Yearly Traffic Safety Analysis

255 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Dickinson County, total traffic crashes increased from 219 in 2015 to 255 in 2016, a rise of 16.4%. Despite the growth in overall collisions, the number of fatalities fell by 50%, from 4 to 2. The most notable shift was in crashes attributed to 'Followed too close,' which increased by 54% and became the leading contributing factor in 2016.

255

▲ 16.4%was 219

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

109

▲ 19.8%was 91

Persons Injured

2

▼ -50.0%was 4

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic safety trends in Dickinson County showed a notable increase in crash volume year-over-year. The total number of crashes rose by 16.4%, from 219 in 2015 to 255 in 2016. Similarly, the number of people injured increased by 19.8%, from 91 to 109. However, fatalities decreased significantly, dropping from 4 in 2015 to 2 in 2016.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

2

Pedestrians Injured

Prior: 2→0.0%

107

Motorists Injured

Prior: 85↑25.9%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted between the two periods. In 2016, Friday was the most common day for crashes, with 60 incidents, a change from 2015 when Tuesday had the highest frequency at 43 crashes. The peak hour for collisions also changed, moving from the 4 p.m. hour in 2015 (19 crashes) to the 12 p.m. hour in 2016 (24 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes increased, the severity of outcomes generally decreased from 2015 to 2016. Fatal crashes fell from 4 (1.8% of total) to 2 (0.8% of total), and serious injury crashes dropped from 14 (6.4%) to 10 (3.9%). Conversely, crashes resulting in possible injury or no injury saw their counts and share of the total increase. No-injury crashes rose from 146 in 2015 to 177 in 2016, accounting for 69.4% of all incidents in the latter year.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.8%
↓-50.0%prior 4
Serious Injury10serious injury crashes3.9%
↓-28.6%prior 14
Minor Injury26minor injury crashes10.2%
↑13.0%prior 23
Possible Injury40possible injury crashes15.7%
↑25.0%prior 32
No Injury177no injury crashes69.4%
↑21.2%prior 146

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

The ranking of top contributing factors shifted between 2015 and 2016. 'Followed too close' became the leading cause in 2016 with 37 incidents, a 54.2% increase from 24 incidents in 2015. Collisions involving animals, the top factor in 2015 with 30 crashes, decreased to 23 crashes in 2016, becoming the second-leading cause. 'Driving too fast for conditions' remained the third-most common factor, increasing from 15 to 21 incidents.

Officer-Reported Primary Contributing Cause

Followed too close37 (14.5%)↑54.2%prior 24
Animal23 (9%)↓-23.3%prior 30
Driving too fast for conditions21 (8.2%)↑40.0%prior 15
FTYROW: From stop sign20 (7.8%)↑66.7%prior 12
Other (explain in narrative): Other14 (5.5%)↑100.0%prior 7
FTYROW: Making left turn14 (5.5%)↑7.7%prior 13
Ran off road - straight12 (4.7%)
Other (explain in narrative): No improper action10 (3.9%)
Made improper turn10 (3.9%)
Lost Control9 (3.5%)↓-10.0%prior 10

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes across environmental conditions remained largely consistent year-over-year. Crashes in daylight accounted for approximately 68% of all incidents in both 2015 and 2016. Similarly, incidents on dry road surfaces made up the majority in both periods (62.1% in 2015 and 60.8% in 2016). There were no significant shifts in the proportions of crashes occurring in adverse weather, lighting, or road surface conditions.

Weather

Clear156 (67.0%)
↑30.0%prior 120
Cloudy38 (16.3%)
↑11.8%prior 34
Snow16 (6.9%)
↑23.1%prior 13
Rain10 (4.3%)
↓-33.3%prior 15
Blowing Snow5 (2.1%)
Freezing rain/drizzle3 (1.3%)
Fog, smoke, smog3 (1.3%)
Severe Winds1 (0.4%)
Sleet, hail1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight173 (73.6%)
↑16.9%prior 148
Dark - roadway lighted30 (12.8%)
↑36.4%prior 22
Dark - roadway not lighted18 (7.7%)
↑5.9%prior 17
Dawn9 (3.8%)
Dusk4 (1.7%)
Dark - unknown roadway lighting1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry155 (66.5%)
↑14.0%prior 136
Snow27 (11.6%)
↑35.0%prior 20
Wet25 (10.7%)
↑8.7%prior 23
Ice/frost16 (6.9%)
↑100.0%prior 8
Slush5 (2.1%)
Gravel2 (0.9%)
↓-66.7%prior 6
Other (explain in narrative)1 (0.4%)
Water (standing or moving)1 (0.4%)
Mud, dirt1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

The types of vehicles most frequently involved in crashes were consistent, with Chevrolet and Ford models leading in both years. An analysis of persons involved shows a shift in age demographics. The 65+ age group became the most represented group in 2016 with 86 individuals, up from 68 in 2015. The number of individuals aged 16-20 involved in crashes also grew significantly, from 50 in 2015 to 69 in 2016.

Top Vehicle Makes (444 vehicles)

1
FORD77 (17.3%)
↑32.8%prior 58
2
CHEVROLET62 (14%)
↑8.8%prior 57
3
CHEV42 (9.5%)
↓-20.8%prior 53
4
DODGE19 (4.3%)
↑72.7%prior 11
5
TOYOTA18 (4.1%)
↑38.5%prior 13
6
TOYT17 (3.8%)
↑70.0%prior 10
7
JEEP16 (3.6%)
↑166.7%prior 6
8
DODG14 (3.2%)
↓-26.3%prior 19
9
GMC13 (2.9%)
↑62.5%prior 8
10
CHRY12 (2.7%)
↑71.4%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (341 persons with recorded sex)

Male212 (62.2%)
↑15.8%prior 183
Female129 (37.8%)
↓-15.7%prior 153

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 255
  • Total persons involved: 512
  • Total vehicles involved: 444

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: 2016." Published September 9, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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