Monthly Traffic Safety Analysis

5,964 CRASHES IN
IOWA, IA
NOVEMBER 2019

All metrics benchmarked againstNovember 2018

In November 2019, there were 5,964 total crashes, a 1.0% decrease from the 6,022 crashes recorded in November 2018. While overall crashes slightly declined, the number of fatalities rose from 29 to 35, a 20.7% year-over-year increase. This increase in crash severity, despite a stable total crash volume, represents the most notable shift between the two periods.

5,964

▼ -1.0%was 6,022

Total Crash Events

35

▲ 20.7%was 29

Persons Killed

1,605

▲ 0.9%was 1,590

Persons Injured

32

▲ 28.0%was 25

Fatal Crash Events

Note: "Persons Killed" (35) counts individual fatalities across all crash events. "Fatal" in the severity table below (32) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-11-01 to 2019-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in traffic crashes shows a slight year-over-year decrease. Total crashes fell from 6,022 in November 2018 to 5,964 in November 2019, a reduction of 58 incidents or 1.0%. Despite this minor decline in volume, key severity metrics showed an increase.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 4↓-50.0%

0

Cyclists Killed

Prior: 0→0.0%

33

Motorists Killed

Prior: 25↑32.0%

0

Other Killed

Prior: 0→0.0%

39

Pedestrians Injured

Prior: 30↑30.0%

10

Cyclists Injured

Prior: 11↓-9.1%

1,555

Motorists Injured

Prior: 1,545↑0.6%

1

Other Injured

Prior: 4↓-75.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-11-01 to 2019-11-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained largely consistent year-over-year. Friday was the peak day for crashes in both November 2019 (1,154 crashes) and November 2018 (1,280 crashes), and the 5 PM hour was the peak hour in both periods (702 and 723 crashes, respectively). One notable shift was a substantial increase in Monday crashes, which rose from 773 in the prior year to 1,043 in the current period.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-11-01 to 2019-11-30 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-11-01 to 2019-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes decreased, the severity of crashes worsened year-over-year. The number of fatal crashes increased from 25 to 32, and total fatalities rose from 29 to 35. Consequently, the fatal crash rate per 100 crashes increased from 0.42 to 0.54. The proportion of crashes resulting in serious injuries also saw a slight increase, rising from 1.5% to 1.6% of all crashes.

Severity is per crash event (most severe injury). 32 fatal crash events resulted in 35 persons killed.

Outcome by Severity (Crash Events)

Fatal32fatal crashes0.5%
↑28.0%prior 25
Serious Injury95serious injury crashes1.6%
↑8.0%prior 88
Minor Injury385minor injury crashes6.5%
↓-4.0%prior 401
Possible Injury842possible injury crashes14.1%
↑1.0%prior 834
No Injury4,610no injury crashes77.3%
↓-1.4%prior 4,674

Source: Iowa Crash Data · ArcGIS Open Data · 2019-11-01 to 2019-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-11-01 to 2019-11-30 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors remained consistent between the two periods, with 'Animal' being the most cited factor in both November 2019 (1,588 crashes) and November 2018 (1,602 crashes). The top five factors were identical in both years, including 'Followed too close' and 'Driving too fast for conditions'. The count of crashes attributed to 'Followed too close' decreased from 556 to 496, while crashes involving 'Ran off road - left' increased from 335 to 362.

Officer-Reported Primary Contributing Cause

Animal1,588 (26.6%)↓-0.9%prior 1,602
Followed too close496 (8.3%)↓-10.8%prior 556
Driving too fast for conditions414 (6.9%)↑0.2%prior 413
Ran off road - left362 (6.1%)↑8.1%prior 335
Lost Control312 (5.2%)↑2.6%prior 304
Other (explain in narrative): Other273 (4.6%)↑2.2%prior 267
FTYROW: From stop sign272 (4.6%)↓-4.9%prior 286
FTYROW: Making left turn192 (3.2%)↓-15.8%prior 228
Ran off road - straight180 (3%)↓-2.2%prior 184
Ran Stop Sign173 (2.9%)↑15.3%prior 150

Source: Iowa Crash Data · ArcGIS Open Data · 2019-11-01 to 2019-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Year-over-year, the distribution of crashes across different conditions remained relatively stable. Crashes on dry road surfaces were most common in both periods, though the count decreased from 3,118 to 2,967. There was a notable increase in crashes occurring on roads with ice or frost, which rose from 421 incidents in November 2018 to 482 in November 2019. Crashes in clear weather increased from 2,484 to 2,784, while crashes during rain and snow events both decreased.

