Yearly Traffic Safety Analysis

631 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Lee County, total traffic crashes remained stable, with 631 incidents in 2017 compared to 629 in 2016, an increase of less than one percent. Despite the steady number of crashes, the most significant year-over-year change was a substantial decrease in traffic fatalities, which fell from 11 in 2016 to 4 in 2017. The total number of injuries also declined from 214 to 202.

631

▲ 0.3%was 629

Total Crash Events

4

▼ -63.6%was 11

Persons Killed

202

▼ -5.6%was 214

Persons Injured

4

▼ -63.6%was 11

Fatal Crash Events

Note: "Persons Killed" (4) 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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crash trends in Lee County were stable between 2016 and 2017. The total number of crashes increased by only two incidents, from 629 to 631. However, the severity of these incidents decreased, as evidenced by a 64% reduction in fatalities and a 5.6% reduction in total injuries year-over-year.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 1↓-100.0%

3

Motorists Killed

Prior: 10↓-70.0%

0

Other Killed

Prior: 0→0.0%

2

Pedestrians Injured

Prior: 2→0.0%

4

Cyclists Injured

Prior: 2↑100.0%

195

Motorists Injured

Prior: 210↓-7.1%

1

Other Injured

Prior: 0→%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed some shifts between the two periods. Friday remained the peak day for crashes in both 2016 (120 crashes) and 2017 (111 crashes). However, the peak hour for crashes shifted from the 5 p.m. evening commute hour in 2016, which saw 43 crashes, to the 6 a.m. morning commute hour in 2017, which recorded 46 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes decreased from 2016 to 2017. Fatal crashes dropped from 11 incidents, representing 1.7% of all crashes in 2016, to 4 incidents, or 0.6% of the total in 2017. The proportion of crashes resulting in serious or possible injuries also declined, while the share of no-injury crashes increased from 71.9% in 2016 to 74.8% in 2017.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.6%
↓-63.6%prior 11
Serious Injury24serious injury crashes3.8%
↓-14.3%prior 28
Minor Injury60minor injury crashes9.5%
→0.0%prior 60
Possible Injury71possible injury crashes11.3%
↓-9.0%prior 78
No Injury472no injury crashes74.8%
↑4.4%prior 452

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in both years, with the count of such incidents increasing from 221 in 2016 to 249 in 2017. Crashes attributed to a driver losing control decreased from 52 to 38. Conversely, incidents where a driver followed too closely increased from 24 in 2016 to 31 in 2017.

Officer-Reported Primary Contributing Cause

Animal249 (39.5%)↑12.7%prior 221
Other (explain in narrative): Other38 (6%)↑2.7%prior 37
Lost Control38 (6%)↓-26.9%prior 52
Ran off road - straight32 (5.1%)↑3.2%prior 31
FTYROW: From stop sign32 (5.1%)↓-15.8%prior 38
Followed too close31 (4.9%)↑29.2%prior 24
Ran off road - left27 (4.3%)↑17.4%prior 23
Ran Stop Sign21 (3.3%)↑16.7%prior 18
Driving too fast for conditions17 (2.7%)↓-26.1%prior 23
Ran Traffic Signal10 (1.6%)↑66.7%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions were broadly similar year-over-year, with the majority of incidents in both periods occurring in daylight, on dry roads, and in clear weather. There was a notable decrease in crashes occurring on adverse road surfaces; incidents on ice or frost-covered roads fell from 26 in 2016 to 10 in 2017, and crashes on snow-covered roads decreased from 23 to 11.

Weather

Clear256 (65.8%)
↓-3.8%prior 266
Cloudy90 (23.1%)
↑12.5%prior 80
Rain24 (6.2%)
↑14.3%prior 21
Snow9 (2.3%)
↓-43.8%prior 16
Freezing rain/drizzle4 (1.0%)
↓-63.6%prior 11
Fog, smoke, smog3 (0.8%)
Blowing Snow2 (0.5%)
Blowing sand, soil, dirt1 (0.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight239 (61.1%)
↓-6.6%prior 256
Dark - roadway not lighted74 (18.9%)
↓-5.1%prior 78
Dark - roadway lighted38 (9.7%)
↓-2.6%prior 39
Dark - unknown roadway lighting22 (5.6%)
↑57.1%prior 14
Dusk12 (3.1%)
↑71.4%prior 7
Dawn6 (1.5%)
↓-50.0%prior 12

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry318 (81.3%)
↑6.7%prior 298
Wet41 (10.5%)
↓-4.7%prior 43
Snow11 (2.8%)
↓-52.2%prior 23
Ice/frost10 (2.6%)
↓-61.5%prior 26
Gravel6 (1.5%)
↓-53.8%prior 13
Slush4 (1.0%)
Sand1 (0.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes, including Ford and Chevrolet, remained consistent between 2016 and 2017. An analysis of persons involved in crashes shows a shift in age distribution; the number of individuals in the 21-25 age group increased from 87 to 115, while those in the 35-44 age group decreased from 163 to 129. The 0-15 age group also saw a significant drop from 25 individuals in 2016 to 9 in 2017.

Top Vehicle Makes (895 vehicles)

1
FORD182 (20.3%)
↓-1.1%prior 184
2
CHEVROLET105 (11.7%)
↓-24.5%prior 139
3
CHEV72 (8%)
↑63.6%prior 44
4
GMC39 (4.4%)
↓-2.5%prior 40
5
DODGE37 (4.1%)
↓-50.0%prior 74
6
TOYOTA35 (3.9%)
↑2.9%prior 34
7
JEEP32 (3.6%)
↑39.1%prior 23
8
DODG30 (3.4%)
↑57.9%prior 19
9
PONTIAC28 (3.1%)
↑3.7%prior 27
10
CHRYSLER26 (2.9%)
↓-38.1%prior 42

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (594 persons with recorded sex)

Male356 (59.9%)
↑0.8%prior 353
Female238 (40.1%)
↓-19.9%prior 297

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 631
  • Total persons involved: 1,071
  • Total vehicles involved: 895

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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