Yearly Traffic Safety Analysis

664 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Lee County, total vehicle crashes increased by 9.4%, rising from 607 in 2018 to 664 in 2019. This period saw a corresponding rise in both fatalities, from 4 to 5, and injuries, from 141 to 154. The most significant shift was a 61.5% increase in non-collision, single-vehicle crashes, which grew from 157 incidents in the prior year to 260 in the current year.

664

▲ 9.4%was 607

Total Crash Events

5

▲ 25.0%was 4

Persons Killed

154

▲ 9.2%was 141

Persons Injured

4

▲ 33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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

Traffic crash data for Lee County indicates a rising trend year-over-year. The total number of crashes increased from 607 to 664 (+9.4%). Similarly, the number of persons injured rose from 141 to 154 (+9.2%), and fatalities increased from 4 to 5 (+25.0%).

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Cyclists Killed

Prior: 0→%

4

Motorists Killed

Prior: 4→0.0%

2

Pedestrians Injured

Prior: 3↓-33.3%

0

Cyclists Injured

Prior: 5↓-100.0%

152

Motorists Injured

Prior: 133↑14.3%

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 temporal patterns of crashes shifted between the two periods. The peak day for crashes moved from Thursday (96 incidents) in 2018 to Friday (117 incidents) in 2019. The peak hour also shifted earlier, from 8 p.m. in the prior year (45 crashes) to the 5 p.m. rush hour in the current year (49 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

The severity of crashes worsened slightly year-over-year. The number of fatal crashes increased from 3 to 4, and the number of fatalities rose from 4 to 5. Crashes resulting in serious injuries saw a notable increase, rising from 13 incidents (2.1% of total) in 2018 to 21 incidents (3.2% of total) in 2019.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.6%
↑33.3%prior 3
Serious Injury21serious injury crashes3.2%
↑61.5%prior 13
Minor Injury45minor injury crashes6.8%
↑4.7%prior 43
Possible Injury65possible injury crashes9.8%
↑12.1%prior 58
No Injury529no injury crashes79.7%
↑8.0%prior 490

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 leading contributing factor in both years, with the count increasing from 270 in 2018 to 296 in 2019. The number of crashes attributed to losing control rose by 25.0%, from 36 to 45 incidents, making it the second-most cited factor in 2019. Conversely, crashes involving failure to yield from a stop sign decreased in count from 35 to 25.

Officer-Reported Primary Contributing Cause

Animal296 (44.6%)↑9.6%prior 270
Lost Control45 (6.8%)↑25.0%prior 36
Other (explain in narrative): Other34 (5.1%)↑36.0%prior 25
Ran off road - straight33 (5%)↑57.1%prior 21
Followed too close31 (4.7%)↑10.7%prior 28
FTYROW: From stop sign25 (3.8%)↓-28.6%prior 35
Ran off road - left18 (2.7%)↓-10.0%prior 20
Driving too fast for conditions14 (2.1%)↓-17.6%prior 17
FTYROW: Making left turn13 (2%)→0.0%prior 13
Ran Stop Sign12 (1.8%)→0.0%prior 12

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While daylight remained the most common lighting condition with an identical count of 231 crashes in both years, incidents in darkness saw a notable increase. Crashes on unlit dark roadways rose from 60 to 82, and those on lit dark roadways increased from 33 to 43. The distribution of crashes by weather and road surface conditions remained largely stable, with clear weather and dry roads accounting for the majority of incidents in both periods.

Weather

Clear233 (59.9%)
↑1.3%prior 230
Cloudy87 (22.4%)
↑40.3%prior 62
Rain27 (6.9%)
↑12.5%prior 24
Snow17 (4.4%)
↑41.7%prior 12
Freezing rain/drizzle14 (3.6%)
↑55.6%prior 9
Blowing Snow4 (1.0%)
Fog, smoke, smog3 (0.8%)
Severe Winds2 (0.5%)
Other (explain in narrative)1 (0.3%)
Blowing sand, soil, dirt1 (0.3%)

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

Lighting

Daylight231 (59.1%)
→0.0%prior 231
Dark - roadway not lighted82 (21.0%)
↑36.7%prior 60
Dark - roadway lighted43 (11.0%)
↑30.3%prior 33
Dawn17 (4.3%)
↑41.7%prior 12
Dusk12 (3.1%)
↑100.0%prior 6
Dark - unknown roadway lighting6 (1.5%)
↓-25.0%prior 8

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

Road Surface

Dry275 (70.3%)
↑8.7%prior 253
Wet45 (11.5%)
↓-8.2%prior 49
Snow30 (7.7%)
↑87.5%prior 16
Ice/frost26 (6.6%)
↑23.8%prior 21
Gravel9 (2.3%)
↑80.0%prior 5
Slush6 (1.5%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes, led by Ford and Chevrolet, remained consistent in ranking and volume between 2018 and 2019. However, the age distribution of persons involved in crashes showed significant changes. The number of individuals in the 16-20 age group increased from 101 to 145, and the 26-34 age group saw a substantial rise from 148 to 234 persons involved.

Top Vehicle Makes (924 vehicles)

1
FORD173 (18.7%)
↑1.2%prior 171
2
CHEV108 (11.7%)
↑27.1%prior 85
3
CHEVROLET83 (9%)
↓-4.6%prior 87
4
GMC47 (5.1%)
↑34.3%prior 35
5
DODG46 (5%)
↑35.3%prior 34
6
DODGE44 (4.8%)
↓-8.3%prior 48
7
KIA34 (3.7%)
↑25.9%prior 27
8
JEEP31 (3.4%)
↑19.2%prior 26
9
TOYOTA23 (2.5%)
↓-30.3%prior 33
10
CHRY23 (2.5%)
↑15.0%prior 20

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

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

Sex Distribution (845 persons with recorded sex)

Male487 (57.6%)
↑43.2%prior 340
Female358 (42.4%)
↑46.7%prior 244

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: 664
  • Total persons involved: 1,412
  • Total vehicles involved: 924

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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