Yearly Traffic Safety Analysis

250 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Dickinson County recorded 250 total crashes, a 1.6% increase from the 246 crashes documented in 2018. Despite the slight rise in total incidents, the most notable year-over-year shift was a significant decrease in traffic fatalities, which fell from 3 in 2018 to 1 in 2019. The total number of injuries also saw a reduction from 105 to 94.

250

▲ 1.6%was 246

Total Crash Events

1

▼ -66.7%was 3

Persons Killed

94

▼ -10.5%was 105

Persons Injured

1

▼ -66.7%was 3

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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash volume in Dickinson County remained relatively stable, with a minor 1.6% increase from 246 incidents in 2018 to 250 in 2019. While total crashes saw a slight rise, the severity of outcomes improved. The number of people killed in crashes fell from 3 to 1, and the total number of injuries decreased by 10.5% from 105 to 94.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 2↓-100.0%

1

Motorists Killed

Prior: 1→0.0%

2

Pedestrians Injured

Prior: 1↑100.0%

92

Motorists Injured

Prior: 104↓-11.5%

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 shifted between the two periods. In 2019, the peak day for crashes was Friday with 57 incidents, a change from 2018 when Saturday was the peak with 45 crashes. The busiest hour also shifted earlier, moving from 4 p.m. in 2018 (21 crashes) to 3 p.m. in 2019 (27 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

Crash severity outcomes showed a mixed trend in 2019 compared to 2018. The number of fatal crashes decreased from 3 to 1, lowering the fatal crash rate from 1.2% to 0.4% of all incidents. While the count of serious injury crashes increased from 5 to 8, crashes resulting in minor injuries fell substantially from 44 to 25. The proportion of crashes with no injuries remained stable at approximately 67% in both years.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
↓-66.7%prior 3
Serious Injury8serious injury crashes3.2%
↑60.0%prior 5
Minor Injury25minor injury crashes10%
↓-43.2%prior 44
Possible Injury48possible injury crashes19.2%
↑54.8%prior 31
No Injury168no injury crashes67.2%
↑3.1%prior 163

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

The leading contributing factors for crashes saw some shifts between 2018 and 2019. 'Followed too close' remained the primary factor, with its count increasing from 32 to 38 incidents. Crashes involving an 'Animal', the second-leading factor in 2018 with a count of 30, decreased to 17 incidents in 2019. 'Driving too fast for conditions' saw its count rise from 22 to 24, becoming the second most cited factor in 2019.

Officer-Reported Primary Contributing Cause

Followed too close38 (15.2%)↑18.8%prior 32
Driving too fast for conditions24 (9.6%)↑9.1%prior 22
Animal17 (6.8%)↓-43.3%prior 30
Other (explain in narrative): Other16 (6.4%)→0.0%prior 16
FTYROW: Making left turn16 (6.4%)↓-30.4%prior 23
FTYROW: From stop sign16 (6.4%)→0.0%prior 16
Ran off road - straight12 (4.8%)
Ran off road - left9 (3.6%)↑80.0%prior 5
Improper Backing9 (3.6%)
Lost Control8 (3.2%)↓-11.1%prior 9

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

Road & Environmental Conditions

The conditions under which crashes occurred were largely similar year-over-year, with most incidents happening in clear weather and on dry roads in both periods. In 2019, 70.4% of crashes occurred during daylight hours, an increase from a 66.3% share in 2018. The number of crashes on adverse road surfaces like snow, ice, or wet pavement saw a small increase from 74 incidents in 2018 to 79 in 2019.

Weather

Clear155 (66.8%)
↓-1.3%prior 157
Cloudy33 (14.2%)
↑32.0%prior 25
Snow16 (6.9%)
↑33.3%prior 12
Rain13 (5.6%)
↓-18.8%prior 16
Freezing rain/drizzle8 (3.4%)
Blowing Snow5 (2.2%)
Sleet, hail2 (0.9%)

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

Lighting

Daylight176 (74.9%)
↑8.0%prior 163
Dark - roadway lighted26 (11.1%)
↓-16.1%prior 31
Dark - roadway not lighted21 (8.9%)
↓-4.5%prior 22
Dusk8 (3.4%)
Dawn4 (1.7%)

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

Road Surface

Dry146 (62.7%)
↑4.3%prior 140
Snow31 (13.3%)
↑29.2%prior 24
Ice/frost23 (9.9%)
↑21.1%prior 19
Wet22 (9.4%)
↓-21.4%prior 28
Slush5 (2.1%)
Gravel4 (1.7%)
↓-20.0%prior 5
Sand1 (0.4%)
Mud, dirt1 (0.4%)

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

Vehicles & Demographics

Analysis of vehicles and persons involved shows shifts in both make and age demographics. Chevrolet and Ford remained the top two vehicle makes involved in crashes, with both seeing an increase in counts from 2018 to 2019. In terms of driver and passenger age, there was a notable increase in the involvement of individuals aged 21-25 (from 49 to 74) and those 65 and older (from 79 to 93). Conversely, the 16-20 age group saw a decrease in involvement, from 83 individuals in 2018 to 68 in 2019.

Top Vehicle Makes (450 vehicles)

1
FORD73 (16.2%)
↑21.7%prior 60
2
CHEV66 (14.7%)
↓-2.9%prior 68
3
CHEVROLET45 (10%)
↑25.0%prior 36
4
JEEP25 (5.6%)
↑31.6%prior 19
5
TOYT23 (5.1%)
↑4.5%prior 22
6
GMC22 (4.9%)
↑37.5%prior 16
7
DODG15 (3.3%)
↓-21.1%prior 19
8
HOND11 (2.4%)
↑22.2%prior 9
9
DODGE11 (2.4%)
↑10.0%prior 10
10
PONTIAC10 (2.2%)
↑66.7%prior 6

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

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

Sex Distribution (405 persons with recorded sex)

Male217 (53.6%)
↑3.3%prior 210
Female188 (46.4%)
↑43.5%prior 131

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: 250
  • Total persons involved: 598
  • Total vehicles involved: 450

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

ThatCarHitMe.com · An Injuria.ai Company