Yearly Traffic Safety Analysis

629 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Lee County recorded 629 total crashes, an 18.0% decrease from the 767 crashes reported in 2015. Despite this overall reduction in collisions, the number of fatal crashes increased slightly from 10 to 11. The most notable shift was in crash severity, as the count of serious injury crashes more than doubled from 11 in 2015 to 28 in 2016.

629

▼ -18.0%was 767

Total Crash Events

11

Persons Killed

214

▼ -13.0%was 246

Persons Injured

11

▲ 10.0%was 10

Fatal Crash Events

Note: "Persons Killed" (11) counts individual fatalities across all crash events. "Fatal" in the severity table below (11) 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

Overall, traffic crashes in Lee County saw a downward trend from 2015 to 2016. The total number of crashes decreased by 18.0%, from 767 to 629. Similarly, the number of people injured in these incidents fell by 13.0% from 246 to 214, while the number of fatalities remained unchanged at 11 for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Cyclists Killed

Prior: 0→%

10

Motorists Killed

Prior: 11↓-9.1%

2

Pedestrians Injured

Prior: 5↓-60.0%

2

Cyclists Injured

Prior: 4↓-50.0%

210

Motorists Injured

Prior: 237↓-11.4%

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 temporal patterns of crashes in Lee County remained largely consistent between 2015 and 2016. Friday was the peak day for crashes in 2016 with 120 incidents, similar to the prior year where Friday tied for the highest volume with 121 incidents. The 5 p.m. hour was the peak time for crashes in both periods, although the number of crashes during this hour decreased from 62 to 43 year-over-year. November was the month with the most crashes in both 2016 (80 crashes) and 2015 (95 crashes).

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

While total crashes declined, the severity of crashes generally increased from 2015 to 2016. The fatal crash rate rose from 1.3% to 1.75%, with the count of fatal crashes increasing from 10 to 11. Most notably, the count of serious injury crashes more than doubled, jumping from 11 in 2015 to 28 in 2016, which raised its share of all crashes from 1.4% to 4.5%. Conversely, crashes involving possible injuries saw a decrease in both count, from 112 to 78, and proportion, from 14.6% to 12.4%.

Outcome by Severity (Crash Events)

Fatal11fatal crashes1.7%
↑10.0%prior 10
Serious Injury28serious injury crashes4.5%
↑154.5%prior 11
Minor Injury60minor injury crashes9.5%
↓-13.0%prior 69
Possible Injury78possible injury crashes12.4%
↓-30.4%prior 112
No Injury452no injury crashes71.9%
↓-20.0%prior 565

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 with an animal remained the leading contributing factor in both periods, with an identical count of 221 crashes in 2016 and 2015; however, its share of total crashes increased from 28.8% to 35.1%. Incidents attributed to 'Followed too close' saw a notable decrease, falling from 53 crashes in 2015 to 24 in 2016. In contrast, crashes involving 'Failure to Yield Right of Way: From stop sign' increased from 34 to 38 incidents. 'Lost Control' remained a top factor, though its count decreased slightly from 57 to 52 crashes.

Officer-Reported Primary Contributing Cause

Animal221 (35.1%)→0.0%prior 221
Lost Control52 (8.3%)↓-8.8%prior 57
FTYROW: From stop sign38 (6%)↑11.8%prior 34
Other (explain in narrative): Other37 (5.9%)↑5.7%prior 35
Ran off road - straight31 (4.9%)↑6.9%prior 29
Followed too close24 (3.8%)↓-54.7%prior 53
Driving too fast for conditions23 (3.7%)↓-34.3%prior 35
Ran off road - left23 (3.7%)↓-43.9%prior 41
FTYROW: Making left turn20 (3.2%)↓-13.0%prior 23
Ran Stop Sign18 (2.9%)↓-5.3%prior 19

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 different environmental conditions saw minor changes between 2015 and 2016, largely mirroring the overall decrease in crash volume. Crashes in clear weather decreased from 364 to 266, and those on dry road surfaces fell from 400 to 298. The proportion of crashes occurring in daylight conditions decreased from 48.1% of total crashes in 2015 to 40.7% in 2016. Crashes on roads affected by ice or frost remained relatively stable, with 26 incidents in 2016 compared to 28 in the prior year.

Weather

Clear266 (66.0%)
↓-26.9%prior 364
Cloudy80 (19.9%)
↓-10.1%prior 89
Rain21 (5.2%)
↓-51.2%prior 43
Snow16 (4.0%)
↓-42.9%prior 28
Freezing rain/drizzle11 (2.7%)
↓-15.4%prior 13
Fog, smoke, smog4 (1.0%)
Blowing Snow3 (0.7%)
Severe Winds1 (0.2%)
Sleet, hail1 (0.2%)
↓-80.0%prior 5

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

Lighting

Daylight256 (63.1%)
↓-30.6%prior 369
Dark - roadway not lighted78 (19.2%)
↓-8.2%prior 85
Dark - roadway lighted39 (9.6%)
↓-31.6%prior 57
Dark - unknown roadway lighting14 (3.4%)
↑16.7%prior 12
Dawn12 (3.0%)
↓-25.0%prior 16
Dusk7 (1.7%)
↓-22.2%prior 9

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

Road Surface

Dry298 (73.6%)
↓-25.5%prior 400
Wet43 (10.6%)
↓-28.3%prior 60
Ice/frost26 (6.4%)
↓-7.1%prior 28
Snow23 (5.7%)
↓-50.0%prior 46
Gravel13 (3.2%)
→0.0%prior 13
Slush2 (0.5%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes showed a consistent pattern, with Ford and Chevrolet remaining the top two most frequent makes in both 2016 and 2015. The number of Fords involved decreased from 203 to 184, and Chevrolets decreased from 197 to 139, in line with the overall reduction in crashes. When examining the age of persons involved, the 16-20 age group saw its representation increase from 10.6% of all persons in 2015 to 12.5% in 2016, despite a drop in the absolute number of individuals in that group from 152 to 133.

Top Vehicle Makes (912 vehicles)

1
FORD184 (20.2%)
↓-9.4%prior 203
2
CHEVROLET139 (15.2%)
↓-29.4%prior 197
3
DODGE74 (8.1%)
↑5.7%prior 70
4
CHEV44 (4.8%)
↓-37.1%prior 70
5
CHRYSLER42 (4.6%)
↑2.4%prior 41
6
GMC40 (4.4%)
↓-25.9%prior 54
7
TOYOTA34 (3.7%)
↓-20.9%prior 43
8
KIA29 (3.2%)
↑7.4%prior 27
9
PONTIAC27 (3%)
→0.0%prior 27
10
BUICK25 (2.7%)
↓-19.4%prior 31

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

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

Sex Distribution (650 persons with recorded sex)

Male353 (54.3%)
↓-35.6%prior 548
Female297 (45.7%)
↓-21.8%prior 380

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: 629
  • Total persons involved: 1,060
  • Total vehicles involved: 912

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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