Yearly Traffic Safety Analysis

196 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Louisa County recorded 196 total crashes, an increase of 14.0% from the 172 crashes documented in 2015. This rise in collisions was accompanied by a notable increase in persons injured, which grew from 43 in the prior year to 61 in the current period. Fatalities also increased, from one in 2015 to two in 2016.

196

▲ 14.0%was 172

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

61

▲ 41.9%was 43

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 · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Louisa County indicates a rising trend in collisions year-over-year. Total crashes increased by 14.0%, from 172 in 2015 to 196 in 2016. The number of people injured in these incidents also rose significantly, increasing by 41.9% from 43 to 61, while fatalities doubled from one to two.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 1↑100.0%

0

Other Killed

Prior: 0→0.0%

1

Cyclists Injured

Prior: 0→%

59

Motorists Injured

Prior: 43↑37.2%

1

Other Injured

Prior: 0→%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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 2015 and 2016. The peak day for collisions moved from Thursday, with 31 crashes in 2015, to Saturday, with 41 crashes in 2016. Similarly, the peak hour for crashes changed from the morning at 6 AM (19 crashes) in the prior year to the evening at 9 PM (21 crashes) in the current year.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased in 2016 compared to the previous year. The number of fatal crashes doubled from one to two, and the fatal crash rate rose from 0.58 to 1.02 per 100 crashes. The proportion of crashes resulting in any level of injury (serious, minor, or possible) also increased, accounting for 25.0% of all crashes in 2016, up from 21.5% in 2015. Consequently, the share of crashes with no injuries decreased from 77.9% in 2015 to 74.0% in 2016.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1%
↑100.0%prior 1
Serious Injury9serious injury crashes4.6%
↑28.6%prior 7
Minor Injury18minor injury crashes9.2%
↑28.6%prior 14
Possible Injury22possible injury crashes11.2%
↑37.5%prior 16
No Injury145no injury crashes74%
↑8.2%prior 134

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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 such incidents decreased by 17.6% from 102 in 2015 to 84 in 2016. 'Lost Control' was the second-most cited factor in both periods, with its count increasing by 40% from 15 to 21 crashes. Other factors with notable increases in count included 'Ran off road - straight,' which rose from 7 to 12 incidents, and 'Operating vehicle in a reckless, erratic, careless, negligent manner,' which increased from 1 to 6 incidents.

Officer-Reported Primary Contributing Cause

Animal84 (42.9%)↓-17.6%prior 102
Lost Control21 (10.7%)↑40.0%prior 15
Ran off road - straight12 (6.1%)↑71.4%prior 7
Driving too fast for conditions8 (4.1%)↑14.3%prior 7
Ran off road - left7 (3.6%)
FTYROW: From stop sign6 (3.1%)↑20.0%prior 5
Driver Distraction: Other interior distraction6 (3.1%)
Operating vehicle in an reckless, erratic, careless, negligent manner6 (3.1%)
Driver Distraction: Exterior distraction5 (2.6%)
FTYROW: Making left turn5 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes across lighting and road surface conditions remained relatively stable year-over-year, with crashes on dry roads accounting for approximately 76% of incidents in 2016 versus 74% in 2015. However, there was a shift in weather conditions, as the proportion of crashes occurring in clear weather increased from 59.1% in 2015 to 68.7% in 2016. Correspondingly, the share of crashes in adverse weather conditions like clouds, snow, or rain decreased.

Weather

Clear90 (68.7%)
↑73.1%prior 52
Cloudy31 (23.7%)
↑24.0%prior 25
Snow3 (2.3%)
↓-40.0%prior 5
Freezing rain/drizzle3 (2.3%)
Fog, smoke, smog2 (1.5%)
Rain1 (0.8%)
Severe Winds1 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight77 (57.0%)
↑42.6%prior 54
Dark - roadway not lighted42 (31.1%)
↑50.0%prior 28
Dark - roadway lighted10 (7.4%)
Dawn3 (2.2%)
↓-50.0%prior 6
Dusk3 (2.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry101 (75.9%)
↑46.4%prior 69
Wet9 (6.8%)
↑12.5%prior 8
Gravel9 (6.8%)
↑50.0%prior 6
Ice/frost7 (5.3%)
↑40.0%prior 5
Snow6 (4.5%)
Slush1 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet remained the two most common vehicle makes involved in crashes in both periods, with the count for both increasing in 2016. An analysis of persons involved shows shifts in age demographics year-over-year. The proportion of individuals in the 45-54 age group grew, representing 18.9% of those involved in 2016 compared to 15.4% in 2015. Conversely, the share of persons in the 26-34 age group decreased from 19.2% in 2015 to 11.6% in 2016.

Top Vehicle Makes (247 vehicles)

1
FORD35 (14.2%)
↑12.9%prior 31
2
CHEVROLET34 (13.8%)
↑47.8%prior 23
3
CHEV26 (10.5%)
↑85.7%prior 14
4
DODGE13 (5.3%)
↓-7.1%prior 14
5
TOYT11 (4.5%)
6
DODG11 (4.5%)
↓-8.3%prior 12
7
TOYOTA10 (4%)
↑25.0%prior 8
8
HONDA9 (3.6%)
9
CHRYSLER9 (3.6%)
↓-18.2%prior 11
10
GMC9 (3.6%)
↑80.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (170 persons with recorded sex)

Male104 (61.2%)
↑7.2%prior 97
Female66 (38.8%)
↓-27.5%prior 91

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 196
  • Total persons involved: 299
  • Total vehicles involved: 247

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: 2016." Published September 9, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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