Yearly Traffic Safety Analysis

489 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Sioux County recorded 489 total crashes, a slight increase of 1.5% from the 482 crashes reported in 2018. While the number of fatalities remained stable at two for both years, the total number of injuries rose by 16.3% from 196 to 228. The most significant year-over-year change was a 50% increase in crashes resulting in serious injuries, which grew from 8 in 2018 to 12 in 2019.

489

▲ 1.5%was 482

Total Crash Events

2

Persons Killed

228

▲ 16.3%was 196

Persons Injured

2

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

Trend Summary

Overall traffic crash trends in Sioux County showed a slight increase between 2018 and 2019. The total number of crashes rose by 1.5%, from 482 to 489. This was accompanied by a more significant 16.3% increase in total injuries, which climbed from 196 to 228, while fatalities held steady at two in both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

4

Pedestrians Injured

Prior: 0→%

2

Cyclists Injured

Prior: 2→0.0%

222

Motorists Injured

Prior: 194↑14.4%

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 timing of crashes saw minor shifts between the two periods. In 2019, the peak day for crashes was Thursday with 90 incidents, moving from Friday in 2018 which had 89 crashes. The peak hour also shifted slightly earlier, from 5 p.m. in 2018 (43 crashes) to 4 p.m. in 2019 (51 crashes). Both years consistently show that crashes were most frequent on weekdays during afternoon commuting hours.

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 worsened slightly in 2019 compared to 2018, despite the number of fatal crashes holding steady at two in both years. Crashes resulting in serious injuries increased by 50%, from 8 incidents in 2018 to 12 in 2019, raising their share of total crashes from 1.7% to 2.5%. Similarly, minor injury crashes rose by 23.7% from 59 to 73, while crashes with possible injuries decreased from 90 to 79.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.4%
→0.0%prior 2
Serious Injury12serious injury crashes2.5%
↑50.0%prior 8
Minor Injury73minor injury crashes14.9%
↑23.7%prior 59
Possible Injury79possible injury crashes16.2%
↓-12.2%prior 90
No Injury323no injury crashes66.1%
→0.0%prior 323

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

Collisions with animals remained the leading contributing factor in both years, with a slight increase from 80 crashes in 2018 to 84 in 2019. The most notable shift in crash causation was a 53.8% increase in crashes attributed to "Failure to Yield Right of Way from a stop sign," which rose from 26 to 40 incidents. "Followed too close" also saw a significant 26% increase in count, from 50 to 63 crashes, moving it to the second-ranked factor. In contrast, crashes caused by "Driving too fast for conditions" decreased by 20.8%, from 53 to 42 incidents.

Officer-Reported Primary Contributing Cause

Animal84 (17.2%)↑5.0%prior 80
Followed too close63 (12.9%)↑26.0%prior 50
Driving too fast for conditions42 (8.6%)↓-20.8%prior 53
FTYROW: From stop sign40 (8.2%)↑53.8%prior 26
Lost Control34 (7%)↑13.3%prior 30
FTYROW: At uncontrolled intersection22 (4.5%)↑29.4%prior 17
Ran off road - left19 (3.9%)↓-5.0%prior 20
Ran off road - straight18 (3.7%)↓-25.0%prior 24
Ran Stop Sign18 (3.7%)↑20.0%prior 15
Other (explain in narrative): Other14 (2.9%)↓-36.4%prior 22

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 majority of crashes in both 2019 (71.8%) and 2018 (68.9%) occurred in clear or cloudy weather conditions. However, there was a noticeable decrease in crashes happening on adverse road surfaces; incidents on snow, ice, or slush-covered roads fell from 111 in 2018 to 90 in 2019. Correspondingly, crashes on dry roads increased from 230 to 251. Crashes in daylight conditions also increased their share, rising from 53.1% of all incidents in 2018 to 58.5% in 2019.

Weather

Clear231 (56.2%)
↑4.5%prior 221
Cloudy120 (29.2%)
↑8.1%prior 111
Rain23 (5.6%)
↑21.1%prior 19
Snow22 (5.4%)
↓-24.1%prior 29
Blowing Snow7 (1.7%)
→0.0%prior 7
Freezing rain/drizzle5 (1.2%)
↓-64.3%prior 14
Fog, smoke, smog3 (0.7%)
↓-72.7%prior 11

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

Lighting

Daylight286 (69.1%)
↑11.7%prior 256
Dark - roadway not lighted64 (15.5%)
↓-24.7%prior 85
Dark - roadway lighted40 (9.7%)
↑8.1%prior 37
Dusk14 (3.4%)
↓-6.7%prior 15
Dawn9 (2.2%)
↓-59.1%prior 22
Dark - unknown roadway lighting1 (0.2%)

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

Road Surface

Dry251 (60.9%)
↑9.1%prior 230
Wet50 (12.1%)
↑19.0%prior 42
Snow49 (11.9%)
↓-12.5%prior 56
Ice/frost35 (8.5%)
↓-31.4%prior 51
Gravel17 (4.1%)
↓-43.3%prior 30
Slush6 (1.5%)
Other (explain in narrative)3 (0.7%)
Mud, dirt1 (0.2%)

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

Vehicles & Demographics

Ford and Chevrolet vehicles were the most frequently involved in crashes in both periods, with Chevrolet's count increasing from 173 to 191 vehicles, while Ford's decreased from 151 to 134. An analysis of persons involved in crashes shows a notable increase in the representation of younger individuals. The number of persons aged 16-20 involved in crashes rose from 137 in 2018 to 204 in 2019, and the 0-15 age group's involvement more than doubled from 23 to 53 persons.

Top Vehicle Makes (778 vehicles)

1
FORD134 (17.2%)
↓-11.3%prior 151
2
CHEV121 (15.6%)
↓-1.6%prior 123
3
CHEVROLET70 (9%)
↑40.0%prior 50
4
BUIC36 (4.6%)
↑24.1%prior 29
5
GMC35 (4.5%)
↓-16.7%prior 42
6
PETERBILT27 (3.5%)
↑17.4%prior 23
7
DODG25 (3.2%)
↓-26.5%prior 34
8
CHRY24 (3.1%)
↑60.0%prior 15
9
JEEP22 (2.8%)
↓-18.5%prior 27
10
DODGE22 (2.8%)
↓-18.5%prior 27

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

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

Sex Distribution (738 persons with recorded sex)

Male452 (61.2%)
↑29.1%prior 350
Female286 (38.8%)
↑44.4%prior 198

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: 489
  • Total persons involved: 1,114
  • Total vehicles involved: 778

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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