Yearly Traffic Safety Analysis

181 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Sac County recorded 181 total crashes, a 17.5% increase from the 154 crashes reported in 2016. Despite the rise in total collisions, reported fatalities decreased from 4 to 3 and total injuries fell from 80 to 68. A notable shift in contributing factors was observed, with crashes attributed to animals increasing from 32 in 2016 to 50 in 2017.

181

▲ 17.5%was 154

Total Crash Events

3

▼ -25.0%was 4

Persons Killed

68

▼ -15.0%was 80

Persons Injured

2

▼ -33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (3) 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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash volume in Sac County increased from 2016 to 2017. Total reported crashes rose by 17.5%, from 154 to 181 incidents. However, the severity of these crashes trended downward, with total fatalities decreasing from 4 to 3 and total injuries declining from 80 to 68 over the same period.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

2

Motorists Killed

Prior: 4↓-50.0%

1

Pedestrians Injured

Prior: 0→%

67

Motorists Injured

Prior: 80↓-16.3%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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 2016 and 2017. The most frequent day for crashes moved from Saturday (30 incidents) in 2016 to Friday (32 incidents) in 2017. A more pronounced change occurred in the peak hour, which shifted from 8 AM (14 crashes) in the prior year to 5 PM (20 crashes) in the current year.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes decreased from 2016 to 2017. The number of fatal crashes fell from 3 to 2, and the proportion of crashes involving a fatality dropped from 1.9% to 1.1%. Similarly, serious injury crashes declined from 6 to 4. Correspondingly, the share of crashes with no reported injuries increased from 63.0% of all incidents in 2016 to 69.1% in 2017.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.1%
↓-33.3%prior 3
Serious Injury4serious injury crashes2.2%
↓-33.3%prior 6
Minor Injury27minor injury crashes14.9%
↓-6.9%prior 29
Possible Injury23possible injury crashes12.7%
↑21.1%prior 19
No Injury125no injury crashes69.1%
↑28.9%prior 97

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

In both 2016 and 2017, 'Animal' was the leading contributing factor in crashes. The number of crashes attributed to this factor increased by 56%, from 32 incidents in 2016 to 50 in 2017, and its share of all crashes grew from 20.8% to 27.6%. 'Lost Control' remained the second-most common factor, though its count decreased from 21 to 18. Crashes involving 'Followed too close' more than doubled from 5 to 11 incidents.

Officer-Reported Primary Contributing Cause

Animal50 (27.6%)↑56.3%prior 32
Lost Control18 (9.9%)↓-14.3%prior 21
Other (explain in narrative): Other17 (9.4%)↑112.5%prior 8
Followed too close11 (6.1%)↑120.0%prior 5
Ran off road - straight8 (4.4%)↓-33.3%prior 12
Ran Stop Sign8 (4.4%)↑33.3%prior 6
Driving too fast for conditions8 (4.4%)→0.0%prior 8
FTYROW: From stop sign6 (3.3%)↓-33.3%prior 9
Driver Distraction: Other interior distraction5 (2.8%)
Ran off road - left5 (2.8%)↓-28.6%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The majority of crashes in both 2017 and 2016 occurred in clear weather and on dry roads, with these conditions accounting for a stable proportion of incidents year-over-year. In 2017, 54.1% of crashes were in clear weather and 51.4% on dry surfaces, compared to 51.3% and 51.9% respectively in 2016. Crashes occurring in dark, unlighted conditions decreased from 34 to 27, while those on icy or frosty roads increased from 11 to 15.

Weather

Clear98 (67.1%)
↑24.1%prior 79
Cloudy26 (17.8%)
↑23.8%prior 21
Rain6 (4.1%)
↑20.0%prior 5
Snow5 (3.4%)
↓-37.5%prior 8
Freezing rain/drizzle5 (3.4%)
Fog, smoke, smog3 (2.1%)
Other (explain in narrative)1 (0.7%)
Severe Winds1 (0.7%)
Blowing Snow1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight96 (65.3%)
↑14.3%prior 84
Dark - roadway not lighted27 (18.4%)
↓-20.6%prior 34
Dark - roadway lighted10 (6.8%)
↑42.9%prior 7
Dawn7 (4.8%)
Dusk5 (3.4%)
Dark - unknown roadway lighting2 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry93 (63.7%)
↑16.3%prior 80
Wet16 (11.0%)
↑6.7%prior 15
Ice/frost15 (10.3%)
↑36.4%prior 11
Gravel11 (7.5%)
→0.0%prior 11
Snow9 (6.2%)
→0.0%prior 9
Mud, dirt1 (0.7%)
Other (explain in narrative)1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Chevrolet and Ford models leading in both years. The number of Chevrolet vehicles involved increased from 53 in 2016 to 73 in 2017, while Ford vehicles increased from 36 to 49. Regarding persons involved, there were notable increases in several age groups: the number of individuals aged 26-34 grew from 28 to 45, and the 45-54 age group increased from 27 to 45. The 16-20 age group also saw an increase from 27 to 34 persons involved.

Top Vehicle Makes (260 vehicles)

1
FORD49 (18.8%)
↑36.1%prior 36
2
CHEV40 (15.4%)
↑73.9%prior 23
3
CHEVROLET33 (12.7%)
↑10.0%prior 30
4
GMC11 (4.2%)
↑37.5%prior 8
5
DODG9 (3.5%)
6
DODGE8 (3.1%)
↑33.3%prior 6
7
TOYT8 (3.1%)
↑14.3%prior 7
8
PONT8 (3.1%)
↑60.0%prior 5
9
BUIC7 (2.7%)
→0.0%prior 7
10
NR6 (2.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (171 persons with recorded sex)

Male124 (72.5%)
↑27.8%prior 97
Female47 (27.5%)
↓-25.4%prior 63

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 181
  • Total persons involved: 315
  • Total vehicles involved: 260

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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