Yearly Traffic Safety Analysis

206 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Louisa County, total crashes increased from 198 in 2019 to 206 in 2020, a rise of approximately 4%. While overall crash volume remained relatively stable, the most significant year-over-year change was the occurrence of 2 fatalities in 2020, whereas none were recorded in the prior year. Total injuries also increased from 33 to 45 during this period.

206

▲ 4.0%was 198

Total Crash Events

2

Persons Killed

45

▲ 36.4%was 33

Persons Injured

2

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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Louisa County showed a slight increase year-over-year, with total incidents rising from 198 in 2019 to 206 in 2020. This upward trend was more pronounced in terms of human impact, as total injuries grew by 36.4% from 33 to 45, and fatalities increased from zero to two.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 0→%

45

Motorists Injured

Prior: 33↑36.4%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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. The peak day for crashes moved from Thursday, with 39 incidents in 2019, to Sunday, with 35 incidents in 2020. Similarly, the peak hour for crashes shifted slightly earlier, moving from 6 a.m. in 2019 (20 crashes) to 5 a.m. in 2020 (19 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened in 2020 compared to the previous year. The county recorded 2 fatal crashes, representing 1% of all incidents, up from zero fatal crashes in 2019. The proportion of crashes resulting in possible injury increased from a 5.6% share (11 crashes) in 2019 to a 9.7% share (20 crashes) in 2020. Consequently, the share of no-injury crashes decreased from 86.4% to 81.1% of all incidents.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1%
Serious Injury6serious injury crashes2.9%
↑20.0%prior 5
Minor Injury11minor injury crashes5.3%
→0.0%prior 11
Possible Injury20possible injury crashes9.7%
↑81.8%prior 11
No Injury167no injury crashes81.1%
↓-2.3%prior 171

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both years, with the count increasing by 11% from 100 incidents in 2019 to 111 in 2020. 'Lost Control' was the second-most cited factor in both periods, though its count decreased from 23 to 19. Notably, crashes attributed to 'Driving too fast for conditions' fell from 11 in 2019 to 2 in 2020, while incidents involving 'Ran off road - straight' increased from 6 to 11.

Officer-Reported Primary Contributing Cause

Animal111 (53.9%)↑11.0%prior 100
Lost Control19 (9.2%)↓-17.4%prior 23
Ran off road - straight11 (5.3%)↑83.3%prior 6
Followed too close7 (3.4%)↑16.7%prior 6
FTYROW: From stop sign7 (3.4%)↑40.0%prior 5
Ran off road - left6 (2.9%)↓-25.0%prior 8
Driver Distraction: Other interior distraction6 (2.9%)→0.0%prior 6
Other (explain in narrative): Other5 (2.4%)
Failed to yield to emergency vehicle3 (1.5%)
Ran Stop Sign3 (1.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with most incidents in both 2020 and 2019 occurring in clear weather and on dry roads. However, there was a notable decrease in crashes related to winter weather, with incidents on snow or ice-covered roads falling from a combined 22 in 2019 to 12 in 2020. Crashes occurring in dark, unlighted conditions increased from 21 to 27.

Weather

Clear81 (68.1%)
↓-1.2%prior 82
Cloudy27 (22.7%)
↑12.5%prior 24
Rain4 (3.4%)
Freezing rain/drizzle3 (2.5%)
Severe Winds2 (1.7%)
Snow2 (1.7%)
↓-75.0%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight73 (60.3%)
↓-15.1%prior 86
Dark - roadway not lighted27 (22.3%)
↑28.6%prior 21
Dark - roadway lighted10 (8.3%)
Dawn9 (7.4%)
→0.0%prior 9
Dark - unknown roadway lighting2 (1.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry85 (70.8%)
↑3.7%prior 82
Wet15 (12.5%)
↑15.4%prior 13
Ice/frost10 (8.3%)
→0.0%prior 10
Gravel7 (5.8%)
↑16.7%prior 6
Snow2 (1.7%)
↓-83.3%prior 12
Slush1 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet were the two most common vehicle makes involved in crashes in both periods, with the count for Ford increasing from 37 to 54 year-over-year. The demographics of persons involved in crashes also shifted, with the number of individuals in the 35-44 age group increasing from 63 to 86. Conversely, the 26-34 age group saw a decrease in involvement, from 70 persons in 2019 to 55 in 2020.

Top Vehicle Makes (259 vehicles)

1
FORD54 (20.8%)
↑45.9%prior 37
2
CHEV41 (15.8%)
↑10.8%prior 37
3
DODG12 (4.6%)
↓-29.4%prior 17
4
JEEP12 (4.6%)
5
TOYT12 (4.6%)
↓-33.3%prior 18
6
CHRY11 (4.2%)
↓-8.3%prior 12
7
GMC10 (3.9%)
→0.0%prior 10
8
CHEVROLET10 (3.9%)
↑25.0%prior 8
9
PONT8 (3.1%)
↑33.3%prior 6
10
NISS7 (2.7%)
→0.0%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

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

Sex Distribution (243 persons with recorded sex)

Male155 (63.8%)
↓-1.3%prior 157
Female88 (36.2%)
↑6.0%prior 83

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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: 2020-01-01 through 2020-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 206
  • Total persons involved: 406
  • Total vehicles involved: 259

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: 2020." Published September 9, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2020-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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