Yearly Traffic Safety Analysis

1,056 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Cerro Gordo County recorded 1,056 total crashes, a 2.2% increase from the 1,033 crashes reported in 2018. While the total number of injuries remained unchanged at 299 for both years, the number of fatalities rose from 5 in 2018 to 7 in 2019. This increase in fatalities occurred across 6 fatal crashes in 2019, compared to 5 in the prior year.

1,056

▲ 2.2%was 1,033

Total Crash Events

7

▲ 40.0%was 5

Persons Killed

299

Persons Injured

6

▲ 20.0%was 5

Fatal Crash Events

Note: "Persons Killed" (7) 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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Cerro Gordo County showed a slight increase year-over-year, with total collisions rising from 1,033 in 2018 to 1,056 in 2019. This represents a 2.2% increase in crash volume. While the total number of injuries was identical in both periods at 299, fatalities increased by 40%, from 5 in 2018 to 7 in 2019.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

7

Motorists Killed

Prior: 4↑75.0%

0

Other Killed

Prior: 0→0.0%

7

Pedestrians Injured

Prior: 7→0.0%

4

Cyclists Injured

Prior: 7↓-42.9%

287

Motorists Injured

Prior: 285↑0.7%

1

Other Injured

Prior: 0→%

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 temporal patterns of crashes remained largely consistent year-over-year. Friday was the day with the most crashes in both 2019 (190 crashes) and 2018 (187 crashes). However, the peak hour for collisions shifted earlier from 5 p.m. in 2018 (93 crashes) to 3 p.m. in 2019 (96 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 shifted between the two periods, with the number of fatal crashes increasing from 5 in 2018 to 6 in 2019. Crashes resulting in minor injuries rose from 70 to 86, while those with possible injuries decreased from 160 to 132. The proportion of crashes resulting in no injuries increased from 76.1% in 2018 to 77.5% in 2019.

Severity is per crash event (most severe injury). 6 fatal crash events resulted in 7 persons killed.

Outcome by Severity (Crash Events)

Fatal6fatal crashes0.6%
↑20.0%prior 5
Serious Injury14serious injury crashes1.3%
↑16.7%prior 12
Minor Injury86minor injury crashes8.1%
↑22.9%prior 70
Possible Injury132possible injury crashes12.5%
↓-17.5%prior 160
No Injury818no injury crashes77.5%
↑4.1%prior 786

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 involving an animal remained the leading contributing factor in both periods, with 164 incidents in 2019 compared to 163 in 2018. The factor 'Driving too fast for conditions' saw a notable increase, rising from 78 incidents in 2018 to 93 in 2019, and moving from the fourth to the second most common cause. 'Failure to yield right of way from a stop sign' also increased from 68 to 81 incidents, while crashes attributed to 'Followed too close' decreased from 93 to 85.

Officer-Reported Primary Contributing Cause

Animal164 (15.5%)↑0.6%prior 163
Driving too fast for conditions93 (8.8%)↑19.2%prior 78
Other (explain in narrative): Other91 (8.6%)↓-4.2%prior 95
Followed too close85 (8%)↓-8.6%prior 93
FTYROW: From stop sign81 (7.7%)↑19.1%prior 68
Ran off road - left51 (4.8%)↓-21.5%prior 65
FTYROW: Making left turn36 (3.4%)↑12.5%prior 32
Lost Control33 (3.1%)↓-21.4%prior 42
Made improper turn29 (2.7%)↑93.3%prior 15
Driver Distraction: Other interior distraction28 (2.7%)→0.0%prior 28

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

Road & Environmental Conditions

The proportion of crashes occurring on adverse road surfaces increased from 29.9% in 2018 to 36.4% in 2019. This was driven by a rise in crashes on snowy (137 vs. 82) and icy (96 vs. 80) roads, while crashes on dry surfaces decreased from 570 to 534. Lighting conditions remained stable, with approximately 64% of crashes in 2019 and 63% in 2018 occurring during daylight.

Weather

Clear555 (60.1%)
↓-1.2%prior 562
Cloudy203 (22.0%)
↑8.0%prior 188
Rain52 (5.6%)
↑13.0%prior 46
Snow51 (5.5%)
↑4.1%prior 49
Blowing Snow30 (3.2%)
↑76.5%prior 17
Freezing rain/drizzle20 (2.2%)
↑5.3%prior 19
Fog, smoke, smog5 (0.5%)
Severe Winds4 (0.4%)
Sleet, hail2 (0.2%)
↓-60.0%prior 5
Other (explain in narrative)2 (0.2%)

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

Lighting

Daylight677 (73.3%)
↑4.2%prior 650
Dark - roadway lighted116 (12.6%)
↓-7.2%prior 125
Dark - roadway not lighted93 (10.1%)
↑17.7%prior 79
Dusk19 (2.1%)
→0.0%prior 19
Dawn13 (1.4%)
↑18.2%prior 11
Dark - unknown roadway lighting6 (0.6%)
↓-14.3%prior 7

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

Road Surface

Dry534 (57.5%)
↓-6.3%prior 570
Snow137 (14.8%)
↑67.1%prior 82
Wet135 (14.5%)
↑5.5%prior 128
Ice/frost96 (10.3%)
↑20.0%prior 80
Slush16 (1.7%)
↓-15.8%prior 19
Gravel5 (0.5%)
↓-44.4%prior 9
Other (explain in narrative)3 (0.3%)
Mud, dirt2 (0.2%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes were consistent, with Ford and Chevrolet models being the most common in both years. An analysis of persons involved in crashes shows a significant demographic shift: the number of individuals in the 55-64 age group increased from 257 to 371. Conversely, the number of persons aged 65 and older involved in crashes decreased from 329 to 272.

Top Vehicle Makes (1,818 vehicles)

1
FORD342 (18.8%)
↑3.6%prior 330
2
CHEV264 (14.5%)
↓-15.4%prior 312
3
CHEVROLET122 (6.7%)
↑22.0%prior 100
4
TOYT90 (5%)
↑3.4%prior 87
5
DODG77 (4.2%)
↑8.5%prior 71
6
GMC70 (3.9%)
↑20.7%prior 58
7
JEEP60 (3.3%)
↑13.2%prior 53
8
HOND57 (3.1%)
↓-19.7%prior 71
9
PONT49 (2.7%)
↓-9.3%prior 54
10
TOYOTA43 (2.4%)
↑53.6%prior 28

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

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

Sex Distribution (1,656 persons with recorded sex)

Male913 (55.1%)
↑16.8%prior 782
Female743 (44.9%)
↑17.7%prior 631

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: 1,056
  • Total persons involved: 2,389
  • Total vehicles involved: 1,818

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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