Yearly Traffic Safety Analysis

999 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Cerro Gordo County recorded 999 total crashes, a 6.6% decrease from the 1,069 crashes reported in 2016. Despite the overall reduction in collisions, the number of fatalities doubled, increasing from 6 in the prior period to 12 in the current period. The total number of injuries also rose from 298 to 351.

999

▼ -6.5%was 1,069

Total Crash Events

12

▲ 100.0%was 6

Persons Killed

351

▲ 17.8%was 298

Persons Injured

7

▲ 16.7%was 6

Fatal Crash Events

Note: "Persons Killed" (12) counts individual fatalities across all crash events. "Fatal" in the severity table below (7) 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

The overall trend in crash volume shows a decrease, with 70 fewer crashes in 2017 compared to 2016, representing a 6.6% reduction. However, the severity of these crashes increased, as total injuries rose by 17.8% (from 298 to 351) and total fatalities doubled from 6 to 12.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

12

Motorists Killed

Prior: 5↑140.0%

8

Pedestrians Injured

Prior: 9↓-11.1%

12

Cyclists Injured

Prior: 6↑100.0%

331

Motorists Injured

Prior: 283↑17.0%

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 showed some shifts between the two periods. While Friday remained the peak day for crashes in both years, the volume on Fridays decreased from 204 in 2016 to 174 in 2017. The peak hour for crashes shifted later in the afternoon, moving from 3 p.m. in 2016 (86 crashes) to 5 p.m. in 2017 (87 crashes).

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

While total crashes decreased, the severity of outcomes worsened year-over-year. The number of fatal crashes increased from 6 to 7, and the total number of people killed doubled from 6 to 12. The proportion of crashes resulting in minor injuries grew from 7.1% to 9.5% of all crashes, while the proportion of no-injury crashes fell from 76.0% to 72.5%. The share of serious injury crashes remained constant at 2.0%.

Severity is per crash event (most severe injury). 7 fatal crash events resulted in 12 persons killed.

Outcome by Severity (Crash Events)

Fatal7fatal crashes0.7%
↑16.7%prior 6
Serious Injury20serious injury crashes2%
↓-4.8%prior 21
Minor Injury95minor injury crashes9.5%
↑25.0%prior 76
Possible Injury153possible injury crashes15.3%
↓-0.6%prior 154
No Injury724no injury crashes72.5%
↓-10.8%prior 812

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

Collisions with animals remained the leading contributing factor in both 2017 (157 crashes) and 2016 (169 crashes), despite a decrease in count. The count of crashes attributed to 'Ran off road - left' increased from 63 to 73, while incidents of 'Followed too close' decreased from 88 to 85. Crashes due to 'FTYROW: From stop sign' were unchanged at 60 in both periods, though its rank among top factors shifted.

Officer-Reported Primary Contributing Cause

Animal157 (15.7%)↓-7.1%prior 169
Followed too close85 (8.5%)↓-3.4%prior 88
Other (explain in narrative): Other83 (8.3%)↓-9.8%prior 92
Ran off road - left73 (7.3%)↑15.9%prior 63
Driving too fast for conditions61 (6.1%)↑5.2%prior 58
FTYROW: From stop sign60 (6%)→0.0%prior 60
Lost Control39 (3.9%)↓-11.4%prior 44
Ran Traffic Signal33 (3.3%)↓-19.5%prior 41
Ran off road - straight27 (2.7%)→0.0%prior 27
Made improper turn27 (2.7%)↓-25.0%prior 36

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 periods occurred in clear weather and on dry roads. However, the proportion of crashes occurring in daylight decreased from 65.5% in 2016 to 60.1% in 2017. Crashes attributed to snow conditions saw a significant drop, falling from 68 incidents in 2016 to 28 in 2017. Similarly, the total number of crashes on snow, ice, or slush-covered roads declined from 158 to 124 year-over-year.

Weather

Clear535 (62.1%)
↑5.5%prior 507
Cloudy208 (24.2%)
↓-25.2%prior 278
Rain49 (5.7%)
↑75.0%prior 28
Snow28 (3.3%)
↓-58.8%prior 68
Freezing rain/drizzle19 (2.2%)
↑90.0%prior 10
Blowing Snow11 (1.3%)
↓-38.9%prior 18
Fog, smoke, smog6 (0.7%)
↓-40.0%prior 10
Severe Winds3 (0.3%)
Sleet, hail1 (0.1%)
Other (explain in narrative)1 (0.1%)

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

Lighting

Daylight600 (69.5%)
↓-14.3%prior 700
Dark - roadway lighted121 (14.0%)
↑6.1%prior 114
Dark - roadway not lighted92 (10.7%)
↑26.0%prior 73
Dusk27 (3.1%)
↑17.4%prior 23
Dawn19 (2.2%)
↑58.3%prior 12
Dark - unknown roadway lighting4 (0.5%)
↓-20.0%prior 5

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

Road Surface

Dry614 (71.1%)
↓-4.1%prior 640
Wet101 (11.7%)
↓-6.5%prior 108
Ice/frost65 (7.5%)
↑25.0%prior 52
Snow53 (6.1%)
↓-36.9%prior 84
Gravel17 (2.0%)
↑54.5%prior 11
Slush6 (0.7%)
↓-72.7%prior 22
Sand3 (0.3%)
Other (explain in narrative)3 (0.3%)
Mud, dirt1 (0.1%)
Oil1 (0.1%)

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

Vehicles & Demographics

The distribution of vehicle makes involved in crashes remained stable, with Ford and Chevrolet models being the most common in both 2017 and 2016. The total number of vehicles involved in crashes decreased from 1,833 to 1,666, in line with the overall reduction in collisions. The age demographics of persons involved in crashes also showed little change, with all major age brackets showing similar proportional representation across both periods.

Top Vehicle Makes (1,666 vehicles)

1
FORD299 (17.9%)
↓-8.6%prior 327
2
CHEV249 (14.9%)
↑10.2%prior 226
3
CHEVROLET123 (7.4%)
↓-35.6%prior 191
4
TOYT75 (4.5%)
↓-21.1%prior 95
5
GMC65 (3.9%)
↑16.1%prior 56
6
DODG63 (3.8%)
↑10.5%prior 57
7
JEEP48 (2.9%)
↑9.1%prior 44
8
CHRY48 (2.9%)
↑33.3%prior 36
9
DODGE45 (2.7%)
↓-27.4%prior 62
10
HOND44 (2.6%)
↑15.8%prior 38

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

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

Sex Distribution (1,254 persons with recorded sex)

Male683 (54.5%)
↓-13.3%prior 788
Female571 (45.5%)
↓-10.2%prior 636

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: 999
  • Total persons involved: 1,946
  • Total vehicles involved: 1,666

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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