this period. However, 3 pedestrians and 4 cyclists sustained injuries in traffic crashes.

0

Pedestrians Killed

0

Cyclists Killed

1

Motorists Killed

3

Pedestrians Injured

4

Cyclists Injured

243

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 Muscatine County peaked on Mondays, which saw 93 incidents throughout the year. The single busiest hour for crashes was the 3 p.m. hour, with 43 recorded events, indicating a significant concentration during the afternoon commute period. While over half of the crashes occurred during daylight (296 incidents), a substantial number, 136, took place in dark conditions.

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, 340 out of 526 (64.6%), resulted in no injuries and were property-damage-only events. The remaining 186 crashes involved at least one injury or fatality, with 23 classified as serious injury crashes and 1 as a fatal crash. This single fatal crash resulted in one death, though in some instances, a single crash can involve multiple fatalities.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.2%
Serious Injury23serious injury crashes4.4%
Minor Injury71minor injury crashes13.5%
Possible Injury91possible injury crashes17.3%
No Injury340no injury crashes64.6%

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 frequently cited contributing factor in 2015 crashes was an animal in the roadway, accounting for 99 incidents (18.8%). Other leading factors included failure to yield the right-of-way while making a left turn and driving too fast for conditions, each cited in 38 crashes (7.2%). Losing control of the vehicle was also a significant factor, noted in 37 crashes (7.0%).

Officer-Reported Primary Contributing Cause

Animal99 (18.8%)
FTYROW: Making left turn38 (7.2%)
Driving too fast for conditions38 (7.2%)
Lost Control37 (7%)
FTYROW: From stop sign32 (6.1%)
Followed too close32 (6.1%)
Other (explain in narrative): Other25 (4.8%)
Ran Stop Sign24 (4.6%)
Ran off road - straight20 (3.8%)
Ran off road - left18 (3.4%)

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

Road & Environmental Conditions

A majority of crashes occurred in favorable conditions, with 56.3% (296 crashes) happening in daylight and 59.5% (313 crashes) on dry road surfaces. Clear weather was reported for 279 crashes (53.0%). Adverse conditions still played a role, with 63 crashes occurring on wet roads, 30 in rain, and 29 during snowfall.

Weather

Clear279 (61.7%)
Cloudy100 (22.1%)
Rain30 (6.6%)
Snow29 (6.4%)
Severe Winds4 (0.9%)
Freezing rain/drizzle4 (0.9%)
Blowing Snow3 (0.7%)
Fog, smoke, smog3 (0.7%)

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

Lighting

Daylight296 (65.2%)
Dark - roadway not lighted85 (18.7%)
Dark - roadway lighted49 (10.8%)
Dusk12 (2.6%)
Dawn10 (2.2%)
Dark - unknown roadway lighting2 (0.4%)

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

Road Surface

Dry313 (69.1%)
Wet63 (13.9%)
Snow35 (7.7%)
Ice/frost21 (4.6%)
Gravel16 (3.5%)
Slush3 (0.7%)
Other (explain in narrative)2 (0.4%)

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

Vehicles & Demographics

Among all individuals involved in crashes, the 16-20 age group was the most represented, with 173 people, followed by the 26-34 age group with 164 people. Analysis of the 851 vehicles involved shows that Ford was the most frequent make, appearing in 172 instances. Chevrolet vehicles (combining 'CHEV' and 'CHEVROLET' records) were involved in 166 crashes, and Toyota vehicles ('TOYT' and 'TOYOTA') were involved in 72.

Top Vehicle Makes (851 vehicles)

1
FORD172 (20.2%)
2
CHEV106 (12.5%)
3
CHEVROLET60 (7.1%)
4
TOYT44 (5.2%)
5
DODG33 (3.9%)
6
JEEP32 (3.8%)
7
GMC28 (3.3%)
8
TOYOTA28 (3.3%)
9
DODGE23 (2.7%)
10
HOND22 (2.6%)

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

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

Sex Distribution (773 persons with recorded sex)

Male456 (59.0%)
Female317 (41.0%)

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

Major Cause

The leading major cause attributed to crashes was the presence of an animal, cited in 99 incidents. Tied for the second most common cause were failure to yield while making a left turn and driving too fast for conditions, each contributing to 38 crashes. Losing control of the vehicle was the fourth leading cause, documented in 37 crashes.

