Yearly Traffic Safety Analysis

182 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Allamakee County recorded 182 total crashes, a 13% increase from the 161 crashes reported in 2018. This period also saw a rise in total injuries from 53 to 62 and an increase in fatalities from one to two. The most notable year-over-year shift was the overall increase in crash volume.

182

▲ 13.0%was 161

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

62

▲ 17.0%was 53

Persons Injured

2

▲ 100.0%was 1

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

Crash data for Allamakee County indicates an upward trend in 2019 compared to the prior year. Total crashes increased by 13%, from 161 in 2018 to 182 in 2019. Similarly, the number of persons injured rose by 17% from 53 to 62, and fatalities increased from one to two.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 1↑100.0%

2

Pedestrians Injured

Prior: 1↑100.0%

60

Motorists Injured

Prior: 52↑15.4%

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 periods. In 2019, the peak day for collisions was Wednesday with 41 incidents, a change from Tuesday (33 incidents) in 2018. The peak hour for crashes also moved from the 5 p.m. hour in 2018 (14 crashes) to the 12 p.m. hour in 2019 (16 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

Crash severity increased in 2019 compared to the previous year. The number of fatal crashes doubled from one to two, and the fatal crash rate rose from 0.6% to 1.1% of all crashes. The proportion of crashes involving any level of injury grew from 23.0% in 2018 to 27.5% in 2019, while the share of crashes with no reported injuries decreased from 76.4% to 71.4%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.1%
↑100.0%prior 1
Serious Injury6serious injury crashes3.3%
→0.0%prior 6
Minor Injury18minor injury crashes9.9%
↑38.5%prior 13
Possible Injury26possible injury crashes14.3%
↑44.4%prior 18
No Injury130no injury crashes71.4%
↑5.7%prior 123

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 the count increasing by 30% from 43 incidents in 2018 to 56 in 2019. 'Lost Control' was the second-most cited factor in 2019 with 19 incidents, a decrease from 22 incidents in the prior year. 'Driving too fast for conditions' saw a 25% increase in count from 12 to 15 incidents, becoming the third-most common factor in 2019.

Officer-Reported Primary Contributing Cause

Animal56 (30.8%)↑30.2%prior 43
Lost Control19 (10.4%)↓-13.6%prior 22
Driving too fast for conditions15 (8.2%)↑25.0%prior 12
FTYROW: From stop sign13 (7.1%)→0.0%prior 13
Ran off road - straight11 (6%)→0.0%prior 11
Other (explain in narrative): Other7 (3.8%)↓-22.2%prior 9
Ran off road - left6 (3.3%)↓-25.0%prior 8
Ran Stop Sign6 (3.3%)
Driver Distraction: Other interior distraction5 (2.7%)
FTYROW: Making left turn3 (1.6%)

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 distribution of crashes across environmental conditions remained largely consistent between 2018 and 2019. Crashes in clear weather accounted for 58% of incidents in 2019, compared to 57% in 2018. Similarly, collisions on dry road surfaces made up 56% of the total in both years. The proportion of crashes occurring in daylight was also stable, at 54% in 2019 versus 57% in 2018, indicating no significant shift in how conditions contributed to crashes.

Weather

Clear106 (65.4%)
↑15.2%prior 92
Cloudy27 (16.7%)
↓-6.9%prior 29
Rain11 (6.8%)
↑83.3%prior 6
Snow9 (5.6%)
↑12.5%prior 8
Freezing rain/drizzle4 (2.5%)
↓-33.3%prior 6
Fog, smoke, smog2 (1.2%)
Other (explain in narrative)1 (0.6%)
Sleet, hail1 (0.6%)
Blowing Snow1 (0.6%)

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

Lighting

Daylight99 (61.5%)
↑8.8%prior 91
Dark - roadway not lighted37 (23.0%)
→0.0%prior 37
Dark - roadway lighted12 (7.5%)
↑71.4%prior 7
Dusk10 (6.2%)
Dawn3 (1.9%)
↓-57.1%prior 7

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

Road Surface

Dry102 (63.0%)
↑12.1%prior 91
Snow19 (11.7%)
↑35.7%prior 14
Wet18 (11.1%)
↑5.9%prior 17
Ice/frost14 (8.6%)
↓-12.5%prior 16
Gravel6 (3.7%)
↑20.0%prior 5
Slush2 (1.2%)
Mud, dirt1 (0.6%)

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 showed consistency year-over-year, with Ford and Chevrolet remaining the two most common makes in both 2018 and 2019. Ford-made vehicles were involved in 51 crashes in 2019, up from 49, while Chevrolet vehicles were involved in 62 crashes, a slight increase from 61. Analysis of persons involved shows a shift in age demographics; the 16-20 age group's involvement decreased from representing 14.8% of persons in 2018 to 9.1% in 2019, while the 65+ age group's involvement increased from 48 to 66 individuals.

Top Vehicle Makes (246 vehicles)

1
FORD51 (20.7%)
↑4.1%prior 49
2
CHEV35 (14.2%)
↓-14.6%prior 41
3
CHEVROLET27 (11%)
↑35.0%prior 20
4
DODG13 (5.3%)
→0.0%prior 13
5
GMC11 (4.5%)
↓-8.3%prior 12
6
DODGE11 (4.5%)
↑57.1%prior 7
7
JEEP10 (4.1%)
↑25.0%prior 8
8
BUIC7 (2.8%)
9
HOND6 (2.4%)
10
HONDA5 (2%)

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

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

Sex Distribution (229 persons with recorded sex)

Male139 (60.7%)
↑20.9%prior 115
Female90 (39.3%)
↑23.3%prior 73

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: 182
  • Total persons involved: 350
  • Total vehicles involved: 246

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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