Yearly Traffic Safety Analysis

261 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Dickinson County, there were 261 total crashes in 2017, a 2.4% increase from the 255 crashes recorded in 2016. While total crashes and injuries (115 in 2017 vs. 109 in 2016) saw a slight rise, the number of fatalities decreased from two in 2016 to one in 2017. The most significant shift in contributing factors was a 45.9% increase in crashes attributed to 'Followed too close,' which rose from 37 incidents in 2016 to 54 in 2017.

261

▲ 2.4%was 255

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

115

▲ 5.5%was 109

Persons Injured

1

▼ -50.0%was 2

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crash trends in Dickinson County showed a slight increase in 2017 compared to the previous year. Total collisions rose by 2.4%, from 255 to 261. While the number of fatal crashes decreased from two to one, the total number of people injured increased from 109 to 115.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 2↓-50.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

3

Cyclists Injured

Prior: 0→%

111

Motorists Injured

Prior: 107↑3.7%

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 periods. Friday remained the peak day for crashes in both 2017 (49 crashes) and 2016 (60 crashes), though the total count on that day decreased. The peak hour for crashes shifted from 12 p.m. in 2016 (24 crashes) to the evening commute in 2017, which saw a dual peak at 4 p.m. and 5 p.m. (27 crashes each).

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

The severity of crashes shifted year-over-year. The number of fatal crashes was halved, decreasing from two in 2016 to one in 2017, with the fatal crash rate dropping from 0.8% to 0.4%. Conversely, the proportion of crashes resulting in any level of injury increased from 29.8% in 2016 (76 crashes) to 33.3% in 2017 (87 crashes). This was driven by a rise in minor and possible injury crashes, which collectively grew from 66 to 81 incidents, even as serious injury crashes declined from 10 to 6.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
↓-50.0%prior 2
Serious Injury6serious injury crashes2.3%
↓-40.0%prior 10
Minor Injury32minor injury crashes12.3%
↑23.1%prior 26
Possible Injury49possible injury crashes18.8%
↑22.5%prior 40
No Injury173no injury crashes66.3%
↓-2.3%prior 177

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 showed notable changes year-over-year. 'Followed too close' remained the primary factor in both periods but saw its count increase by 45.9%, from 37 crashes in 2016 to 54 in 2017. Conversely, crashes attributed to 'Driving too fast for conditions' decreased by 61.9%, falling from 21 incidents in 2016 to just 8 in 2017. 'Driver Distraction: Other interior distraction' also saw a significant increase in count, rising from 5 crashes in 2016 to 15 in 2017.

Officer-Reported Primary Contributing Cause

Followed too close54 (20.7%)↑45.9%prior 37
Animal23 (8.8%)→0.0%prior 23
Driver Distraction: Other interior distraction15 (5.7%)↑200.0%prior 5
Other (explain in narrative): Other14 (5.4%)→0.0%prior 14
FTYROW: From stop sign14 (5.4%)↓-30.0%prior 20
FTYROW: Making left turn12 (4.6%)↓-14.3%prior 14
Ran Traffic Signal10 (3.8%)↑100.0%prior 5
Lost Control10 (3.8%)↑11.1%prior 9
Ran off road - left9 (3.4%)
Ran Stop Sign9 (3.4%)

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

Road & Environmental Conditions

A comparison of crash conditions reveals a shift toward incidents on dry roads and away from those on adverse surfaces. Crashes on dry roads increased from 155 in 2016 to 189 in 2017. Correspondingly, crashes on snow-covered roads decreased from 27 to 18, and collisions on icy or frosty roads fell from 16 to 11. The proportion of crashes occurring in daylight versus darkness remained relatively stable across both years.

Weather

Clear161 (66.3%)
↑3.2%prior 156
Cloudy57 (23.5%)
↑50.0%prior 38
Snow10 (4.1%)
↓-37.5%prior 16
Rain9 (3.7%)
↓-10.0%prior 10
Fog, smoke, smog2 (0.8%)
Freezing rain/drizzle2 (0.8%)
Severe Winds1 (0.4%)
Blowing Snow1 (0.4%)
↓-80.0%prior 5

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

Lighting

Daylight187 (77.0%)
↑8.1%prior 173
Dark - roadway lighted36 (14.8%)
↑20.0%prior 30
Dark - roadway not lighted13 (5.3%)
↓-27.8%prior 18
Dusk5 (2.1%)
Dark - unknown roadway lighting1 (0.4%)
Dawn1 (0.4%)
↓-88.9%prior 9

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

Road Surface

Dry189 (77.5%)
↑21.9%prior 155
Wet21 (8.6%)
↓-16.0%prior 25
Snow18 (7.4%)
↓-33.3%prior 27
Ice/frost11 (4.5%)
↓-31.3%prior 16
Gravel5 (2.0%)

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

Vehicles & Demographics

An analysis of vehicle and person data shows shifts in both make involvement and age demographics. Ford was the most common vehicle make involved in crashes in both years, with 79 in 2017 and 77 in 2016. Among people involved in crashes, the 45-54 age group saw a notable increase from 56 individuals in 2016 to 75 in 2017. In contrast, the 55-64 age group's involvement decreased from 84 people in 2016 to 68 in 2017.

Top Vehicle Makes (458 vehicles)

1
FORD79 (17.2%)
↑2.6%prior 77
2
CHEV57 (12.4%)
↑35.7%prior 42
3
CHEVROLET30 (6.6%)
↓-51.6%prior 62
4
TOYT30 (6.6%)
↑76.5%prior 17
5
GMC22 (4.8%)
↑69.2%prior 13
6
CHRY20 (4.4%)
↑66.7%prior 12
7
TOYOTA20 (4.4%)
↑11.1%prior 18
8
DODG19 (4.1%)
↑35.7%prior 14
9
DODGE16 (3.5%)
↓-15.8%prior 19
10
PONT14 (3.1%)
↑180.0%prior 5

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

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

Sex Distribution (354 persons with recorded sex)

Male205 (57.9%)
↓-3.3%prior 212
Female149 (42.1%)
↑15.5%prior 129

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: 261
  • Total persons involved: 535
  • Total vehicles involved: 458

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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