Major Cause

1
Animal99 (19.6%)
2
FTYROW: Making left turn38 (7.5%)
3
Driving too fast for conditions38 (7.5%)
4
Lost Control37 (7.3%)
5
FTYROW: From stop sign32 (6.3%)
6
Followed too close32 (6.3%)
7
Other (explain in narrative): Other25 (4.9%)
8
Ran Stop Sign24 (4.7%)
9
Ran off road - straight20 (4%)

Showing top 9 of 46 reported. 37 additional (161 total) not shown: Ran off road - left, Operating vehicle in an reckless, erratic, careless, negligent manner, Driver Distraction: Inattentive/lost in thought, Driver Distraction: Other interior distraction, Swerving/Evasive Action, Ran Traffic Signal, Other (explain in narrative): No improper action, Exceeded authorized speed, FTYROW: From driveway, FTYROW: Other (explain in narrative), Made improper turn, Driver Distraction: Exterior distraction, Driver Distraction: Passenger, Improper Backing, Other (explain in narrative): Vision obstructed, Ran off road - right, FTYROW: From parked position, Crossed centerline (undivided), Driver Distraction: Other electronic device activity, FTYROW: At uncontrolled intersection, Improper or erratic lane changing, Failed to keep in proper lane, Equipment failure, Driver Distraction: Reaching for object(s)/fallen object(s), Passing: Other passing (explain in narrative), Aggressive driving/road rage, Driver Distraction: Adjusting devices (radio, climate), FTYROW: From yield sign, FTYROW: Making right turn on red signal, Failure to signal intentions, Failed to yield to emergency vehicle, Traveling wrong way or on wrong side of road, Illegally Parked/Unattended, Driver Distraction: Talking on a hand-held device, Driver Distraction: Manual operation of an electronic communication device, Cargo/equipment loss or shift, Over correcting/over steering.

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 267 crashes. Collisions with animals were the second most frequent event, recorded 95 times. Single-vehicle events were also prominent, including 34 overturns or rollovers and numerous collisions with fixed objects like ditches (34), trees (11), and utility poles (10).

First Harmful Event

1
Collision with: Vehicle in traffic267 (51.1%)
2
Collision with: Animal95 (18.2%)
3
Collision with fixed object: Ditch34 (6.5%)
4
Non-collision events: Overturn/rollover34 (6.5%)
5
Collision with: Parked motor vehicle18 (3.4%)
6
Collision with fixed object: Tree11 (2.1%)
7
Collision with fixed object: Utility pole/light support10 (1.9%)
8
Miscellaneous events: Hit and run6 (1.1%)
9
Non-collision events: Other non-collision (explain in narrative)5 (1%)

Showing top 9 of 31 reported. 22 additional (43 total) not shown: Collision with fixed object: Fence, Collision with fixed object: Traffic sign support, Collision with: Non-motorist (see non-motorist section - NOT a unit), Other (explain in narrative), Collision with: Other non-fixed object (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, Collision with fixed object: Other post/pole/support (explain in narrative), Collision with fixed object: Other fixed object (explain in narrative), Miscellaneous events: Eluding law enforcement, Miscellaneous events: Immersion, Collision with fixed object: Mailbox, Non-collision events: Vehicle went airborne, Collision with fixed object: Snow bank, Collision with fixed object: Guardrail - face, Collision with fixed object: Other traffic barrier (explain in narrative), Collision with fixed object: Embankment, Collision with: Re-entering roadway, Collision with: Thrown or falling object, Collision with fixed object: Building.

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

Roadway Junction / Feature

Crashes were more likely to occur on non-intersection roadway segments, which accounted for 222 incidents. Intersections were also common crash locations, with 120 crashes at four-way intersections and 60 at T-intersections. In total, 193 crashes, or 36.7% of all incidents, happened at an intersection of some type.