Weather

Clear2,784 (59.1%)
↑12.1%prior 2,484
Cloudy1,038 (22.0%)
↓-17.7%prior 1,261
Snow329 (7.0%)
↓-13.0%prior 378
Rain291 (6.2%)
↓-22.8%prior 377
Freezing rain/drizzle143 (3.0%)
↑21.2%prior 118
Blowing Snow89 (1.9%)
↑122.5%prior 40
Sleet, hail13 (0.3%)
↑160.0%prior 5
Fog, smoke, smog9 (0.2%)
↓-80.0%prior 45
Severe Winds8 (0.2%)
Other (explain in narrative)6 (0.1%)
↑20.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2019-11-01 to 2019-11-30 · Weather condition at time of crash

Lighting

Daylight2,540 (53.8%)
↓-1.6%prior 2,581
Dark - roadway lighted959 (20.3%)
↓-0.9%prior 968
Dark - roadway not lighted882 (18.7%)
↑3.6%prior 851
Dusk178 (3.8%)
↓-7.8%prior 193
Dawn129 (2.7%)
↑21.7%prior 106
Dark - unknown roadway lighting32 (0.7%)
↑14.3%prior 28

Source: Iowa Crash Data · ArcGIS Open Data · 2019-11-01 to 2019-11-30 · Lighting condition field

Road Surface

Dry2,967 (63.0%)
↓-4.8%prior 3,118
Wet691 (14.7%)
↓-1.1%prior 699
Ice/frost482 (10.2%)
↑14.5%prior 421
Snow389 (8.3%)
↑2.9%prior 378
Slush85 (1.8%)
↑165.6%prior 32
Gravel80 (1.7%)
↑23.1%prior 65
Mud, dirt9 (0.2%)
↑80.0%prior 5
Other (explain in narrative)6 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-11-01 to 2019-11-30 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes were nearly identical year-over-year, with Ford and Chevrolet vehicles consistently being the most numerous in both periods. Analysis of persons involved shows a significant increase in nearly every age demographic, despite a slight decrease in total crashes. For instance, the number of persons aged 26-34 involved in crashes grew from 1,609 to 2,055, and the 16-20 age group grew from 1,243 to 1,528.

Top Vehicle Makes (9,524 vehicles)

1
FORD1,602 (16.8%)
↓-0.8%prior 1,615
2
CHEV1,304 (13.7%)
↓-7.3%prior 1,406
3
CHEVROLET587 (6.2%)
↑5.4%prior 557
4
DODG441 (4.6%)
↓-0.2%prior 442
5
TOYT429 (4.5%)
↓-13.3%prior 495
6
JEEP344 (3.6%)
↓-6.5%prior 368
7
GMC298 (3.1%)
↓-10.2%prior 332
8
HOND298 (3.1%)
↓-9.7%prior 330
9
NISS263 (2.8%)
↓-6.1%prior 280
10
NR250 (2.6%)
↑3.3%prior 242

Source: Iowa Crash Data · ArcGIS Open Data · 2019-11-01 to 2019-11-30 · Vehicle unit records

1,342 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (8,761 persons with recorded sex)

Male5,001 (57.1%)
↑41.4%prior 3,536
Female3,760 (42.9%)
↑35.4%prior 2,776

Source: Iowa Crash Data · ArcGIS Open Data · 2019-11-01 to 2019-11-30 · 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: 2019-11-01 through 2019-11-30
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-11-01 through 2019-11-30 (30 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 5,964
  • Total persons involved: 13,072
  • Total vehicles involved: 9,524

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: November 2019." Published September 9, 2026. Reporting period: 2019-11-01 to 2019-11-30. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/november-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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