Yearly Traffic Safety Analysis

1,069 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Cerro Gordo County, total traffic crashes increased by 9.3% from 978 in 2015 to 1,069 in 2016. While total fatalities decreased slightly from 7 to 6, the number of people injured rose from 265 to 298. The most notable year-over-year shift was a 75% increase in the number of serious injury crashes, which grew from 12 in the prior period to 21 in the current period.

1,069

▲ 9.3%was 978

Total Crash Events

6

▼ -14.3%was 7

Persons Killed

298

▲ 12.5%was 265

Persons Injured

6

Fatal Crash Events

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (6) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crash trends in Cerro Gordo County show an increase year-over-year. Total collisions rose from 978 to 1,069, a 9.3% increase. Similarly, the number of individuals injured in these incidents grew by 12.5%, from 265 to 298, while fatalities saw a slight decrease from 7 to 6.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 7↓-28.6%

9

Pedestrians Injured

Prior: 4↑125.0%

6

Cyclists Injured

Prior: 7↓-14.3%

283

Motorists Injured

Prior: 254↑11.4%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained largely consistent year-over-year. Friday was the day with the most crashes in both 2016 (204 crashes) and 2015 (200 crashes). However, the peak time for collisions shifted slightly; the 4 p.m. hour was the peak in 2015 with 92 crashes, while in 2016, the 12 p.m. and 3 p.m. hours tied for the peak with 86 crashes each.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes worsened in 2016 compared to the previous year. While the number of fatal crashes remained constant at 6 for both periods, crashes resulting in serious injuries increased significantly, rising 75% from 12 incidents in 2015 to 21 in 2016. Consequently, the share of crashes involving serious injuries grew from 1.2% to 2.0% of all collisions. Crashes resulting in minor or possible injuries also increased in count from 189 to 230.

Outcome by Severity (Crash Events)

Fatal6fatal crashes0.6%
→0.0%prior 6
Serious Injury21serious injury crashes2%
↑75.0%prior 12
Minor Injury76minor injury crashes7.1%
↑20.6%prior 63
Possible Injury154possible injury crashes14.4%
↑22.2%prior 126
No Injury812no injury crashes76%
↑5.3%prior 771

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes were consistent across both years, with 'Animal' being the most cited cause in both 2016 (169 crashes) and 2015 (154 crashes). The count of crashes attributed to an animal increased by 9.7%. Several other factors saw notable increases in count, including 'Ran off road - left,' which grew 70.3% from 37 to 63 incidents, and 'Followed too close,' which increased 17.3% from 75 to 88 incidents.

Officer-Reported Primary Contributing Cause

Animal169 (15.8%)↑9.7%prior 154
Other (explain in narrative): Other92 (8.6%)↑2.2%prior 90
Followed too close88 (8.2%)↑17.3%prior 75
Ran off road - left63 (5.9%)↑70.3%prior 37
FTYROW: From stop sign60 (5.6%)↓-11.8%prior 68
Driving too fast for conditions58 (5.4%)↓-12.1%prior 66
Lost Control44 (4.1%)↑37.5%prior 32
Ran Traffic Signal41 (3.8%)↑17.1%prior 35
Made improper turn36 (3.4%)↓-10.0%prior 40
Ran Stop Sign36 (3.4%)↑56.5%prior 23

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions showed some shifts between the two periods. The number of crashes in cloudy weather saw a substantial increase from 181 in 2015 to 278 in 2016, while crashes in clear weather remained nearly unchanged (506 vs. 507). Collisions on dry road surfaces increased from 567 to 640, and crashes during daylight hours also rose from 614 to 700, reflecting the overall increase in total crashes.

Weather

Clear507 (54.9%)
↑0.2%prior 506
Cloudy278 (30.1%)
↑53.6%prior 181
Snow68 (7.4%)
↑15.3%prior 59
Rain28 (3.0%)
↓-57.6%prior 66
Blowing Snow18 (2.0%)
↑125.0%prior 8
Freezing rain/drizzle10 (1.1%)
↓-9.1%prior 11
Fog, smoke, smog10 (1.1%)
Other (explain in narrative)2 (0.2%)
Sleet, hail1 (0.1%)
Severe Winds1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight700 (75.5%)
↑14.0%prior 614
Dark - roadway lighted114 (12.3%)
↑14.0%prior 100
Dark - roadway not lighted73 (7.9%)
↓-2.7%prior 75
Dusk23 (2.5%)
↓-23.3%prior 30
Dawn12 (1.3%)
→0.0%prior 12
Dark - unknown roadway lighting5 (0.5%)
↓-28.6%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry640 (69.3%)
↑12.9%prior 567
Wet108 (11.7%)
↓-4.4%prior 113
Snow84 (9.1%)
↑16.7%prior 72
Ice/frost52 (5.6%)
↓-8.8%prior 57
Slush22 (2.4%)
↑120.0%prior 10
Gravel11 (1.2%)
↓-31.3%prior 16
Other (explain in narrative)3 (0.3%)
Water (standing or moving)2 (0.2%)
Mud, dirt1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

The distribution of involved vehicle makes remained stable year-over-year, with Ford and Chevrolet continuing to be the top two most frequently involved makes in both periods. Analysis of person demographics shows that the number of individuals aged 55-64 involved in crashes decreased from 302 to 255. Other age groups, such as the 16-20 and 65+ cohorts, saw relatively stable numbers of involved persons between 2015 and 2016.

Top Vehicle Makes (1,833 vehicles)

1
FORD327 (17.8%)
↑2.5%prior 319
2
CHEV226 (12.3%)
↓-3.0%prior 233
3
CHEVROLET191 (10.4%)
↑20.9%prior 158
4
TOYT95 (5.2%)
↑35.7%prior 70
5
TOYOTA65 (3.5%)
↑25.0%prior 52
6
DODGE62 (3.4%)
↑24.0%prior 50
7
DODG57 (3.1%)
↓-24.0%prior 75
8
GMC56 (3.1%)
↑19.1%prior 47
9
HONDA48 (2.6%)
↑108.7%prior 23
10
PONT44 (2.4%)
↓-27.9%prior 61

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (1,424 persons with recorded sex)

Male788 (55.3%)
↓-4.6%prior 826
Female636 (44.7%)
↓-9.4%prior 702

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 1,069
  • Total persons involved: 2,035
  • Total vehicles involved: 1,833

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: 2016." Published September 9, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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