Roadway Junction / Feature

1
Non-intersection: Non-junction/no special feature222 (48.8%)
2
Intersection: Four-way intersection120 (26.4%)
3
Intersection: T-intersection60 (13.2%)
4
Non-intersection: Driveway access (related, not in)14 (3.1%)
5
Intersection: Other intersection (explain in narrative)7 (1.5%)
6
Non-intersection: Crossover-related7 (1.5%)
7
Non-intersection: Driveway access (within)7 (1.5%)
8
Non-intersection: Alley5 (1.1%)
9
Non-intersection: Other non-intersection (explain in narrative)4 (0.9%)

Showing top 9 of 15 reported. 6 additional (9 total) not shown: Intersection: Y-intersection, Non-intersection: Bike lanes, Intersection: L-intersection, Interchange-related: Other interchange (explain in narrative), Intersection: Roundabout, Interchange-related: On-ramp merge 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 common vehicle type involved in crashes, accounting for 393 of the 851 vehicles. Light trucks and sport utility vehicles were also frequently involved, with 159 and 158 vehicles respectively. Commercial vehicles had a smaller but notable presence, with 25 tractor-trailers involved in incidents, as were motorcycles, with 14 involved.

Vehicle Type

"Other" combines 13 smaller categories (31 records): Single unit truck (2-axle, 6-tire) (10), Cargo/panel van (5), Other (explain in narrative) (3), School bus (seats > 15) (3), Maintenance/construction vehicle (2), Motor home/recreational vehicle (1), Passenger van (seats 9-15) (1), Farm equipment (explain in narrative) (1), Limousine/taxi (sets >15) (1), Golf cart (1), Farm tractor (1), All-terrain vehicle (ATV) (1), Other heavy truck (> 10000 lbs) (cannot classify) (1).

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

Traffic Control Device

The vast majority of crashes occurred in areas where no traffic controls were present, with 484 such instances recorded. For crashes at controlled locations, traffic signals were the most common device, present in 160 cases. Stop signs were the next most frequent form of traffic control, noted in 104 instances.

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 vehicle damage, with the primary point of impact being the front in 246 instances. This suggests a high prevalence of head-on or front-to-object collisions. Rear impacts, indicative of rear-end collisions, were the second most common, noted in 87 cases, while side impacts were recorded 82 times between the driver and passenger sides.

Most Damaged Area

"Other" combines 10 smaller categories (177 records): Driver side - rear (32), Top (30), Passenger side - rear (29), Passenger side - front (28), Rear - driver side corner (20), Rear - passenger side corner (15), Other (explain in narrative) (11), Non-collision/no damage (6), Undercarriage (5), Cargo loss (1).

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

Impairment (Alcohol / Drugs)

Driver impairment was suspected in 25 crashes, representing 4.75% of all incidents in 2015. Alcohol was the primary substance involved, cited in 23 of these cases, while drugs were suspected in the remaining 2. These figures represent a minimum, as impairment is often under-reported in crash data.

Crashes by City

Within Muscatine County, the city of Muscatine had the highest number of crashes, with 255 incidents. Far fewer crashes were recorded in other municipalities, with Wilton and West Liberty each reporting 12 crashes. Most other towns in the county, such as Atalissa, Conesville, and Fruitland, each recorded only 2 crashes.

Crashes by City

1
MUSCATINE255 (88.9%)
2
WILTON12 (4.2%)
3
WEST LIBERTY12 (4.2%)
4
ATALISSA2 (0.7%)
5
CONESVILLE2 (0.7%)
6
FRUITLAND2 (0.7%)
7
WALCOTT1 (0.3%)
8
STOCKTON1 (0.3%)

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, 493 out of 525 with known road surface type, occurred on paved roadways. Crashes on unpaved surfaces like gravel or dirt roads were less common, accounting for 32 incidents, or 6.1% of the total. This highlights that while Iowa has an extensive unpaved road network, most crashes are concentrated on paved routes.

Paved vs Unpaved Road

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

Roadway Contributing Factor

Among roadway-related contributing factors, adverse surface conditions such as wet or icy pavement were the most frequently cited, contributing to 59 crashes. Other roadway factors were noted far less often, with a slippery or worn surface cited in 5 cases and work zones contributing to just 2 crashes. For the majority of incidents, no roadway factor was identified as a contributor.

