Monthly Traffic Safety Analysis

3,963 CRASHES IN
IOWA, IA
JUNE 2020

All metrics benchmarked againstJune 2019

In June 2020, Iowa recorded 3,963 vehicle crashes, a 14.3% decrease from the 4,626 crashes reported in June 2019. This year-over-year decline was also reflected in crash outcomes, with total fatalities dropping from 35 to 24 and injuries decreasing from 1,595 to 1,419. The most notable shift was the change in the peak day for crashes, which moved from Saturday in the prior year to Monday in the current period.

3,963

▼ -14.3%was 4,626

Total Crash Events

24

▼ -31.4%was 35

Persons Killed

1,419

▼ -11.0%was 1,595

Persons Injured

24

▼ -29.4%was 34

Fatal Crash Events

Note: "Persons Killed" (24) counts individual fatalities across all crash events. "Fatal" in the severity table below (24) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2020-06-01 to 2020-06-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety metrics showed a significant downward trend in June 2020 compared to the same month in 2019. Total crashes fell by 14.3% from 4,626 to 3,963. This was accompanied by an 11.0% decrease in injuries (from 1,595 to 1,419) and a 31.4% decrease in fatalities (from 35 to 24).

Vulnerable Road User Casualties

3

Pedestrians Killed

Prior: 2↑50.0%

0

Cyclists Killed

Prior: 3↓-100.0%

21

Motorists Killed

Prior: 30↓-30.0%

0

Other Killed

Prior: 0→0.0%

18

Pedestrians Injured

Prior: 22↓-18.2%

33

Cyclists Injured

Prior: 46↓-28.3%

1,366

Motorists Injured

Prior: 1,527↓-10.5%

2

Other Injured

Prior: 0→%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-06-01 to 2020-06-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted year-over-year. In June 2020, the peak day for collisions was Monday with 701 incidents, a change from June 2019 when Saturday was the peak day with 736 crashes. The peak hour for crashes shifted slightly earlier to the 3 p.m. hour (324 crashes) from the 4 p.m. hour (364 crashes) in the prior year.

Source: Iowa Crash Data · ArcGIS Open Data · 2020-06-01 to 2020-06-30 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-06-01 to 2020-06-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The overall severity of crashes decreased compared to the previous year. The fatal crash rate fell from 0.73% of all crashes in June 2019 to 0.61% in June 2020. While the absolute number of injury-related crashes decreased, the proportion of crashes involving a possible, minor, or serious injury saw a slight increase, accounting for 29.5% of all crashes in the current period versus 28.0% in the prior year.

Outcome by Severity (Crash Events)

Fatal24fatal crashes0.6%
↓-29.4%prior 34
Serious Injury126serious injury crashes3.2%
↓-1.6%prior 128
Minor Injury434minor injury crashes11%
↓-7.9%prior 471
Possible Injury609possible injury crashes15.4%
↓-12.2%prior 694
No Injury2,770no injury crashes69.9%
↓-16.0%prior 3,299

Source: Iowa Crash Data · ArcGIS Open Data · 2020-06-01 to 2020-06-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-06-01 to 2020-06-30 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the leading contributing factor in both periods, though the count decreased by 21.1% from 838 to 661 crashes. Following too closely was the second most common factor in both years, with its count dropping by 28.7% from 534 to 381. While most top factors saw a decrease in count, their relative rankings remained largely consistent year-over-year.

