Yearly Traffic Safety Analysis

396 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Boone County recorded 396 total crashes, a 5.0% increase from the 377 crashes reported in 2018. While total fatalities decreased from 3 to 2, the number of people injured in crashes rose from 122 to 140, a 14.8% year-over-year increase.

396

▲ 5.0%was 377

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

140

▲ 14.8%was 122

Persons Injured

2

▼ -33.3%was 3

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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Boone County increased by 5.0% from 2018 to 2019, rising from 377 to 396 incidents. This upward trend in crash volume was accompanied by a 14.8% increase in total injuries (from 122 to 140), even as fatalities declined from 3 to 2.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

4

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

135

Motorists Injured

Prior: 122↑10.7%

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 showed some shifts between 2018 and 2019. The peak hour for crashes remained consistent at 5 PM in both periods, with 35 crashes in 2019 and 36 in 2018. However, the most common day for crashes shifted from a tie between Thursday and Friday (67 crashes each) in 2018 to Monday (74 crashes) in 2019.

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

While the number of fatal crashes decreased from 3 in 2018 to 2 in 2019, the severity of non-fatal crashes increased. The count of serious injury crashes rose from 9 to 15, representing a shift from 2.4% to 3.8% of all crashes. The total number of crashes resulting in either minor or possible injury remained unchanged at 86 for both years. The proportion of crashes with no injuries was stable at 74.0% in both 2018 and 2019.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.5%
↓-33.3%prior 3
Serious Injury15serious injury crashes3.8%
↑66.7%prior 9
Minor Injury31minor injury crashes7.8%
↓-18.4%prior 38
Possible Injury55possible injury crashes13.9%
↑14.6%prior 48
No Injury293no injury crashes74%
↑5.0%prior 279

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 periods, with the count increasing from 103 in 2018 to 119 in 2019. The top five contributing factors were identical in both years, though their counts shifted. Crashes attributed to 'Failure to Yield Right of Way from a stop sign' saw a notable increase in count from 27 to 37. Meanwhile, incidents of 'Ran Stop Sign' remained unchanged at 22, and 'Lost Control' incidents increased slightly from 21 to 24.

Officer-Reported Primary Contributing Cause

Animal119 (30.1%)↑15.5%prior 103
FTYROW: From stop sign37 (9.3%)↑37.0%prior 27
Lost Control24 (6.1%)↑14.3%prior 21
Ran Stop Sign22 (5.6%)→0.0%prior 22
Followed too close21 (5.3%)↑5.0%prior 20
Other (explain in narrative): Other15 (3.8%)↑15.4%prior 13
Ran off road - straight14 (3.5%)↓-12.5%prior 16
Ran off road - left14 (3.5%)↓-26.3%prior 19
FTYROW: Making left turn12 (3%)↓-7.7%prior 13
Driving too fast for conditions12 (3%)↑20.0%prior 10

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

Road & Environmental Conditions

Crashes on dry roads and in clear weather constituted the majority of incidents in both years. The proportion of crashes occurring on dry road surfaces increased from 49.6% in 2018 to 53.0% in 2019. Correspondingly, crashes on adverse road surfaces like snow, ice, or wet pavement decreased as a share of the total, accounting for 22.0% of crashes in 2019 compared to 24.9% in 2018. The proportion of crashes happening in daylight conditions saw a slight increase, while crashes in dark conditions remained proportionally stable year-over-year.

Weather

Clear184 (60.5%)
↑4.0%prior 177
Cloudy74 (24.3%)
↑10.4%prior 67
Snow12 (3.9%)
↓-14.3%prior 14
Blowing Snow11 (3.6%)
Rain11 (3.6%)
↓-15.4%prior 13
Freezing rain/drizzle5 (1.6%)
↓-37.5%prior 8
Fog, smoke, smog4 (1.3%)
Severe Winds2 (0.7%)
Other (explain in narrative)1 (0.3%)

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

Lighting

Daylight212 (69.3%)
↑10.4%prior 192
Dark - roadway not lighted58 (19.0%)
↑23.4%prior 47
Dark - roadway lighted14 (4.6%)
↓-39.1%prior 23
Dusk14 (4.6%)
↑16.7%prior 12
Dawn8 (2.6%)
↓-46.7%prior 15

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

Road Surface

Dry210 (68.9%)
↑12.3%prior 187
Wet32 (10.5%)
↓-22.0%prior 41
Snow30 (9.8%)
↑25.0%prior 24
Ice/frost22 (7.2%)
↓-18.5%prior 27
Gravel6 (2.0%)
↓-14.3%prior 7
Slush3 (1.0%)
Mud, dirt1 (0.3%)
Other (explain in narrative)1 (0.3%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent year-over-year. Combining variations, Chevrolet (128 vehicles), Ford (115), and Dodge (55) were the top three makes in 2019, reflecting a slight increase in counts from their 2018 totals of 118, 102, and 52, respectively. The age distribution of all persons involved in crashes also showed stability. While the absolute number of people involved increased across most age brackets, the proportional representation of each age group did not change significantly between the two periods.

Top Vehicle Makes (608 vehicles)

1
FORD115 (18.9%)
↑12.7%prior 102
2
CHEV103 (16.9%)
↑19.8%prior 86
3
DODG39 (6.4%)
→0.0%prior 39
4
CHEVROLET25 (4.1%)
↓-21.9%prior 32
5
GMC21 (3.5%)
↑16.7%prior 18
6
TOYT20 (3.3%)
↓-25.9%prior 27
7
CHRY17 (2.8%)
↓-5.6%prior 18
8
HOND16 (2.6%)
↑14.3%prior 14
9
TOYO16 (2.6%)
↑23.1%prior 13
10
JEEP16 (2.6%)
↓-40.7%prior 27

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

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

Sex Distribution (575 persons with recorded sex)

Male340 (59.1%)
↑28.8%prior 264
Female235 (40.9%)
↑26.3%prior 186

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: 396
  • Total persons involved: 866
  • Total vehicles involved: 608

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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