Yearly Traffic Safety Analysis

218 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Cass County recorded 218 total crashes, an 11% decrease from the 245 crashes documented in 2018. This overall reduction in collisions was accompanied by a significant drop in severity, with total fatalities falling from 5 to 2 and total injuries decreasing from 97 to 74 year-over-year.

218

▼ -11.0%was 245

Total Crash Events

2

▼ -60.0%was 5

Persons Killed

74

▼ -23.7%was 97

Persons Injured

2

▼ -60.0%was 5

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

Crash data for Cass County indicates a downward trend from 2018 to 2019. Total crashes fell by 11%, from 245 to 218. This positive trend extended to crash outcomes, with a 60% reduction in fatalities (from 5 to 2) and a 24% reduction in injuries (from 97 to 74).

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 1↓-100.0%

2

Motorists Killed

Prior: 4↓-50.0%

1

Pedestrians Injured

Prior: 0→%

3

Cyclists Injured

Prior: 0→%

70

Motorists Injured

Prior: 97↓-27.8%

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 shifted between the two periods. In 2019, the peak day for crashes was Saturday with 40 incidents, a change from 2018 when Friday was the peak day with 58 incidents. The peak hour also moved from the 8 a.m. morning hour in 2018 (21 crashes) to the 2 p.m. afternoon hour in 2019 (19 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 decreased from 2018 to 2019. Fatal crashes dropped from 5 to 2, with their share of all crashes falling from 2.0% to 0.9%. The number of serious injury crashes also declined from 8 to 5. Correspondingly, the proportion of crashes resulting in no injury increased from 69.4% in 2018 to 74.3% in 2019.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.9%
↓-60.0%prior 5
Serious Injury5serious injury crashes2.3%
↓-37.5%prior 8
Minor Injury28minor injury crashes12.8%
↑12.0%prior 25
Possible Injury21possible injury crashes9.6%
↓-43.2%prior 37
No Injury162no injury crashes74.3%
↓-4.7%prior 170

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

While "Animal" remained the top contributing factor in 2019 with 38 crashes, this was a decrease from 45 in the prior year. The most significant change was in crashes due to "Lost Control," which fell by 41% from 44 incidents in 2018 to 26 in 2019. In contrast, incidents where a driver "Ran off road - straight" increased in count from 18 to 22 year-over-year.

Officer-Reported Primary Contributing Cause

Animal38 (17.4%)↓-15.6%prior 45
Lost Control26 (11.9%)↓-40.9%prior 44
Ran off road - straight22 (10.1%)↑22.2%prior 18
FTYROW: From stop sign19 (8.7%)↑11.8%prior 17
Followed too close15 (6.9%)→0.0%prior 15
Ran off road - left14 (6.4%)↑27.3%prior 11
Driving too fast for conditions14 (6.4%)↓-17.6%prior 17
Other (explain in narrative): Other8 (3.7%)↓-27.3%prior 11
Ran Stop Sign6 (2.8%)
Driver Distraction: Other interior distraction4 (1.8%)↓-33.3%prior 6

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

Road & Environmental Conditions

Crashes under adverse conditions were less frequent in 2019 than in 2018. Collisions occurring in rain or snow dropped from a combined 50 incidents to 21. Similarly, crashes on wet, icy, or snow-covered road surfaces decreased from a total of 86 to 55. Consequently, the proportion of crashes happening in clear weather and on dry roads was higher in 2019.

Weather

Clear111 (58.7%)
↓-5.1%prior 117
Cloudy39 (20.6%)
↑25.8%prior 31
Snow12 (6.3%)
↓-57.1%prior 28
Blowing Snow11 (5.8%)
↑57.1%prior 7
Rain9 (4.8%)
↓-59.1%prior 22
Freezing rain/drizzle6 (3.2%)
↓-14.3%prior 7
Fog, smoke, smog1 (0.5%)

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

Lighting

Daylight120 (62.8%)
↓-11.8%prior 136
Dark - roadway not lighted43 (22.5%)
↓-15.7%prior 51
Dark - roadway lighted19 (9.9%)
↑46.2%prior 13
Dawn6 (3.1%)
↓-14.3%prior 7
Dusk2 (1.0%)
↓-75.0%prior 8
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry131 (68.9%)
↑6.5%prior 123
Ice/frost19 (10.0%)
↓-24.0%prior 25
Wet18 (9.5%)
↓-53.8%prior 39
Snow18 (9.5%)
↓-18.2%prior 22
Gravel3 (1.6%)
Slush1 (0.5%)

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 remained consistent, though their counts shifted; Ford vehicles increased from 59 to 69, while Chevrolet vehicles (all variations) decreased from 80 to 56. A notable demographic shift occurred among persons aged 16-20, whose involvement in crashes fell by 56%, from 57 individuals in 2018 to 25 in 2019.

Top Vehicle Makes (334 vehicles)

1
FORD69 (20.7%)
↑16.9%prior 59
2
CHEV38 (11.4%)
↓-28.3%prior 53
3
DODG19 (5.7%)
→0.0%prior 19
4
CHEVROLET18 (5.4%)
↓-33.3%prior 27
5
JEEP17 (5.1%)
↑142.9%prior 7
6
BUIC13 (3.9%)
↑44.4%prior 9
7
FREIGHTLINER10 (3%)
↓-28.6%prior 14
8
DODGE10 (3%)
↑42.9%prior 7
9
GMC9 (2.7%)
↑12.5%prior 8
10
INTERNATIONA8 (2.4%)

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

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

Sex Distribution (304 persons with recorded sex)

Male194 (63.8%)
↑9.0%prior 178
Female110 (36.2%)
↓-2.7%prior 113

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: 218
  • Total persons involved: 464
  • Total vehicles involved: 334

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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