Officer-Reported Primary Contributing Cause

Animal661 (16.7%)↓-21.1%prior 838
Followed too close381 (9.6%)↓-28.7%prior 534
Ran off road - left235 (5.9%)↓-5.6%prior 249
Other (explain in narrative): Other222 (5.6%)↓-24.7%prior 295
FTYROW: From stop sign209 (5.3%)↓-10.7%prior 234
Lost Control204 (5.1%)↓-7.7%prior 221
Ran off road - straight146 (3.7%)↑5.8%prior 138
FTYROW: Making left turn144 (3.6%)↓-28.0%prior 200
Operating vehicle in an reckless, erratic, careless, negligent manner125 (3.2%)↑0.8%prior 124
Driver Distraction: Other interior distraction120 (3%)↓-7.7%prior 130

Source: Iowa Crash Data · ArcGIS Open Data · 2020-06-01 to 2020-06-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with most incidents occurring in clear conditions. The proportion of crashes on dry road surfaces increased slightly from 74.7% (3,455 crashes) in the prior period to 76.3% (3,022 crashes) in the current period. Similarly, crashes in clear weather accounted for 69.0% of the total in June 2020, up from 62.2% in June 2019. The share of crashes occurring during daylight hours was stable at approximately 68.5% for both periods.

Weather

Clear2,732 (79.5%)
↓-5.1%prior 2,878
Cloudy486 (14.1%)
↓-37.5%prior 777
Rain204 (5.9%)
↓-23.9%prior 268
Other (explain in narrative)6 (0.2%)
Fog, smoke, smog4 (0.1%)
↓-73.3%prior 15
Severe Winds3 (0.1%)
↓-62.5%prior 8
Freezing rain/drizzle1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-06-01 to 2020-06-30 · Weather condition at time of crash

Lighting

Daylight2,716 (78.9%)
↓-14.5%prior 3,176
Dark - roadway lighted289 (8.4%)
↓-9.7%prior 320
Dark - roadway not lighted287 (8.3%)
↓-13.0%prior 330
Dusk86 (2.5%)
→0.0%prior 86
Dawn52 (1.5%)
↑2.0%prior 51
Dark - unknown roadway lighting11 (0.3%)
↑22.2%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2020-06-01 to 2020-06-30 · Lighting condition field

Road Surface

Dry3,022 (87.9%)
↓-12.5%prior 3,455
Wet324 (9.4%)
↓-20.6%prior 408
Gravel78 (2.3%)
↓-8.2%prior 85
Other (explain in narrative)7 (0.2%)
↑16.7%prior 6
Mud, dirt6 (0.2%)
→0.0%prior 6
Sand1 (0.0%)
Water (standing or moving)1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-06-01 to 2020-06-30 · Road surface condition field

Vehicles & Demographics

The top five vehicle makes involved in crashes—Chevrolet, Ford, Toyota, Dodge, and Honda—were the same in both June 2020 and June 2019, and they maintained the same rank order. The representation of different age groups among persons involved in crashes also remained relatively stable, although the 26-34 age group's share increased slightly from 16.9% of all persons with a known age to 18.9% year-over-year.

Top Vehicle Makes (6,643 vehicles)

1
FORD986 (14.8%)
↓-24.2%prior 1,301
2
CHEV825 (12.4%)
↓-17.6%prior 1,001
3
CHEVROLET511 (7.7%)
↓-0.2%prior 512
4
JEEP267 (4%)
↓-2.6%prior 274
5
DODG256 (3.9%)
↓-11.4%prior 289
6
GMC243 (3.7%)
↓-4.3%prior 254
7
TOYT229 (3.4%)
↓-29.1%prior 323
8
NR223 (3.4%)
↑5.2%prior 212
9
HOND203 (3.1%)
↓-22.5%prior 262
10
DODGE188 (2.8%)
↑4.4%prior 180

Source: Iowa Crash Data · ArcGIS Open Data · 2020-06-01 to 2020-06-30 · Vehicle unit records

1,291 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (5,920 persons with recorded sex)

Male3,518 (59.4%)
↓-12.0%prior 3,998
Female2,402 (40.6%)
↓-22.8%prior 3,111

Source: Iowa Crash Data · ArcGIS Open Data · 2020-06-01 to 2020-06-30 · 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: 2020-06-01 through 2020-06-30
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-06-01 through 2020-06-30 (30 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 3,963
  • Total persons involved: 9,258
  • Total vehicles involved: 6,643

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 2020." Published September 9, 2026. Reporting period: 2020-06-01 to 2020-06-30. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/june-2020-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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