Roadway Contributing Factor

1
Surface condition (e.g.wet, icy)59 (84.3%)
2
Slippery, loose or worn surface5 (7.1%)
3
Work Zone (roadway-related)2 (2.9%)
4
Ruts/holes/bumps1 (1.4%)
5
Obstruction in roadway1 (1.4%)
6
Traffic backup, prior non-recurring incident1 (1.4%)
7
Traffic backup, regular congestion1 (1.4%)

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,' a driver condition was noted in a subset of crashes. The most common non-normal condition was being under the influence of alcohol, recorded for 25 drivers. An emotional state, such as being depressed or angry, was noted for 13 drivers, while 3 drivers were identified as being asleep or fatigued.

Driver Condition

1
Under the influence of alcohol25 (48.1%)
2
Emotional (e.g. depressed, angry)13 (25%)
3
Medical condition (seizure, reaction)4 (7.7%)
4
Under the influence of drugs/meds3 (5.8%)
5
Asleep/fatigued3 (5.8%)
6
Visually impaired1 (1.9%)
7
Hearing impaired/deaf1 (1.9%)
8
Paraplegic/wheelchair restricted1 (1.9%)
9
Physical impairment1 (1.9%)

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

Property Damage

The most common estimated property damage cost fell within the $1,500 to $7,500 range, which applied to 365 crashes. A smaller but significant number of crashes, 131, resulted in damages between $7,500 and $25,000. Only 15 crashes were estimated to have damages 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

Single-vehicle, non-collision events, such as running off the road or overturning, were the most frequent crash type, accounting for 200 incidents (38.0%). The most common type of multi-vehicle crash was a rear-end collision, which occurred 100 times (19.0%). Broadside, or T-bone, collisions were the third most common manner, with 80 crashes (15.2%).

Manner of Collision

"Other" combines 3 smaller categories (14 records): Sideswipe, opposite direction (9), Rear to side (3), 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 recorded in 485 instances. Turning left was the second most frequent action, noted 111 times, often preceding angle or head-on collisions at intersections. Turning right and being legally parked were tied for the next most common pre-crash maneuvers, each recorded 35 times.

Pre-Crash Driver Action

1
Movement essentially straight485 (61.4%)
2
Turning left111 (14.1%)
3
Legally Parked35 (4.4%)
4
Turning right35 (4.4%)
5
Stopped in traffic31 (3.9%)
6
Slowing/stopping (deceleration)21 (2.7%)
7
Other (explain in narrative)14 (1.8%)
8
Overtaking/passing12 (1.5%)
9
Negotiating a curve12 (1.5%)

Showing top 9 of 18 reported. 9 additional (34 total) not shown: Backing, Changing lanes, Entering traffic lane (merging), Leaving traffic lane, Leaving a parked position, Accelerating in road, Illegally Parked/Unattended, Entering a parked position, Making U-turn.

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

Person Type

Of the 1,107 people involved in crashes, the vast majority, 1,038, were drivers. Passengers constituted the next largest group with 62 individuals. A small number of vulnerable road users were involved, including 4 bicyclists and 3 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 1,107 total people involved in crashes, 250 sustained an injury and 1 person was killed. Among the injured, 27 suffered serious injuries, 88 had minor injuries, and 135 had possible injuries. The data shows that the majority of individuals involved in crashes were not injured.

Person Injury Severity

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

Occupant Safety Equipment

Based on available data, 175 vehicle occupants were reported to be using a shoulder and lap belt at the time of the crash. Conversely, 23 individuals were explicitly noted as using no safety equipment. An additional 5 occupants were secured in child safety seats.

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

Two-vehicle collisions were the most common crash configuration, accounting for 267 of the 526 total incidents. Single-vehicle crashes were also very common, with 231 incidents, representing 43.9% of all crashes. Crashes involving three or more vehicles were infrequent, with only 28 such events 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: 526
  • Total persons involved: 1,107
  • Total vehicles involved: 851

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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