Yearly Traffic Safety Analysis

159 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Sac County, total traffic crashes increased from 131 in 2019 to 159 in 2020, representing a 21.4% year-over-year rise. During this same period, the number of people injured grew from 41 to 49, an increase of 19.5%, while fatalities remained unchanged at one death in each period. The most notable shift was the increase in total crash volume, accompanied by a shift in the peak time for collisions from the evening commute to later at night.

159

▲ 21.4%was 131

Total Crash Events

1

Persons Killed

49

▲ 19.5%was 41

Persons Injured

1

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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Sac County indicates a rising trend year-over-year. Total collisions increased by 21.4%, from 131 in 2019 to 159 in 2020. Similarly, the number of individuals injured in these incidents rose by 19.5% from 41 to 49, while the number of fatalities held steady at one.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

1

Pedestrians Injured

Prior: 0→%

48

Motorists Injured

Prior: 41↑17.1%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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 2020, the peak day for crashes was Friday with 30 incidents, a change from 2019 when Thursday was the peak day with 22 crashes. The peak hour also changed, moving from the 5 p.m. hour (14 crashes) in 2019 to the 9 p.m. hour (12 crashes) in 2020.

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the number of fatal crashes remained constant at one in both 2019 and 2020, the distribution of injury severity changed. The count of serious injury crashes decreased from 5 to 3 year-over-year. However, minor injury crashes increased from 15 to 23, and possible injury crashes rose from 8 to 12. Consequently, the total number of crashes resulting in any level of injury increased from 28 in 2019 to 38 in 2020.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.6%
→0.0%prior 1
Serious Injury3serious injury crashes1.9%
↓-40.0%prior 5
Minor Injury23minor injury crashes14.5%
↑53.3%prior 15
Possible Injury12possible injury crashes7.5%
↑50.0%prior 8
No Injury120no injury crashes75.5%
↑17.6%prior 102

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the leading contributing factor in both years, with the count rising slightly from 50 in 2019 to 52 in 2020. The most significant changes were seen in other factors; crashes attributed to 'Ran off road - straight' increased by 175% in count from 4 to 11, and 'Followed too close' incidents increased by 167% in count from 3 to 8. Conversely, crashes due to 'Driving too fast for conditions' decreased by 33% in count, from 12 incidents in 2019 to 8 in 2020.

Officer-Reported Primary Contributing Cause

Animal52 (32.7%)↑4.0%prior 50
Lost Control11 (6.9%)↑10.0%prior 10
Ran off road - straight11 (6.9%)
Driving too fast for conditions8 (5%)↓-33.3%prior 12
Followed too close8 (5%)
FTYROW: From stop sign7 (4.4%)
Driver Distraction: Inattentive/lost in thought6 (3.8%)
Ran off road - left6 (3.8%)
Other (explain in narrative): No improper action5 (3.1%)
Other (explain in narrative): Other5 (3.1%)→0.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

When comparing conditions, a larger proportion of crashes in 2020 occurred in seemingly favorable environments compared to 2019. Crashes in clear weather constituted 67.9% of the total in 2020, up from a 55.0% share in 2019. Similarly, the share of crashes on dry roads increased from 54.2% to 62.9%, and the share during daylight hours rose slightly from 44.3% to 45.9%.

Weather

Clear108 (78.8%)
↑50.0%prior 72
Cloudy13 (9.5%)
↓-40.9%prior 22
Snow5 (3.6%)
Freezing rain/drizzle5 (3.6%)
→0.0%prior 5
Rain3 (2.2%)
Fog, smoke, smog1 (0.7%)
Severe Winds1 (0.7%)
Blowing Snow1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight73 (52.9%)
↑25.9%prior 58
Dark - roadway not lighted44 (31.9%)
↑25.7%prior 35
Dark - roadway lighted13 (9.4%)
↑85.7%prior 7
Dawn5 (3.6%)
Dusk3 (2.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry100 (72.5%)
↑40.8%prior 71
Ice/frost12 (8.7%)
↑50.0%prior 8
Gravel12 (8.7%)
↑71.4%prior 7
Snow6 (4.3%)
↓-40.0%prior 10
Wet5 (3.6%)
↓-44.4%prior 9
Slush2 (1.4%)
Mud, dirt1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes, Ford and Chevrolet, remained consistent across both years. A more significant shift occurred in the age distribution of persons involved in crashes. The number of individuals in the 16-20 age group increased from 32 to 49, and the 21-25 age group saw an increase from 10 to 32. In contrast, the number of persons aged 65 and older involved in crashes decreased from 43 in 2019 to 34 in 2020.

Top Vehicle Makes (220 vehicles)

1
FORD40 (18.2%)
↑48.1%prior 27
2
CHEV38 (17.3%)
↑22.6%prior 31
3
CHEVROLET21 (9.5%)
↑40.0%prior 15
4
DODG9 (4.1%)
↓-25.0%prior 12
5
DODGE8 (3.6%)
6
PETERBILT7 (3.2%)
↑40.0%prior 5
7
TOYT7 (3.2%)
8
GMC6 (2.7%)
↓-50.0%prior 12
9
HONDA6 (2.7%)
10
JEEP5 (2.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

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

Sex Distribution (210 persons with recorded sex)

Male128 (61.0%)
↑19.6%prior 107
Female82 (39.0%)
↑49.1%prior 55

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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: 2020-01-01 through 2020-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 159
  • Total persons involved: 301
  • Total vehicles involved: 220

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: 2020." Published September 9, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2020-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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