Yearly Traffic Safety Analysis

131 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Sac County, total traffic crashes decreased by 16.0%, from 156 in 2018 to 131 in 2019. This overall reduction was accompanied by a drop in fatalities from two to one and a slight decrease in injuries from 45 to 41. One of the most significant shifts was a 150% increase in crashes involving a driver under the influence, which rose from two incidents in 2018 to five in 2019.

131

▼ -16.0%was 156

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

41

▼ -8.9%was 45

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

Trend Summary

Overall, Sac County experienced a downward trend in traffic collisions from 2018 to 2019. The total number of crashes fell by 16.0% from 156 to 131. Similarly, the number of people killed in crashes was halved from two to one, and the total number of injuries decreased by 8.9% from 45 to 41.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 2↓-50.0%

41

Motorists Injured

Prior: 42↓-2.4%

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 shifted between the two periods. In 2019, the peak day for crashes was Thursday with 22 incidents, a change from Friday (29 incidents) in the prior year. The peak hour for collisions also shifted earlier, moving from 7 p.m. in 2018 (17 crashes) to the 5 p.m. commute hour in 2019 (14 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

The severity of crashes lessened from 2018 to 2019. The number of fatal crashes decreased from two to one, and serious injury crashes were halved from 10 to five. While the proportion of no-injury crashes remained stable at approximately 77-78% of all incidents, minor injury crashes increased in both count (from 13 to 15) and share of total crashes (from 8.3% to 11.5%).

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.8%
↓-50.0%prior 2
Serious Injury5serious injury crashes3.8%
↓-50.0%prior 10
Minor Injury15minor injury crashes11.5%
↑15.4%prior 13
Possible Injury8possible injury crashes6.1%
↓-27.3%prior 11
No Injury102no injury crashes77.9%
↓-15.0%prior 120

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 top contributing factor in both years, though the count decreased from 58 in 2018 to 50 in 2019. "Driving too fast for conditions" was the second-most cited factor in both periods, with its count also falling from 15 to 12. Incidents attributed to "Lost Control" increased from 8 to 10, while crashes from failure to yield at a stop sign decreased from 9 to 3.

Officer-Reported Primary Contributing Cause

Animal50 (38.2%)↓-13.8%prior 58
Driving too fast for conditions12 (9.2%)↓-20.0%prior 15
Lost Control10 (7.6%)↑25.0%prior 8
Other (explain in narrative): Other5 (3.8%)↓-50.0%prior 10
Operating vehicle in an reckless, erratic, careless, negligent manner5 (3.8%)→0.0%prior 5
Ran off road - straight4 (3.1%)↓-20.0%prior 5
Made improper turn4 (3.1%)
Other (explain in narrative): No improper action3 (2.3%)
FTYROW: From stop sign3 (2.3%)↓-66.7%prior 9
Failed to keep in proper lane3 (2.3%)

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

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with clear weather and dry roads being the most common circumstances in both 2018 and 2019. There was a notable decrease in crashes occurring on roads with ice or frost, which fell from 17 incidents in 2018 to 8 in 2019. Conversely, the proportion of crashes happening in darkness on unlighted roadways increased from 20.5% of all crashes in 2018 to 26.7% in 2019.

Weather

Clear72 (66.1%)
↓-1.4%prior 73
Cloudy22 (20.2%)
↓-21.4%prior 28
Freezing rain/drizzle5 (4.6%)
↓-37.5%prior 8
Snow4 (3.7%)
↓-33.3%prior 6
Blowing Snow3 (2.8%)
Rain2 (1.8%)
↓-66.7%prior 6
Fog, smoke, smog1 (0.9%)

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

Lighting

Daylight58 (53.2%)
↓-23.7%prior 76
Dark - roadway not lighted35 (32.1%)
↑9.4%prior 32
Dark - roadway lighted7 (6.4%)
↓-41.7%prior 12
Dawn4 (3.7%)
Dusk4 (3.7%)
Dark - unknown roadway lighting1 (0.9%)

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

Road Surface

Dry71 (65.1%)
↓-13.4%prior 82
Snow10 (9.2%)
↑25.0%prior 8
Wet9 (8.3%)
↑12.5%prior 8
Ice/frost8 (7.3%)
↓-52.9%prior 17
Gravel7 (6.4%)
↓-12.5%prior 8
Sand2 (1.8%)
Slush2 (1.8%)

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

Vehicles & Demographics

Chevrolet and Ford vehicles were the most frequently involved makes in crashes during both periods, with the count of Chevrolets decreasing from 66 to 46 year-over-year. Analysis of persons involved shows a significant increase in the 16-20 age group, whose involvement rose from 19 individuals in 2018 to 32 in 2019. In contrast, the number of individuals in the 26-34 age group involved in crashes decreased from 50 to 40.

Top Vehicle Makes (175 vehicles)

1
CHEV31 (17.7%)
↓-29.5%prior 44
2
FORD27 (15.4%)
↑12.5%prior 24
3
CHEVROLET15 (8.6%)
↓-31.8%prior 22
4
GMC12 (6.9%)
↑20.0%prior 10
5
DODG12 (6.9%)
↑33.3%prior 9
6
BUIC6 (3.4%)
↓-14.3%prior 7
7
PETERBILT5 (2.9%)
8
PONT5 (2.9%)
9
TOYT4 (2.3%)
↓-50.0%prior 8
10
DODGE4 (2.3%)

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

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

Sex Distribution (162 persons with recorded sex)

Male107 (66.0%)
↓-5.3%prior 113
Female55 (34.0%)
↑3.8%prior 53

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: 131
  • Total persons involved: 250
  • Total vehicles involved: 175

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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