Monthly Traffic Safety Analysis

4,626 CRASHES IN
IOWA, IA
JUNE 2019

All metrics benchmarked againstJune 2018

In June 2019, there were 4,626 total crashes across Iowa, representing a 2.5% increase from the 4,515 crashes recorded in June 2018. While overall crash volume and outcomes remained relatively stable, the number of incidents attributed to driving under the influence (DUI) increased by 22.5% year-over-year, from 129 to 158.

4,626

▲ 2.5%was 4,515

Total Crash Events

35

▼ -10.3%was 39

Persons Killed

1,595

▼ -1.0%was 1,611

Persons Injured

34

▼ -5.6%was 36

Fatal Crash Events

Note: "Persons Killed" (35) counts individual fatalities across all crash events. "Fatal" in the severity table below (34) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-06-01 to 2019-06-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Total crashes in Iowa increased by 2.5% in June 2019 compared to the same month in the prior year, rising from 4,515 to 4,626. Despite this rise in crash volume, total fatalities decreased from 39 to 35, and the total number of injuries saw a slight decline from 1,611 to 1,595.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 2→0.0%

3

Cyclists Killed

Prior: 1↑200.0%

30

Motorists Killed

Prior: 36↓-16.7%

22

Pedestrians Injured

Prior: 31↓-29.0%

46

Cyclists Injured

Prior: 42↑9.5%

1,527

Motorists Injured

Prior: 1,531↓-0.3%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-06-01 to 2019-06-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak hour for crashes remained consistent at 4 p.m. for both June 2019 and June 2018, though the crash count during that hour decreased from 393 to 364. The peak day of the week shifted from Friday in the prior year (912 crashes) to Saturday in the current year (736 crashes). Crashes occurring on weekends (Saturday and Sunday) represented a larger portion of the monthly total in June 2019 compared to June 2018.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-06-01 to 2019-06-30 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-06-01 to 2019-06-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity distribution of crashes saw minimal change year-over-year. The fatal crash rate decreased slightly from 0.8% of all crashes in June 2018 to 0.7% in June 2019. Similarly, the proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) experienced a marginal decrease from 28.5% in the prior period to 28.0% in the current period.

Severity is per crash event (most severe injury). 34 fatal crash events resulted in 35 persons killed.

Outcome by Severity (Crash Events)

Fatal34fatal crashes0.7%
↓-5.6%prior 36
Serious Injury128serious injury crashes2.8%
↑0.8%prior 127
Minor Injury471minor injury crashes10.2%
↓-1.3%prior 477
Possible Injury694possible injury crashes15%
↑2.1%prior 680
No Injury3,299no injury crashes71.3%
↑3.3%prior 3,195

Source: Iowa Crash Data · ArcGIS Open Data · 2019-06-01 to 2019-06-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-06-01 to 2019-06-30 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors remained consistent, with 'Animal' (838 incidents) and 'Followed too close' (534 incidents) being the top two in June 2019. The count of crashes involving an animal increased by 11.6% from 751 in the prior year. Conversely, crashes attributed to 'Ran Traffic Signal' decreased by 13.9% in count, from 151 incidents in June 2018 to 130 in June 2019.

Officer-Reported Primary Contributing Cause

Animal838 (18.1%)↑11.6%prior 751
Followed too close534 (11.5%)↓-2.9%prior 550
Other (explain in narrative): Other295 (6.4%)↑5.0%prior 281
Ran off road - left249 (5.4%)↑5.1%prior 237
FTYROW: From stop sign234 (5.1%)↓-6.4%prior 250
Lost Control221 (4.8%)↑3.8%prior 213
FTYROW: Making left turn200 (4.3%)↑6.4%prior 188
Ran off road - straight138 (3%)↓-4.8%prior 145
Driver Distraction: Other interior distraction130 (2.8%)↑8.3%prior 120
Ran Traffic Signal130 (2.8%)↓-13.9%prior 151

Source: Iowa Crash Data · ArcGIS Open Data · 2019-06-01 to 2019-06-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

In both periods, the vast majority of crashes occurred in clear weather, during daylight hours, and on dry road surfaces. There was no significant year-over-year shift in the proportions of crashes related to lighting or road surface conditions. The proportion of crashes occurring in clear weather increased from 58.4% in June 2018 to 62.2% in June 2019, while the share of crashes in rainy conditions decreased from 7.3% to 5.8%.

