Yearly Traffic Safety Analysis

165 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Humboldt County recorded 165 total crashes, a decrease of approximately 11.8% from the 187 crashes documented in 2018. Despite the overall reduction in collisions, the total number of injuries increased by 7%, from 57 to 61. The most significant year-over-year change was a 50% reduction in traffic-related fatalities, which fell from 4 in 2018 to 2 in 2019.

165

▼ -11.8%was 187

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

61

▲ 7.0%was 57

Persons Injured

2

▼ -50.0%was 4

Fatal Crash Events

Note: "Persons Killed" (2) 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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic safety trends in Humboldt County showed improvement in 2019 compared to the prior year. The total number of crashes decreased by 11.8%, from 187 in 2018 to 165 in 2019. This positive trend was also reflected in fatalities, which were halved from 4 to 2. However, the total number of injuries saw a slight increase, rising from 57 to 61.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

1

Cyclists Injured

Prior: 1→0.0%

60

Motorists Injured

Prior: 55↑9.1%

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 timing of crashes shifted between the two periods. In 2019, the highest number of crashes occurred on Thursdays with 32 incidents, a change from 2018 when Wednesdays saw the most collisions at 39. The peak hour for crashes also moved from a tie between 5 p.m. and 9 p.m. in 2018 (16 crashes each) to a single peak at 4 p.m. in 2019 (16 crashes). The afternoon commute hours remained a consistent period of high crash activity across both years.

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

Crash severity saw a mixed but generally positive change year-over-year. The number of fatal crashes was cut in half, from 4 in 2018 to 2 in 2019, with the corresponding fatal crash rate decreasing from 2.1% to 1.2% of all crashes. While the total count of crashes involving any injury remained constant at 43, their composition changed. Crashes resulting in serious injuries increased from 4 to 6, and minor injury crashes rose from 15 to 21, while crashes with possible injuries decreased from 24 to 16.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.2%
↓-50.0%prior 4
Serious Injury6serious injury crashes3.6%
↑50.0%prior 4
Minor Injury21minor injury crashes12.7%
↑40.0%prior 15
Possible Injury16possible injury crashes9.7%
↓-33.3%prior 24
No Injury120no injury crashes72.7%
↓-14.3%prior 140

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 2019 and 2018, though the count of such incidents decreased by 26.7% from 45 to 33. While the top factor saw a decline, other factors increased in frequency. Crashes attributed to 'Driving too fast for conditions' rose from 13 to 16 incidents, and those involving 'Failure to Yield Right of Way from a stop sign' increased from 11 to 13. Conversely, crashes where a driver lost control fell from 15 incidents in 2018 to 11 in 2019.

Officer-Reported Primary Contributing Cause

Animal33 (20%)↓-26.7%prior 45
Driving too fast for conditions16 (9.7%)↑23.1%prior 13
FTYROW: From stop sign13 (7.9%)↑18.2%prior 11
Lost Control11 (6.7%)↓-26.7%prior 15
Driver Distraction: Other interior distraction10 (6.1%)→0.0%prior 10
Other (explain in narrative): Other9 (5.5%)↓-30.8%prior 13
Ran off road - straight8 (4.8%)→0.0%prior 8
Ran off road - left7 (4.2%)→0.0%prior 7
FTYROW: Making left turn4 (2.4%)↓-33.3%prior 6
Ran Traffic Signal4 (2.4%)

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 environmental conditions at the time of crashes remained broadly consistent between 2018 and 2019. In both years, the majority of crashes occurred during daylight hours (59.4% in 2019 vs. 58.3% in 2018) and on dry road surfaces (47.9% in 2019 vs. 44.9% in 2018). Crashes on adverse road surfaces like ice, snow, or wet pavement accounted for approximately one-third of all incidents in both periods (33.9% in 2019 and 31.6% in 2018), showing no significant year-over-year shift in the proportion of condition-related collisions.

Weather

Clear78 (57.8%)
↓-1.3%prior 79
Cloudy42 (31.1%)
↑13.5%prior 37
Snow5 (3.7%)
↓-58.3%prior 12
Rain3 (2.2%)
↓-70.0%prior 10
Freezing rain/drizzle2 (1.5%)
Severe Winds2 (1.5%)
Fog, smoke, smog2 (1.5%)
Sleet, hail1 (0.7%)

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

Lighting

Daylight98 (71.5%)
↓-10.1%prior 109
Dark - roadway not lighted24 (17.5%)
↑84.6%prior 13
Dark - roadway lighted7 (5.1%)
↓-56.3%prior 16
Dusk4 (2.9%)
Dawn2 (1.5%)
↓-60.0%prior 5
Dark - unknown roadway lighting2 (1.5%)

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

Road Surface

Dry79 (58.1%)
↓-6.0%prior 84
Ice/frost26 (19.1%)
↑13.0%prior 23
Wet17 (12.5%)
↑13.3%prior 15
Snow12 (8.8%)
↓-14.3%prior 14
Slush1 (0.7%)
↓-85.7%prior 7
Gravel1 (0.7%)

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 similar year-over-year, with Chevrolet and Ford consistently being the most common. In 2019, there were 66 Chevrolet vehicles (combining 'CHEV' and 'CHEVROLET' entries) and 34 Ford vehicles involved in crashes, compared to 64 and 45, respectively, in 2018. An analysis of person demographics reveals a significant drop in the number of individuals aged 16-20 involved in crashes, from 66 in 2018 to 45 in 2019. Conversely, involvement for the 35-44 age group increased from 41 to 54 persons.

Top Vehicle Makes (256 vehicles)

1
CHEV50 (19.5%)
↑4.2%prior 48
2
FORD34 (13.3%)
↓-24.4%prior 45
3
GMC17 (6.6%)
→0.0%prior 17
4
CHEVROLET16 (6.3%)
→0.0%prior 16
5
TOYT14 (5.5%)
↑100.0%prior 7
6
DODG10 (3.9%)
↓-44.4%prior 18
7
BUIC9 (3.5%)
→0.0%prior 9
8
JEEP7 (2.7%)
↓-41.7%prior 12
9
CHRY7 (2.7%)
↓-50.0%prior 14
10
NR6 (2.3%)

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

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

Sex Distribution (232 persons with recorded sex)

Male119 (51.3%)
↓-6.3%prior 127
Female113 (48.7%)
↑41.3%prior 80

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: 165
  • Total persons involved: 367
  • Total vehicles involved: 256

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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