Yearly Traffic Safety Analysis

2,322 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Black Hawk County, total traffic crashes increased by 4.4% from 2,225 in 2018 to 2,322 in 2019. This period saw a corresponding rise in both injuries and fatalities. The most significant year-over-year shift was a substantial increase in the proportion of crashes occurring on roads affected by snow, ice, or slush, which rose from 15.0% of all incidents in 2018 to 24.0% in 2019.

2,322

▲ 4.4%was 2,225

Total Crash Events

11

▲ 10.0%was 10

Persons Killed

808

▲ 7.7%was 750

Persons Injured

10

▲ 42.9%was 7

Fatal Crash Events

Note: "Persons Killed" (11) counts individual fatalities across all crash events. "Fatal" in the severity table below (10) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in Black Hawk County shows an increase in traffic incidents year-over-year. Total crashes rose by 4.4%, from 2,225 to 2,322. This was accompanied by a 7.7% increase in persons injured (from 750 to 808) and a 10% increase in fatalities (from 10 to 11).

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 2↓-50.0%

1

Cyclists Killed

Prior: 2↓-50.0%

9

Motorists Killed

Prior: 6↑50.0%

13

Pedestrians Injured

Prior: 15↓-13.3%

20

Cyclists Injured

Prior: 20→0.0%

775

Motorists Injured

Prior: 712↑8.8%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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 changes between the two periods. The peak day for crashes shifted from Monday in 2018 (368 crashes) to Friday in 2019 (408 crashes). However, the afternoon rush hour remained the most frequent time for incidents, with the 3 PM hour being the peak in both years, recording 207 crashes in 2018 and 218 in 2019.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened slightly in 2019 compared to the prior year. The number of fatal crashes increased from 7 to 10, and the fatal crash rate rose from 0.31 to 0.43 per 100 crashes. The proportion of crashes resulting in any injury (possible, minor, or serious) also increased, from 27.1% of all crashes in 2018 to 28.7% in 2019. Crashes resulting in serious injuries rose from 32 to 42.

Severity is per crash event (most severe injury). 10 fatal crash events resulted in 11 persons killed.

Outcome by Severity (Crash Events)

Fatal10fatal crashes0.4%
↑42.9%prior 7
Serious Injury42serious injury crashes1.8%
↑31.3%prior 32
Minor Injury200minor injury crashes8.6%
↑10.5%prior 181
Possible Injury424possible injury crashes18.3%
↑8.7%prior 390
No Injury1,646no injury crashes70.9%
↑1.9%prior 1,615

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes shifted between 2018 and 2019. Incidents attributed to "Driving too fast for conditions" saw a significant 45.7% increase in count, rising from 127 to 185 and becoming the second-most cited factor in 2019. Crashes from running a stop sign also increased by 45%, from 91 to 132. Conversely, crashes due to "Followed too close" decreased from 189 to 166, and animal-related incidents fell from 182 to 163.

Officer-Reported Primary Contributing Cause

Other (explain in narrative): Other231 (9.9%)↑12.1%prior 206
Driving too fast for conditions185 (8%)↑45.7%prior 127
Followed too close166 (7.1%)↓-12.2%prior 189
Ran off road - left164 (7.1%)↑24.2%prior 132
Animal163 (7%)↓-10.4%prior 182
FTYROW: From stop sign158 (6.8%)↑1.3%prior 156
Ran Stop Sign132 (5.7%)↑45.1%prior 91
Ran Traffic Signal98 (4.2%)↓-13.3%prior 113
FTYROW: Making left turn91 (3.9%)↓-7.1%prior 98
Lost Control77 (3.3%)↓-16.3%prior 92

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While lighting conditions and weather patterns remained broadly consistent year-over-year, there was a marked shift in road surface conditions at the time of crashes. The proportion of incidents on dry roads fell from 65.3% in 2018 to 59.1% in 2019. Correspondingly, crashes on roads with snow, ice, or slush increased significantly, accounting for 24.0% of all crashes in 2019, up from 15.0% in the previous year.

Weather

Clear1,448 (65.7%)
↑7.7%prior 1,345
Cloudy346 (15.7%)
↓-13.5%prior 400
Snow146 (6.6%)
↑19.7%prior 122
Rain122 (5.5%)
↓-0.8%prior 123
Freezing rain/drizzle59 (2.7%)
↓-3.3%prior 61
Blowing Snow44 (2.0%)
↑300.0%prior 11
Severe Winds16 (0.7%)
Fog, smoke, smog10 (0.5%)
→0.0%prior 10
Sleet, hail6 (0.3%)
↓-14.3%prior 7
Other (explain in narrative)6 (0.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight1,497 (67.9%)
↑4.5%prior 1,432
Dark - roadway lighted348 (15.8%)
↓-5.4%prior 368
Dark - roadway not lighted226 (10.2%)
↑18.3%prior 191
Dusk62 (2.8%)
↑55.0%prior 40
Dawn41 (1.9%)
↑17.1%prior 35
Dark - unknown roadway lighting32 (1.5%)
↑60.0%prior 20

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry1,373 (62.3%)
↓-5.5%prior 1,453
Snow261 (11.8%)
↑49.1%prior 175
Ice/frost256 (11.6%)
↑109.8%prior 122
Wet248 (11.2%)
↓-11.1%prior 279
Slush41 (1.9%)
↑10.8%prior 37
Gravel12 (0.5%)
↓-7.7%prior 13
Mud, dirt5 (0.2%)
Sand4 (0.2%)
Other (explain in narrative)3 (0.1%)
Oil1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes, primarily Chevrolet and Ford, remained consistent between 2018 and 2019. A more significant change was observed in the age demographics of persons involved in crashes. The number of individuals in the 35-44 and 45-54 age groups involved in crashes increased by 20.1% and 33.1% respectively, representing a notable upward shift in the age of crash participants.

Top Vehicle Makes (4,197 vehicles)

1
FORD718 (17.1%)
↑9.0%prior 659
2
CHEV612 (14.6%)
↓-6.4%prior 654
3
TOYT250 (6%)
↑21.4%prior 206
4
CHEVROLET239 (5.7%)
↑14.9%prior 208
5
DODG172 (4.1%)
↓-9.0%prior 189
6
JEEP155 (3.7%)
↑46.2%prior 106
7
NISS147 (3.5%)
↑34.9%prior 109
8
NR137 (3.3%)
↑55.7%prior 88
9
HOND128 (3%)
↑23.1%prior 104
10
GMC111 (2.6%)
↑5.7%prior 105

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

781 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (3,754 persons with recorded sex)

Male2,069 (55.1%)
↑15.7%prior 1,789
Female1,685 (44.9%)
↑12.0%prior 1,505

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 2,322
  • Total persons involved: 5,469
  • Total vehicles involved: 4,197

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-annual-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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