Monthly Traffic Safety Analysis

4,357 CRASHES IN
IOWA, IA
JULY 2019

All metrics benchmarked againstJuly 2018

Total crashes rose from 4,064 in July 2018 to 4,357 in July 2019, a 7.2% increase. While total fatalities remained constant at 32, the number of crashes attributed to driving under the influence (DUI) increased by 18.2%, from 137 to 162 incidents.

4,357

7.2%was 4,064

Total Crash Events

32

Persons Killed

1,593

4.9%was 1,518

Persons Injured

28

-12.5%was 32

Fatal Crash Events

Note: "Persons Killed" (32) counts individual fatalities across all crash events. "Fatal" in the severity table below (28) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-07-01 to 2019-07-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Year-over-year data indicates a rising trend in traffic incidents for July. Total crashes increased by 7.2% from 4,064 to 4,357. Similarly, the number of individuals injured rose by 4.9% from 1,518 to 1,593, while the number of fatalities held steady at 32 for both periods.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 4-75.0%

1

Cyclists Killed

Prior: 3-66.7%

30

Motorists Killed

Prior: 2520.0%

0

Other Killed

Prior: 00.0%

26

Pedestrians Injured

Prior: 2313.0%

47

Cyclists Injured

Prior: 50-6.0%

1,518

Motorists Injured

Prior: 1,4405.4%

2

Other Injured

Prior: 5-60.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-07-01 to 2019-07-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes showed some shifts between the two periods. The peak day for crashes moved from Tuesday in July 2018 (739 crashes) to Monday in July 2019 (770 crashes). The peak hour for collisions remained consistent at 4 p.m. in both years, though the number of incidents during that hour increased from 336 to 374.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-07-01 to 2019-07-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-07-01 to 2019-07-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes shifted slightly year-over-year. The number of fatal crashes decreased from 32 to 28, lowering the fatal crash rate from 0.8% to 0.6% of all crashes. Conversely, the count of serious injury crashes rose from 110 to 124. The overall proportion of crashes involving any level of injury remained stable at approximately 31%.

Severity is per crash event (most severe injury). 28 fatal crash events resulted in 32 persons killed.

Outcome by Severity (Crash Events)

Fatal28fatal crashes0.6%
-12.5%prior 32
Serious Injury124serious injury crashes2.8%
12.7%prior 110
Minor Injury476minor injury crashes10.9%
7.9%prior 441
Possible Injury735possible injury crashes16.9%
4.4%prior 704
No Injury2,994no injury crashes68.7%
7.8%prior 2,777

Source: Iowa Crash Data · ArcGIS Open Data · 2019-07-01 to 2019-07-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-07-01 to 2019-07-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors were consistent across both periods, though their ranking changed. In July 2019, "Animal" was the top factor with 527 crashes, an increase of 59 incidents (a 12.6% increase in count) from the prior year when it was ranked second. "Followed too close" was the second most common factor with 496 crashes, nearly unchanged from 494 in the prior year. Notably, crashes attributed to "Driver Distraction: Other interior distraction" increased by 45.4% in count, from 108 to 157 incidents.

Officer-Reported Primary Contributing Cause

Animal527 (12.1%)12.6%prior 468
Followed too close496 (11.4%)0.4%prior 494
Other (explain in narrative): Other299 (6.9%)14.6%prior 261
Lost Control241 (5.5%)-8.7%prior 264
Ran off road - left225 (5.2%)2.7%prior 219
FTYROW: From stop sign222 (5.1%)9.4%prior 203
FTYROW: Making left turn196 (4.5%)-3.9%prior 204
Driver Distraction: Other interior distraction157 (3.6%)45.4%prior 108
Ran Traffic Signal147 (3.4%)11.4%prior 132
Made improper turn128 (2.9%)50.6%prior 85

Source: Iowa Crash Data · ArcGIS Open Data · 2019-07-01 to 2019-07-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Environmental conditions showed some variation between the two periods. While the majority of crashes in both years occurred in clear weather on dry roads, the number of incidents in adverse conditions grew. Crashes during rain increased from 87 in July 2018 to 166 in July 2019. Correspondingly, crashes on wet road surfaces increased by 80%, from 141 to 254 incidents.

Weather

Clear3,204 (81.7%)
2.6%prior 3,122
Cloudy541 (13.8%)
18.1%prior 458
Rain166 (4.2%)
90.8%prior 87
Severe Winds4 (0.1%)
Other (explain in narrative)2 (0.1%)
Fog, smoke, smog2 (0.1%)
-71.4%prior 7
Blowing sand, soil, dirt1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-07-01 to 2019-07-31 · Weather condition at time of crash

Lighting

Daylight3,166 (80.4%)
7.4%prior 2,949
Dark - roadway lighted347 (8.8%)
4.8%prior 331
Dark - roadway not lighted273 (6.9%)
0.7%prior 271
Dusk87 (2.2%)
26.1%prior 69
Dawn52 (1.3%)
2.0%prior 51
Dark - unknown roadway lighting11 (0.3%)
-15.4%prior 13

Source: Iowa Crash Data · ArcGIS Open Data · 2019-07-01 to 2019-07-31 · Lighting condition field

Road Surface

Dry3,579 (91.2%)
4.3%prior 3,433
Wet254 (6.5%)
80.1%prior 141
Gravel80 (2.0%)
-15.8%prior 95
Other (explain in narrative)4 (0.1%)
-33.3%prior 6
Sand3 (0.1%)
Mud, dirt2 (0.1%)
Oil1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-07-01 to 2019-07-31 · Road surface condition field

Vehicles & Demographics

The distribution of vehicle makes involved in crashes remained highly stable year-over-year, with Ford and Chevrolet models consistently being the most frequently involved. Regarding person demographics, the age distribution of individuals in crashes showed no significant proportional shifts between the two periods. The ratio of males to females involved in crashes saw a slight increase, moving from 1.30 males for every female in July 2018 to 1.38 in July 2019.

Top Vehicle Makes (7,575 vehicles)

1
FORD1,142 (15.1%)
0.1%prior 1,141
2
CHEV963 (12.7%)
2.7%prior 938
3
CHEVROLET491 (6.5%)
4.5%prior 470
4
TOYT327 (4.3%)
13.5%prior 288
5
DODG319 (4.2%)
7.0%prior 298
6
GMC254 (3.4%)
27.0%prior 200
7
JEEP236 (3.1%)
11.3%prior 212
8
HOND235 (3.1%)
-7.5%prior 254
9
TOYOTA206 (2.7%)
29.6%prior 159
10
DODGE198 (2.6%)
10.6%prior 179

Source: Iowa Crash Data · ArcGIS Open Data · 2019-07-01 to 2019-07-31 · Vehicle unit records

1,274 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (6,820 persons with recorded sex)

Male3,950 (57.9%)
32.8%prior 2,975
Female2,870 (42.1%)
25.2%prior 2,292

Source: Iowa Crash Data · ArcGIS Open Data · 2019-07-01 to 2019-07-31 · 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: 2019-07-01 through 2019-07-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-07-01 through 2019-07-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 4,357
  • Total persons involved: 10,235
  • Total vehicles involved: 7,575

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: July 2019." Published September 9, 2026. Reporting period: 2019-07-01 to 2019-07-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/july-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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