Monthly Traffic Safety Analysis

4,597 CRASHES IN
IOWA, IA
JUNE 2016

All metrics benchmarked againstJune 2015

In June 2016, there were 4,597 total crashes recorded in Iowa, representing a 6.0% increase from the 4,336 crashes documented in June 2015. This overall rise in collisions was accompanied by a significant year-over-year shift in crash outcomes. The most notable change was a 60.0% increase in total fatalities, which rose from 25 in the prior period to 40 in the current period.

4,597

6.0%was 4,336

Total Crash Events

40

60.0%was 25

Persons Killed

1,831

18.3%was 1,548

Persons Injured

36

56.5%was 23

Fatal Crash Events

Note: "Persons Killed" (40) counts individual fatalities across all crash events. "Fatal" in the severity table below (36) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-06-01 to 2016-06-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Year-over-year data indicates a rising trend in traffic collisions for June. Total crashes increased by 6.0%, from 4,336 in June 2015 to 4,597 in June 2016. This increase was also reflected in crash severity, with total injuries rising by 18.3% from 1,548 to 1,831 and total fatalities increasing by 60.0% from 25 to 40.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 3-33.3%

3

Cyclists Killed

Prior: 1200.0%

35

Motorists Killed

Prior: 2075.0%

0

Other Killed

Prior: 1-100.0%

31

Pedestrians Injured

Prior: 310.0%

60

Cyclists Injured

Prior: 5020.0%

1,737

Motorists Injured

Prior: 1,46418.6%

3

Other Injured

Prior: 30.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-06-01 to 2016-06-30 · 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 June 2015 and June 2016. The day with the highest number of crashes moved from Monday (777 crashes) in the prior year to Thursday (804 crashes) in the current year. The peak hour for collisions also shifted slightly later in the day, moving from the 4 PM hour (371 crashes) in 2015 to the 5 PM hour (376 crashes) in 2016.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-06-01 to 2016-06-30 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-06-01 to 2016-06-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened in June 2016 compared to the same month in 2015. The number of fatal crashes rose from 23 to 36, and the corresponding fatal crash rate increased from 0.53 to 0.78 per 100 crashes. The proportion of crashes resulting in any injury also grew, from 29.1% of all crashes in June 2015 to 30.8% in June 2016. This was driven by increases in both serious injury crashes (from 114 to 130) and minor injury crashes (from 440 to 535).

Severity is per crash event (most severe injury). 36 fatal crash events resulted in 40 persons killed.

Outcome by Severity (Crash Events)

Fatal36fatal crashes0.8%
56.5%prior 23
Serious Injury130serious injury crashes2.8%
14.0%prior 114
Minor Injury535minor injury crashes11.6%
21.6%prior 440
Possible Injury752possible injury crashes16.4%
6.2%prior 708
No Injury3,144no injury crashes68.4%
3.0%prior 3,051

Source: Iowa Crash Data · ArcGIS Open Data · 2016-06-01 to 2016-06-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-06-01 to 2016-06-30 · Most severe injury per crash record

Top Contributing Factors

The top three contributing factors to crashes remained the same in both periods, though their counts changed. Collisions involving an animal, the leading factor, decreased in count from 758 to 650. In contrast, the second-ranked factor, 'Followed too close,' saw its count increase by 28.4% from 409 to 525 incidents. 'Lost Control' incidents, the third most common factor, also increased from 262 to 282 crashes.

