Yearly Traffic Safety Analysis

401 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Hamilton County, total traffic crashes increased from 389 in 2018 to 401 in 2019, a rise of approximately 3.1%. While total fatalities decreased from 4 to 3, the most notable year-over-year change was a substantial increase in crashes involving a driver under the influence (DUI), which rose from 2 incidents in 2018 to 11 in 2019.

401

▲ 3.1%was 389

Total Crash Events

3

▼ -25.0%was 4

Persons Killed

120

Persons Injured

3

▼ -25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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

Overall, the trend in traffic crashes in Hamilton County showed a slight increase year-over-year. The total number of crashes rose by 12 incidents, from 389 in 2018 to 401 in 2019. Despite this increase in total crashes, the number of resulting injuries remained unchanged at 120, and the number of fatalities decreased from 4 to 3.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 3→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

1

Cyclists Injured

Prior: 1→0.0%

118

Motorists Injured

Prior: 118→0.0%

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 in Hamilton County showed some shifts between 2018 and 2019. The day with the highest number of incidents moved from Friday, with 71 crashes in 2018, to Sunday, with 67 crashes in 2019. Similarly, the peak hour for crashes shifted later in the day, from the 3 p.m. hour in the prior year (28 crashes) to the 5 p.m. hour in the current year (27 crashes).

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

The severity of crashes saw a mixed but generally stable pattern year-over-year. The number of fatal crashes decreased from 4 in 2018 to 3 in 2019, lowering their share of total crashes from 1.0% to 0.7%. While the total number of injuries remained constant at 120, the count of serious injury crashes increased from 8 to 10. Crashes resulting in minor or possible injuries saw a slight decrease in both count and proportion.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.7%
↓-25.0%prior 4
Serious Injury10serious injury crashes2.5%
↑25.0%prior 8
Minor Injury29minor injury crashes7.2%
↓-6.5%prior 31
Possible Injury51possible injury crashes12.7%
↓-10.5%prior 57
No Injury308no injury crashes76.8%
↑6.6%prior 289

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

Collisions involving an animal remained the leading contributing factor in both periods, increasing slightly from 83 incidents in 2018 to 85 in 2019. A notable shift occurred with 'Driving too fast for conditions,' which decreased from 62 crashes to 42, falling from the second to the third-ranked factor. Conversely, 'Lost Control' incidents rose from 28 to 45, becoming the second-ranked factor in 2019. Failure to yield from a stop sign also saw a notable increase, with the count of related crashes rising from 12 to 26.

Officer-Reported Primary Contributing Cause

Animal85 (21.2%)↑2.4%prior 83
Lost Control45 (11.2%)↑60.7%prior 28
Driving too fast for conditions42 (10.5%)↓-32.3%prior 62
Ran off road - straight37 (9.2%)↑2.8%prior 36
Other (explain in narrative): Other31 (7.7%)↑29.2%prior 24
FTYROW: From stop sign26 (6.5%)↑116.7%prior 12
Ran off road - left20 (5%)↓-35.5%prior 31
Followed too close13 (3.2%)↑85.7%prior 7
Driver Distraction: Other interior distraction11 (2.7%)↓-8.3%prior 12
Operating vehicle in an reckless, erratic, careless, negligent manner8 (2%)↑14.3%prior 7

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

Road & Environmental Conditions

The distribution of environmental conditions during crashes shifted between the two periods, with a greater number of incidents occurring in clear weather and on dry roads in 2019. Crashes in clear weather increased from 125 to 163, while those on dry road surfaces rose from 156 to 174. The proportion of crashes occurring in daylight remained relatively stable, accounting for 209 incidents in 2019 compared to 200 in the prior year.

Weather

Clear163 (50.6%)
↑30.4%prior 125
Cloudy67 (20.8%)
↓-28.7%prior 94
Snow24 (7.5%)
↓-25.0%prior 32
Blowing Snow22 (6.8%)
↑10.0%prior 20
Rain19 (5.9%)
↑18.8%prior 16
Freezing rain/drizzle12 (3.7%)
↓-20.0%prior 15
Severe Winds5 (1.6%)
→0.0%prior 5
Sleet, hail4 (1.2%)
↓-33.3%prior 6
Fog, smoke, smog4 (1.2%)
↓-33.3%prior 6
Other (explain in narrative)2 (0.6%)

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

Lighting

Daylight209 (64.3%)
↑4.5%prior 200
Dark - roadway not lighted73 (22.5%)
↑15.9%prior 63
Dark - roadway lighted19 (5.8%)
↓-36.7%prior 30
Dawn13 (4.0%)
↑8.3%prior 12
Dusk9 (2.8%)
↓-30.8%prior 13
Dark - unknown roadway lighting2 (0.6%)

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

Road Surface

Dry174 (53.7%)
↑11.5%prior 156
Ice/frost51 (15.7%)
→0.0%prior 51
Snow44 (13.6%)
↓-8.3%prior 48
Wet42 (13.0%)
↑10.5%prior 38
Gravel5 (1.5%)
Slush4 (1.2%)
↓-78.9%prior 19
Other (explain in narrative)2 (0.6%)
Mud, dirt2 (0.6%)

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

Vehicles & Demographics

Analysis of vehicles and persons involved shows consistent patterns with some demographic shifts. Chevrolet and Ford remained the most common vehicle makes involved in crashes in both years, with both seeing an increase in total count in 2019. The age distribution of persons involved in crashes changed, with a notable increase in the representation of older individuals; the 55-64 age group grew from 65 to 111 persons, and the 65+ group increased from 59 to 93. Conversely, the 21-25 age group saw a decrease in involvement from 102 to 78 individuals.

Top Vehicle Makes (578 vehicles)

1
CHEV91 (15.7%)
↑11.0%prior 82
2
FORD90 (15.6%)
↑9.8%prior 82
3
CHEVROLET51 (8.8%)
↑34.2%prior 38
4
DODG28 (4.8%)
↑16.7%prior 24
5
GMC21 (3.6%)
↑40.0%prior 15
6
TOYT21 (3.6%)
↓-27.6%prior 29
7
BUIC18 (3.1%)
↑28.6%prior 14
8
JEEP17 (2.9%)
↓-5.6%prior 18
9
DODGE14 (2.4%)
↓-22.2%prior 18
10
FREIGHTLINER14 (2.4%)
↓-6.7%prior 15

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

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

Sex Distribution (520 persons with recorded sex)

Male329 (63.3%)
↑33.2%prior 247
Female191 (36.7%)
↑16.5%prior 164

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: 401
  • Total persons involved: 817
  • Total vehicles involved: 578

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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