Yearly Traffic Safety Analysis

198 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Louisa County, total crashes decreased from 214 in 2018 to 198 in 2019, a 7.5% reduction. The most significant year-over-year change was the elimination of traffic fatalities, which dropped from two in the prior period to zero in the current period. Overall injuries also saw a slight decline from 36 to 33.

198

▼ -7.5%was 214

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

33

▼ -8.3%was 36

Persons Injured

0

▼ -100.0%was 2

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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 Louisa County indicates a downward trend year-over-year. The total number of crashes fell by 7.5%, from 214 in 2018 to 198 in 2019. Similarly, the number of people injured in these incidents decreased from 36 to 33.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 1↓-100.0%

33

Motorists Injured

Prior: 36↓-8.3%

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 in Louisa County shifted between 2018 and 2019. The peak day for incidents moved from a tie between Wednesday and Friday (40 crashes each) in 2018 to Thursday (39 crashes) in 2019. A more pronounced change occurred in the peak hour, which shifted from the evening commute at 6 p.m. in the prior year (18 crashes) to the morning at 6 a.m. in the current year (20 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 improved notably, with fatal crashes decreasing from two in 2018 to zero in 2019. The total number of injuries also saw a slight decline from 36 to 33. However, the number of crashes involving serious injuries increased from 3 in the prior year to 5 in the current year, representing a proportional rise from 1.4% to 2.5% of all crashes.

Outcome by Severity (Crash Events)

Serious Injury5serious injury crashes2.5%
↑66.7%prior 3
Minor Injury11minor injury crashes5.6%
↓-8.3%prior 12
Possible Injury11possible injury crashes5.6%
↓-38.9%prior 18
No Injury171no injury crashes86.4%
↓-4.5%prior 179

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 with animals remained the top contributing factor in both periods, though the count decreased by 19.4% from 124 crashes in 2018 to 100 in 2019. 'Lost Control' became the second-ranked factor, with its crash count increasing by 53.3% from 15 to 23 incidents. 'Driving too fast for conditions' also saw a notable increase in count, rising from 7 crashes to 11 crashes year-over-year.

Officer-Reported Primary Contributing Cause

Animal100 (50.5%)↓-19.4%prior 124
Lost Control23 (11.6%)↑53.3%prior 15
Driving too fast for conditions11 (5.6%)↑57.1%prior 7
Ran off road - left8 (4%)↑33.3%prior 6
Driver Distraction: Other interior distraction6 (3%)↑20.0%prior 5
Ran off road - straight6 (3%)↓-40.0%prior 10
Followed too close6 (3%)
FTYROW: From stop sign5 (2.5%)→0.0%prior 5
Other (explain in narrative): Other4 (2%)↓-66.7%prior 12
Driver Distraction: Exterior distraction4 (2%)

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 during crashes showed some shifts between 2018 and 2019. While crashes on dry roads decreased from 90 to 82, incidents on adverse road surfaces increased. Crashes on snowy roads rose from 7 to 12, and those on icy or frosty roads more than tripled from 3 to 10. Similarly, crashes reported during snowy weather increased from 3 to 8 incidents.

Weather

Clear82 (66.7%)
↑9.3%prior 75
Cloudy24 (19.5%)
↓-25.0%prior 32
Snow8 (6.5%)
Blowing Snow5 (4.1%)
Freezing rain/drizzle4 (3.3%)

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

Lighting

Daylight86 (69.4%)
↑10.3%prior 78
Dark - roadway not lighted21 (16.9%)
↓-16.0%prior 25
Dawn9 (7.3%)
↓-25.0%prior 12
Dusk5 (4.0%)
Dark - roadway lighted3 (2.4%)

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

Road Surface

Dry82 (66.1%)
↓-8.9%prior 90
Wet13 (10.5%)
↓-23.5%prior 17
Snow12 (9.7%)
↑71.4%prior 7
Ice/frost10 (8.1%)
Gravel6 (4.8%)
→0.0%prior 6
Slush1 (0.8%)

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

Vehicles & Demographics

Ford and Chevrolet were the two most common vehicle makes involved in crashes in both years, though their counts decreased from 47 and 46 respectively in 2018 to 37 each in 2019. The demographic profile of persons involved in crashes also shifted, with the 26-34 age group becoming the largest cohort in 2019, accounting for 70 individuals, up from 51 in the previous year. The 35-44 age group, which was the largest in 2018, remained stable with 63 individuals involved in both periods.

Top Vehicle Makes (252 vehicles)

1
FORD37 (14.7%)
↓-21.3%prior 47
2
CHEV37 (14.7%)
↓-19.6%prior 46
3
TOYT18 (7.1%)
↑12.5%prior 16
4
DODG17 (6.7%)
↓-15.0%prior 20
5
CHRY12 (4.8%)
↓-14.3%prior 14
6
HOND10 (4%)
7
GMC10 (4%)
↑25.0%prior 8
8
CHEVROLET8 (3.2%)
↑60.0%prior 5
9
NISS7 (2.8%)
↑16.7%prior 6
10
FREIGHTLINER6 (2.4%)

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

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

Sex Distribution (240 persons with recorded sex)

Male157 (65.4%)
↑41.4%prior 111
Female83 (34.6%)
↑3.8%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: 198
  • Total persons involved: 364
  • Total vehicles involved: 252

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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