Weather

Clear2,878 (72.8%)
↑9.1%prior 2,638
Cloudy777 (19.7%)
↓-15.9%prior 924
Rain268 (6.8%)
↓-18.8%prior 330
Fog, smoke, smog15 (0.4%)
↑66.7%prior 9
Severe Winds8 (0.2%)
↓-11.1%prior 9
Freezing rain/drizzle3 (0.1%)
Other (explain in narrative)3 (0.1%)
Blowing sand, soil, dirt2 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-06-01 to 2019-06-30 · Weather condition at time of crash

Lighting

Daylight3,176 (80.0%)
↑0.1%prior 3,173
Dark - roadway not lighted330 (8.3%)
↑11.5%prior 296
Dark - roadway lighted320 (8.1%)
↑4.9%prior 305
Dusk86 (2.2%)
↑2.4%prior 84
Dawn51 (1.3%)
↓-12.1%prior 58
Dark - unknown roadway lighting9 (0.2%)
↓-43.8%prior 16

Source: Iowa Crash Data · ArcGIS Open Data · 2019-06-01 to 2019-06-30 · Lighting condition field

Road Surface

Dry3,455 (87.2%)
↑5.7%prior 3,270
Wet408 (10.3%)
↓-23.5%prior 533
Gravel85 (2.1%)
↓-4.5%prior 89
Other (explain in narrative)6 (0.2%)
↑20.0%prior 5
Mud, dirt6 (0.2%)
↓-14.3%prior 7
Sand1 (0.0%)
Oil1 (0.0%)
Water (standing or moving)1 (0.0%)
↓-90.9%prior 11

Source: Iowa Crash Data · ArcGIS Open Data · 2019-06-01 to 2019-06-30 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes, including Chevrolet, Ford, and Toyota, remained consistent between June 2018 and June 2019 with no significant changes in their rankings or incident counts. Analysis of persons involved in crashes shows a slight shift in age demographics, with the share of persons in the 55-64 and 65+ age groups increasing slightly from the prior year.

Top Vehicle Makes (7,831 vehicles)

1
FORD1,301 (16.6%)
↑4.1%prior 1,250
2
CHEV1,001 (12.8%)
↓-0.9%prior 1,010
3
CHEVROLET512 (6.5%)
↑9.9%prior 466
4
TOYT323 (4.1%)
↓-14.3%prior 377
5
DODG289 (3.7%)
↓-19.3%prior 358
6
JEEP274 (3.5%)
↑21.2%prior 226
7
HOND262 (3.3%)
↑8.7%prior 241
8
GMC254 (3.2%)
↑9.5%prior 232
9
NR212 (2.7%)
↑27.7%prior 166
10
NISS208 (2.7%)
↑5.6%prior 197

Source: Iowa Crash Data · ArcGIS Open Data · 2019-06-01 to 2019-06-30 · Vehicle unit records

1,280 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (7,109 persons with recorded sex)

Male3,998 (56.2%)
↑3.8%prior 3,851
Female3,111 (43.8%)
↑5.9%prior 2,938

Source: Iowa Crash Data · ArcGIS Open Data · 2019-06-01 to 2019-06-30 · 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: 2019-06-01 through 2019-06-30
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-06-01 through 2019-06-30 (30 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 4,626
  • Total persons involved: 10,719
  • Total vehicles involved: 7,831

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: June 2019." Published September 9, 2026. Reporting period: 2019-06-01 to 2019-06-30. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/june-2019-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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