Yearly Traffic Safety Analysis

4,090 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Scott County recorded 4,090 total crashes, a slight increase of 0.22% from the 4,081 crashes documented in 2016. While the overall crash volume remained stable, the most significant change was a substantial decrease in traffic fatalities, which fell from 19 in 2016 to 8 in 2017. This reduction in fatalities occurred alongside a minor decrease in total injuries, from 1,563 to 1,530.

4,090

▲ 0.2%was 4,081

Total Crash Events

8

▼ -57.9%was 19

Persons Killed

1,530

▼ -2.1%was 1,563

Persons Injured

8

▼ -50.0%was 16

Fatal Crash Events

Note: "Persons Killed" (8) counts individual fatalities across all crash events. "Fatal" in the severity table below (8) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crash volume in Scott County remained stable year-over-year, with a minor increase of just 9 incidents from 4,081 in 2016 to 4,090 in 2017. However, the severity of these crashes lessened, as total injuries decreased from 1,563 to 1,530. Most notably, total fatalities saw a significant drop of 57.9%, from 19 deaths in 2016 to 8 in 2017.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 3↓-66.7%

0

Cyclists Killed

Prior: 0→0.0%

7

Motorists Killed

Prior: 16↓-56.3%

0

Other Killed

Prior: 0→0.0%

31

Pedestrians Injured

Prior: 47↓-34.0%

17

Cyclists Injured

Prior: 26↓-34.6%

1,481

Motorists Injured

Prior: 1,486↓-0.3%

1

Other Injured

Prior: 4↓-75.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed some shifts between the two years. While Friday remained the peak day for crashes in both 2016 (684 crashes) and 2017 (711 crashes), the peak hour shifted. In 2016, the 3 p.m. hour saw the most incidents with 399 crashes, whereas in 2017, the peak moved later to the 5 p.m. hour, which recorded 375 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity outcomes improved in 2017 compared to the prior year. The number of fatal crashes was halved, dropping from 16 in 2016 to 8 in 2017, which reduced the fatal crash share of all crashes from 0.4% to 0.2%. The share of no-injury crashes decreased slightly from 68.4% to 67.6%. Conversely, there was a slight increase in the proportion of serious injury crashes (from 1.2% to 1.4%) and possible injury crashes (from 21.8% to 22.7%).

Outcome by Severity (Crash Events)

Fatal8fatal crashes0.2%
↓-50.0%prior 16
Serious Injury59serious injury crashes1.4%
↑20.4%prior 49
Minor Injury331minor injury crashes8.1%
↓-1.2%prior 335
Possible Injury927possible injury crashes22.7%
↑4.4%prior 888
No Injury2,765no injury crashes67.6%
↓-1.0%prior 2,793

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes in Scott County were consistent across both periods. 'Followed too close' remained the top factor, with its count increasing from 686 incidents in 2016 to 729 in 2017. The second and third most common factors, 'Ran off road - left' and 'FTYROW: Making left turn,' saw their counts decrease from 437 to 394 and from 267 to 234, respectively. Crashes attributed to 'Driving too fast for conditions' also decreased, falling from 155 in 2016 to 129 in 2017.

Officer-Reported Primary Contributing Cause

Followed too close729 (17.8%)↑6.3%prior 686
Ran off road - left394 (9.6%)↓-9.8%prior 437
FTYROW: Making left turn234 (5.7%)↓-12.4%prior 267
Ran Traffic Signal215 (5.3%)↓-2.3%prior 220
Other (explain in narrative): Other206 (5%)↑5.1%prior 196
FTYROW: From stop sign206 (5%)↓-1.0%prior 208
Animal200 (4.9%)↑0.5%prior 199
Operating vehicle in an reckless, erratic, careless, negligent manner174 (4.3%)↑18.4%prior 147
Lost Control159 (3.9%)↑25.2%prior 127
Improper or erratic lane changing140 (3.4%)↑13.8%prior 123

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Environmental conditions at the time of crashes remained largely consistent between 2016 and 2017. In both years, the majority of crashes occurred in clear weather (61.1% in 2017 vs. 59.7% in 2016) and during daylight hours (67.0% in 2017 vs. 68.4% in 2016). Crashes on dry road surfaces were also predominant, accounting for 77.8% of incidents in 2017 compared to 76.1% in 2016. A slight decrease was observed in the share of crashes occurring on snowy roads, from 3.5% in 2016 to 2.4% in 2017.

Weather

Clear2,498 (63.8%)
↑2.5%prior 2,436
Cloudy992 (25.3%)
↓-4.6%prior 1,040
Rain262 (6.7%)
↑12.4%prior 233
Snow99 (2.5%)
↓-23.3%prior 129
Freezing rain/drizzle37 (0.9%)
↑85.0%prior 20
Fog, smoke, smog21 (0.5%)
↑40.0%prior 15
Severe Winds3 (0.1%)
↓-50.0%prior 6
Sleet, hail1 (0.0%)
Blowing Snow1 (0.0%)
↓-93.3%prior 15

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight2,741 (70.0%)
↓-1.8%prior 2,792
Dark - roadway lighted845 (21.6%)
↑7.5%prior 786
Dark - roadway not lighted198 (5.1%)
↓-2.0%prior 202
Dusk76 (1.9%)
↓-9.5%prior 84
Dawn43 (1.1%)
↑22.9%prior 35
Dark - unknown roadway lighting13 (0.3%)
↑116.7%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry3,180 (81.2%)
↑2.4%prior 3,104
Wet547 (14.0%)
↑2.1%prior 536
Snow100 (2.6%)
↓-30.6%prior 144
Ice/frost58 (1.5%)
↓-17.1%prior 70
Gravel15 (0.4%)
↓-31.8%prior 22
Slush11 (0.3%)
↓-45.0%prior 20
Sand2 (0.1%)
Other (explain in narrative)1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The demographics of vehicles and persons involved in crashes showed high stability between 2016 and 2017. Ford, Chevrolet, and Toyota remained the top three vehicle makes involved in crashes, with nearly identical counts year-over-year; for example, 1,409 Ford vehicles were involved in 2017 compared to 1,407 in 2016. Similarly, the age distribution of persons involved in crashes was consistent, with the 26-34 age group representing the largest cohort in both years, accounting for 1,350 persons in 2017 and 1,414 in 2016.

Top Vehicle Makes (7,709 vehicles)

1
FORD1,409 (18.3%)
↑0.1%prior 1,407
2
CHEV687 (8.9%)
↑21.8%prior 564
3
CHEVROLET601 (7.8%)
↓-21.6%prior 767
4
TOYT361 (4.7%)
↑2.8%prior 351
5
HOND325 (4.2%)
↑16.5%prior 279
6
TOYOTA273 (3.5%)
↓-11.7%prior 309
7
NR272 (3.5%)
↑14.3%prior 238
8
JEEP230 (3%)
↑15.6%prior 199
9
GMC222 (2.9%)
↑11.0%prior 200
10
DODG221 (2.9%)
↑15.1%prior 192

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

1,485 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (5,666 persons with recorded sex)

Male3,002 (53.0%)
↓-0.3%prior 3,012
Female2,664 (47.0%)
↑1.6%prior 2,622

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 4,090
  • Total persons involved: 9,000
  • Total vehicles involved: 7,709

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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