Yearly Traffic Safety Analysis

94 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Mitchell County, total crashes increased by 4.4%, from 90 incidents in 2018 to 94 in 2019. While the number of fatalities decreased from two to one, the number of people injured in these crashes rose significantly by 46.9%, from 32 in the prior year to 47 in the current year.

94

▲ 4.4%was 90

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

47

▲ 46.9%was 32

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 Mitchell County show a slight increase in total incidents, rising 4.4% from 90 in 2018 to 94 in 2019. This increase in crash volume was accompanied by a substantial 46.9% rise in injuries, from 32 to 47. However, the number of fatalities declined from two in 2018 to one in 2019.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 2↓-50.0%

47

Motorists Injured

Prior: 32↑46.9%

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 year-over-year. The peak day for incidents moved from Wednesday (17 crashes) in 2018 to Thursday (24 crashes) in 2019. The peak hour for crashes also shifted earlier, from 2 p.m. in 2018 to 12 p.m. in 2019, though both peak hours recorded 13 crashes in their respective 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

While fatal crashes decreased from two in 2018 to one in 2019, the overall severity of non-fatal crashes increased. The number of serious injury crashes more than doubled, rising from four to nine, and their share of all crashes increased from 4.4% to 9.6%. Similarly, minor injury crashes grew from 9 to 13 incidents, with their proportion increasing from 10.0% to 13.8% of all crashes.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.1%
↓-50.0%prior 2
Serious Injury9serious injury crashes9.6%
↑125.0%prior 4
Minor Injury13minor injury crashes13.8%
↑44.4%prior 9
Possible Injury12possible injury crashes12.8%
→0.0%prior 12
No Injury59no injury crashes62.8%
↓-6.3%prior 63

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

The primary contributing factors for crashes changed notably between periods. 'Failure to yield at an uncontrolled intersection' became the leading cause in 2019 with 14 crashes, a seven-fold increase from only 2 such incidents in 2018. Conversely, 'Lost Control,' which was the top factor in 2018 with 16 crashes, decreased in count to 11 crashes in 2019. Crashes attributed to 'Driving too fast for conditions' also fell from 7 in 2018 to 3 in 2019.

Officer-Reported Primary Contributing Cause

FTYROW: At uncontrolled intersection14 (14.9%)
Other (explain in narrative): Other11 (11.7%)
Lost Control11 (11.7%)↓-31.3%prior 16
Operating vehicle in an reckless, erratic, careless, negligent manner8 (8.5%)
Animal4 (4.3%)↓-42.9%prior 7
Followed too close3 (3.2%)
Driver Distraction: Manual operation of an electronic communication device3 (3.2%)
Driver Distraction: Other interior distraction3 (3.2%)
Driver Distraction: Reaching for object(s)/fallen object(s)3 (3.2%)
Driving too fast for conditions3 (3.2%)↓-57.1%prior 7

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 in daylight and clear weather remained consistent year-over-year, accounting for the majority of incidents in both periods. However, there was a significant shift regarding road surface conditions. The number of crashes on snowy roads more than doubled, increasing from 7 in 2018 to 16 in 2019, with their share of total crashes rising from 7.8% to 17.0%.

Weather

Clear58 (64.4%)
↑13.7%prior 51
Cloudy18 (20.0%)
↓-10.0%prior 20
Snow5 (5.6%)
Rain4 (4.4%)
Blowing Snow2 (2.2%)
Fog, smoke, smog2 (2.2%)
Freezing rain/drizzle1 (1.1%)

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

Lighting

Daylight67 (73.6%)
↑6.3%prior 63
Dark - roadway not lighted15 (16.5%)
↑15.4%prior 13
Dark - roadway lighted6 (6.6%)
Dark - unknown roadway lighting2 (2.2%)
Dusk1 (1.1%)

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

Road Surface

Dry58 (64.4%)
↑11.5%prior 52
Snow16 (17.8%)
↑128.6%prior 7
Wet7 (7.8%)
→0.0%prior 7
Ice/frost4 (4.4%)
↓-50.0%prior 8
Slush2 (2.2%)
Gravel2 (2.2%)
↓-66.7%prior 6
Mud, dirt1 (1.1%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes were largely consistent, with Chevrolet, Ford, and Dodge vehicles being the most frequent in both 2018 and 2019. An analysis of persons involved shows that the 16-20 age group was highly represented in both years, with 33 individuals in 2019 and 35 in 2018. The number of people aged 26-34 involved in crashes saw a notable increase, rising from 17 individuals in 2018 to 29 in 2019.

Top Vehicle Makes (152 vehicles)

1
CHEV30 (19.7%)
↑3.4%prior 29
2
FORD19 (12.5%)
↓-17.4%prior 23
3
CHEVROLET19 (12.5%)
↑72.7%prior 11
4
DODG9 (5.9%)
↑12.5%prior 8
5
CHRY5 (3.3%)
6
DODGE4 (2.6%)
7
FREIGHTLINER4 (2.6%)
8
BUICK3 (2%)
9
NISS3 (2%)
10
NR3 (2%)

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

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

Sex Distribution (138 persons with recorded sex)

Male84 (60.9%)
↑13.5%prior 74
Female54 (39.1%)
↑35.0%prior 40

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: 94
  • Total persons involved: 215
  • Total vehicles involved: 152

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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