Officer-Reported Primary Contributing Cause

Animal650 (14.1%)-14.2%prior 758
Followed too close525 (11.4%)28.4%prior 409
Lost Control282 (6.1%)7.6%prior 262
FTYROW: From stop sign257 (5.6%)4.9%prior 245
FTYROW: Making left turn238 (5.2%)22.1%prior 195
Ran off road - left235 (5.1%)3.1%prior 228
Other (explain in narrative): Other213 (4.6%)-0.9%prior 215
Ran Traffic Signal167 (3.6%)9.2%prior 153
Ran off road - straight154 (3.4%)14.9%prior 134
Ran Stop Sign125 (2.7%)20.2%prior 104

Source: Iowa Crash Data · ArcGIS Open Data · 2016-06-01 to 2016-06-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

A significant shift was observed in the environmental conditions under which crashes occurred. In June 2016, a larger proportion of crashes happened in clear weather (74.7% vs. 52.5% in 2015) and on dry roads (80.4% vs. 70.5% in 2015). Correspondingly, the share of crashes during rain dropped from 10.2% to 3.3%, and collisions on wet surfaces fell from 13.3% to 5.0% year-over-year. The distribution of crashes by lighting condition remained relatively stable, with approximately 71% of crashes occurring in daylight in 2016 compared to 69% in 2015.

Weather

Clear3,432 (84.9%)
50.8%prior 2,276
Cloudy443 (11.0%)
-55.2%prior 988
Rain151 (3.7%)
-65.9%prior 443
Severe Winds9 (0.2%)
Other (explain in narrative)4 (0.1%)
Fog, smoke, smog2 (0.0%)
-90.5%prior 21

Source: Iowa Crash Data · ArcGIS Open Data · 2016-06-01 to 2016-06-30 · Weather condition at time of crash

Lighting

Daylight3,262 (80.4%)
9.4%prior 2,983
Dark - roadway lighted354 (8.7%)
9.6%prior 323
Dark - roadway not lighted293 (7.2%)
-6.1%prior 312
Dusk86 (2.1%)
16.2%prior 74
Dawn50 (1.2%)
-15.3%prior 59
Dark - unknown roadway lighting14 (0.3%)
27.3%prior 11

Source: Iowa Crash Data · ArcGIS Open Data · 2016-06-01 to 2016-06-30 · Lighting condition field

Road Surface

Dry3,694 (91.1%)
20.8%prior 3,058
Wet232 (5.7%)
-59.8%prior 577
Gravel116 (2.9%)
19.6%prior 97
Water (standing or moving)5 (0.1%)
Mud, dirt4 (0.1%)
Sand2 (0.0%)
Other (explain in narrative)1 (0.0%)
-80.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2016-06-01 to 2016-06-30 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes remained broadly consistent, with Ford and Chevrolet variants being the most frequent in both periods. The number of Ford vehicles in crashes increased from 1,238 to 1,303 year-over-year. An analysis of persons involved in crashes reveals a shift in age demographics, with the 26-34 age group seeing a notable increase in representation from 1,359 individuals in June 2015 to 1,503 in June 2016.

Top Vehicle Makes (7,911 vehicles)

1
FORD1,303 (16.5%)
5.3%prior 1,238
2
CHEVROLET815 (10.3%)
30.0%prior 627
3
CHEV707 (8.9%)
-12.0%prior 803
4
TOYOTA319 (4%)
34.0%prior 238
5
DODGE285 (3.6%)
23.9%prior 230
6
TOYT262 (3.3%)
-5.8%prior 278
7
DODG231 (2.9%)
-14.1%prior 269
8
JEEP226 (2.9%)
9.7%prior 206
9
HONDA225 (2.8%)
47.1%prior 153
10
GMC220 (2.8%)
18.9%prior 185

Source: Iowa Crash Data · ArcGIS Open Data · 2016-06-01 to 2016-06-30 · Vehicle unit records

1,099 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (6,159 persons with recorded sex)

Male3,423 (55.6%)
-4.6%prior 3,587
Female2,736 (44.4%)
-1.4%prior 2,774

Source: Iowa Crash Data · ArcGIS Open Data · 2016-06-01 to 2016-06-30 · 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: 2016-06-01 through 2016-06-30
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-06-01 through 2016-06-30 (30 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 4,597
  • Total persons involved: 9,629
  • Total vehicles involved: 7,911

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 2016." Published September 9, 2026. Reporting period: 2016-06-01 to 2016-06-30. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/june-2016-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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