Yearly Traffic Safety Analysis

4,070 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Scott County recorded 4,070 total crashes, a 3.3% increase from the 3,940 crashes reported in 2018. Despite the rise in overall collisions, the number of resulting fatalities decreased from 13 to 9 year-over-year. The most notable shift was a significant increase in crashes occurring in the city of Bettendorf.

4,070

▲ 3.3%was 3,940

Total Crash Events

9

▼ -30.8%was 13

Persons Killed

1,291

▼ -3.6%was 1,339

Persons Injured

9

▼ -25.0%was 12

Fatal Crash Events

Note: "Persons Killed" (9) counts individual fatalities across all crash events. "Fatal" in the severity table below (9) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, total crashes in Scott County increased by 3.3% from 2018 to 2019, rising from 3,940 to 4,070. However, the outcomes of these crashes became less severe on average, with total injuries declining by 3.6% from 1,339 to 1,291. The number of fatalities also fell from 13 in 2018 to 9 in 2019.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 2↓-50.0%

0

Cyclists Killed

Prior: 1↓-100.0%

8

Motorists Killed

Prior: 10↓-20.0%

0

Other Killed

Prior: 0→0.0%

28

Pedestrians Injured

Prior: 39↓-28.2%

22

Cyclists Injured

Prior: 23↓-4.3%

1,240

Motorists Injured

Prior: 1,277↓-2.9%

1

Other Injured

Prior: 0→%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained largely consistent year-over-year, with Friday being the peak day and 4 p.m. the peak hour in both periods. Crashes on Thursdays, however, increased from 535 in 2018 to 647 in 2019. The morning commute saw a rise in incidents, with the 7 a.m. hour increasing from 210 to 243 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes decreased from 2018 to 2019, with the number of fatal crashes falling from 12 to 9. The proportion of crashes resulting in possible injuries also dropped from 21.4% to 19.2% of all incidents. While the count of serious injury crashes increased slightly from 51 to 60, the total number of crashes involving any type of injury declined from 1,214 to 1,153.

Outcome by Severity (Crash Events)

Fatal9fatal crashes0.2%
↓-25.0%prior 12
Serious Injury60serious injury crashes1.5%
↑17.6%prior 51
Minor Injury310minor injury crashes7.6%
↓-2.5%prior 318
Possible Injury783possible injury crashes19.2%
↓-7.3%prior 845
No Injury2,908no injury crashes71.4%
↑7.1%prior 2,714

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

Following too closely was the leading contributing factor in both years, though its count decreased from 637 crashes in 2018 to 612 in 2019. The second-ranked factor, running off the road to the left, saw its count increase by 16.3% from 410 to 477 incidents. Failure to yield while making a left turn became more prominent, with its count rising by 29.1% from 203 to 262 crashes, moving it into the top three factors for 2019.

Officer-Reported Primary Contributing Cause

Followed too close612 (15%)↓-3.9%prior 637
Ran off road - left477 (11.7%)↑16.3%prior 410
FTYROW: Making left turn262 (6.4%)↑29.1%prior 203
Driving too fast for conditions229 (5.6%)→0.0%prior 229
Ran Traffic Signal219 (5.4%)↑10.6%prior 198
Other (explain in narrative): Other193 (4.7%)↓-5.4%prior 204
Animal190 (4.7%)↑3.3%prior 184
FTYROW: From stop sign173 (4.3%)↓-10.4%prior 193
Made improper turn154 (3.8%)↑79.1%prior 86
Improper or erratic lane changing145 (3.6%)↑9.0%prior 133

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

In both 2018 and 2019, most crashes occurred in clear weather during daylight hours on dry roads. There was a notable increase in crashes on snowy roads, which rose from 193 to 256, and in dark, unlighted conditions, which increased from 196 to 249 incidents. Conversely, crashes attributed to freezing rain decreased from 100 in 2018 to 70 in 2019.

Weather

Clear2,500 (63.9%)
↑1.5%prior 2,463
Cloudy849 (21.7%)
↑9.8%prior 773
Rain228 (5.8%)
↓-2.1%prior 233
Snow203 (5.2%)
↑29.3%prior 157
Freezing rain/drizzle70 (1.8%)
↓-30.0%prior 100
Blowing Snow35 (0.9%)
↑105.9%prior 17
Severe Winds12 (0.3%)
Fog, smoke, smog10 (0.3%)
↓-56.5%prior 23
Sleet, hail5 (0.1%)
→0.0%prior 5
Other (explain in narrative)1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight2,783 (71.1%)
↑3.2%prior 2,698
Dark - roadway lighted770 (19.7%)
↑1.7%prior 757
Dark - roadway not lighted249 (6.4%)
↑27.0%prior 196
Dusk71 (1.8%)
↓-5.3%prior 75
Dawn38 (1.0%)
↓-19.1%prior 47
Dark - unknown roadway lighting5 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry2,851 (72.8%)
↑2.7%prior 2,777
Wet571 (14.6%)
↓-3.7%prior 593
Snow256 (6.5%)
↑32.6%prior 193
Ice/frost189 (4.8%)
↑16.7%prior 162
Slush40 (1.0%)
↑8.1%prior 37
Gravel5 (0.1%)
↓-37.5%prior 8
Other (explain in narrative)3 (0.1%)
↓-40.0%prior 5
Mud, dirt2 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

The ranking of the top vehicle makes involved in crashes remained consistent, led by Ford, Chevrolet, and Toyota. An analysis of persons involved in crashes shows increased counts across most age demographics, corresponding with the overall rise in collisions. The number of individuals aged 16-20 involved in crashes grew from 944 to 1,069, and the 65+ age group increased from 807 to 965.

Top Vehicle Makes (7,638 vehicles)

1
FORD1,319 (17.3%)
↓-6.3%prior 1,407
2
CHEV720 (9.4%)
↑1.0%prior 713
3
CHEVROLET581 (7.6%)
↑2.5%prior 567
4
TOYT365 (4.8%)
↑4.0%prior 351
5
NR357 (4.7%)
↑17.8%prior 303
6
HOND343 (4.5%)
↑20.8%prior 284
7
GMC270 (3.5%)
↑20.5%prior 224
8
TOYOTA252 (3.3%)
↓-6.3%prior 269
9
HONDA231 (3%)
↑11.1%prior 208
10
JEEP217 (2.8%)
↓-10.0%prior 241

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

1,868 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (6,534 persons with recorded sex)

Male3,509 (53.7%)
↑16.4%prior 3,015
Female3,025 (46.3%)
↑15.3%prior 2,624

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Person-level records linked to crash events

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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 4,070
  • Total persons involved: 9,989
  • Total vehicles involved: 7,638

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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