Yearly Traffic Safety Analysis

277 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Floyd County recorded 277 total crashes, an increase of 7.4% from the 258 crashes reported in 2018. While total crashes and injuries rose, the number of fatalities decreased significantly, from 5 in 2018 to 1 in 2019. The most common contributing factor in both years was collisions with animals, though the count of such incidents fell from 95 to 74.

277

▲ 7.4%was 258

Total Crash Events

1

▼ -80.0%was 5

Persons Killed

71

▲ 14.5%was 62

Persons Injured

1

▼ -66.7%was 3

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

Overall, total crashes in Floyd County increased by 7.4% from 2018 to 2019, rising from 258 to 277 incidents. The number of people injured also rose from 62 to 71. In contrast, the number of fatalities saw a substantial decrease, dropping from 5 in the prior year to 1 in the current year.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 5↓-80.0%

1

Cyclists Injured

Prior: 1→0.0%

70

Motorists Injured

Prior: 61↑14.8%

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 shifted between the two years. In 2019, the peak day for crashes was Tuesday with 49 incidents, compared to Wednesday in 2018 which had 50 crashes. The peak hour for collisions also changed, moving earlier from 7 p.m. in 2018 (21 crashes) to 4 p.m. in 2019 (19 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

While total crashes increased, the severity profile showed a mixed trend year-over-year. The number of fatal crashes decreased from 3 in 2018 to 1 in 2019, and the fatal crash rate dropped from 1.16% to 0.36%. Conversely, the count of serious injury crashes doubled from 3 to 6. Crashes resulting in no injury remained the largest category, accounting for approximately 80% of all incidents in both periods.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
↓-66.7%prior 3
Serious Injury6serious injury crashes2.2%
↑100.0%prior 3
Minor Injury15minor injury crashes5.4%
↓-21.1%prior 19
Possible Injury33possible injury crashes11.9%
↑17.9%prior 28
No Injury222no injury crashes80.1%
↑8.3%prior 205

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 top contributing factor in both years, though the count of these incidents decreased from 95 in 2018 to 74 in 2019. Several other factors saw an increase in crash counts, including 'Driving too fast for conditions' (from 18 to 25 crashes) and 'FTYROW: From stop sign' (from 13 to 18 crashes). Notably, crashes attributed to 'Ran Stop Sign' more than doubled, increasing from 7 incidents in 2018 to 15 in 2019.

Officer-Reported Primary Contributing Cause

Animal74 (26.7%)↓-22.1%prior 95
Driving too fast for conditions25 (9%)↑38.9%prior 18
FTYROW: From stop sign18 (6.5%)↑38.5%prior 13
Ran off road - left16 (5.8%)↑45.5%prior 11
Ran Stop Sign15 (5.4%)↑114.3%prior 7
FTYROW: At uncontrolled intersection15 (5.4%)↑50.0%prior 10
Lost Control13 (4.7%)↑85.7%prior 7
Followed too close11 (4%)→0.0%prior 11
Ran off road - straight11 (4%)↑37.5%prior 8
FTYROW: Making left turn11 (4%)↑10.0%prior 10

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 majority of crashes in both years occurred in clear weather and during daylight hours. However, the number of crashes on roads with snow or ice/frost increased from a combined 39 in 2018 to 59 in 2019. Crashes occurring in dark, lighted roadway conditions also doubled, rising from 14 incidents in 2018 to 28 in 2019.

Weather

Clear144 (67.9%)
↑19.0%prior 121
Cloudy31 (14.6%)
↑10.7%prior 28
Snow13 (6.1%)
↑62.5%prior 8
Blowing Snow8 (3.8%)
↑14.3%prior 7
Rain7 (3.3%)
Freezing rain/drizzle6 (2.8%)
↑20.0%prior 5
Fog, smoke, smog2 (0.9%)
Severe Winds1 (0.5%)

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

Lighting

Daylight149 (70.3%)
↑15.5%prior 129
Dark - roadway not lighted31 (14.6%)
↑14.8%prior 27
Dark - roadway lighted28 (13.2%)
↑100.0%prior 14
Dawn3 (1.4%)
↓-40.0%prior 5
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry129 (61.1%)
↑16.2%prior 111
Snow31 (14.7%)
↑40.9%prior 22
Ice/frost28 (13.3%)
↑64.7%prior 17
Wet17 (8.1%)
↑6.3%prior 16
Gravel4 (1.9%)
↓-20.0%prior 5
Slush2 (0.9%)
↓-71.4%prior 7

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

Vehicles & Demographics

The total number of vehicles involved in crashes rose from 390 in 2018 to 438 in 2019. Chevrolet and Ford were the top two vehicle makes involved in collisions in both years, with both seeing an increase in counts. When analyzing the age of persons involved, there was a notable increase in the 26-34 age group (from 73 to 100 people) and the 55-64 age group (from 75 to 97 people).

Top Vehicle Makes (438 vehicles)

1
FORD79 (18%)
↑31.7%prior 60
2
CHEV73 (16.7%)
↑19.7%prior 61
3
CHEVROLET29 (6.6%)
↑11.5%prior 26
4
DODG29 (6.6%)
↑20.8%prior 24
5
CHRY18 (4.1%)
↑38.5%prior 13
6
JEEP17 (3.9%)
↑70.0%prior 10
7
TOYT13 (3%)
↓-7.1%prior 14
8
DODGE12 (2.7%)
↓-7.7%prior 13
9
CHRYSLER12 (2.7%)
↑140.0%prior 5
10
HOND10 (2.3%)
↑42.9%prior 7

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

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

Sex Distribution (397 persons with recorded sex)

Male231 (58.2%)
↑17.3%prior 197
Female166 (41.8%)
↑59.6%prior 104

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: 277
  • Total persons involved: 602
  • Total vehicles involved: 438

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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