Yearly Traffic Safety Analysis

282 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Tama County, total vehicle crashes decreased by 4.1% from 294 in 2018 to 282 in 2019. While the number of crashes and related fatalities fell, the total number of injuries saw a significant year-over-year increase. The most notable shift was a 39.1% rise in persons injured, from 69 in the prior period to 96 in the current period, even as fatalities dropped from 3 to 1.

282

▼ -4.1%was 294

Total Crash Events

1

▼ -66.7%was 3

Persons Killed

96

▲ 39.1%was 69

Persons Injured

1

▼ -66.7%was 3

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 traffic collisions in Tama County showed a slight downward trend, with 12 fewer crashes in 2019 compared to 2018, a 4.1% reduction. This decrease was accompanied by a positive drop in fatalities from 3 to 1. However, this trend was contrasted by a substantial 39.1% increase in the number of people injured, which rose from 69 to 96 year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 3↓-66.7%

1

Pedestrians Injured

Prior: 1→0.0%

95

Motorists Injured

Prior: 68↑39.7%

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 2018, the peak day for crashes was Monday with 52 incidents, whereas in 2019, Friday and Sunday shared the peak with 45 crashes each. The peak hour also moved later into the evening, from the 5 p.m. hour (18 crashes) in 2018 to the 9 p.m. hour (24 crashes) 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 patterns changed notably year-over-year. The number of fatal crashes fell from 3 in 2018 to 1 in 2019, and the total number of fatalities decreased from 3 to 1. In contrast, the volume of crashes resulting in injury increased, with the number of persons injured climbing from 69 to 96. The proportion of crashes involving a minor injury grew from 6.8% to 10.6%, and those with possible injuries increased from 8.2% to 12.4% of all crashes.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
↓-66.7%prior 3
Serious Injury10serious injury crashes3.5%
↓-28.6%prior 14
Minor Injury30minor injury crashes10.6%
↑50.0%prior 20
Possible Injury35possible injury crashes12.4%
↑45.8%prior 24
No Injury206no injury crashes73%
↓-11.6%prior 233

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 with animals remained the leading contributing factor in both periods, though the count decreased by 12.3% from 122 crashes in 2018 to 107 in 2019. While most top factors like 'Lost Control' and 'Driving too fast for conditions' saw declines, some factors increased. Crashes attributed to 'Failure to Yield Right of Way from a stop sign' doubled in count from 6 to 12, and incidents of 'Ran off road - straight' increased by 40% from 10 to 14.

Officer-Reported Primary Contributing Cause

Animal107 (37.9%)↓-12.3%prior 122
Lost Control21 (7.4%)↓-12.5%prior 24
Driving too fast for conditions15 (5.3%)↓-28.6%prior 21
Ran off road - straight14 (5%)↑40.0%prior 10
Other (explain in narrative): Other13 (4.6%)↓-31.6%prior 19
FTYROW: From stop sign12 (4.3%)↑100.0%prior 6
Ran off road - left11 (3.9%)↑22.2%prior 9
Operating vehicle in an reckless, erratic, careless, negligent manner10 (3.5%)↑11.1%prior 9
Driver Distraction: Other interior distraction9 (3.2%)→0.0%prior 9
Driver Distraction: Exterior distraction6 (2.1%)↑20.0%prior 5

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

Road & Environmental Conditions

There was a shift toward crashes occurring in more favorable conditions year-over-year. The number of crashes on dry road surfaces increased from 128 to 152, and those in clear weather rose from 123 to 143. Conversely, incidents on roads affected by snow, ice, or slush decreased from 42 in 2018 to 33 in 2019. Crashes in darkness on unlighted roadways saw an increase, rising from 52 incidents to 62.

Weather

Clear143 (65.6%)
↑16.3%prior 123
Cloudy42 (19.3%)
↑31.3%prior 32
Rain10 (4.6%)
↑66.7%prior 6
Blowing Snow9 (4.1%)
Snow7 (3.2%)
↓-56.3%prior 16
Severe Winds3 (1.4%)
Fog, smoke, smog1 (0.5%)
↓-80.0%prior 5
Freezing rain/drizzle1 (0.5%)
↓-87.5%prior 8
Other (explain in narrative)1 (0.5%)
Sleet, hail1 (0.5%)

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

Lighting

Daylight127 (58.0%)
↑8.5%prior 117
Dark - roadway not lighted62 (28.3%)
↑19.2%prior 52
Dark - roadway lighted14 (6.4%)
↓-22.2%prior 18
Dusk9 (4.1%)
Dark - unknown roadway lighting4 (1.8%)
Dawn3 (1.4%)
↓-40.0%prior 5

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

Road Surface

Dry152 (69.7%)
↑18.8%prior 128
Wet19 (8.7%)
↑26.7%prior 15
Snow18 (8.3%)
↓-5.3%prior 19
Gravel13 (6.0%)
↑30.0%prior 10
Ice/frost12 (5.5%)
↓-14.3%prior 14
Slush3 (1.4%)
↓-66.7%prior 9
Mud, dirt1 (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, with Chevrolet (68 vehicles) and Ford (63 vehicles) leading in 2019, similar to their top rankings in 2018. The age demographics of persons involved in crashes showed a notable shift. The number of individuals in the 45-54 age group increased from 63 to 92, making it the largest group in 2019. The 16-20 age group also saw a significant increase in involvement, rising from 55 persons in 2018 to 75 in 2019.

Top Vehicle Makes (388 vehicles)

1
CHEV68 (17.5%)
↑3.0%prior 66
2
FORD63 (16.2%)
↑5.0%prior 60
3
DODG26 (6.7%)
↑8.3%prior 24
4
CHEVROLET17 (4.4%)
↓-15.0%prior 20
5
HOND14 (3.6%)
↑55.6%prior 9
6
JEEP13 (3.4%)
→0.0%prior 13
7
TOYT13 (3.4%)
↓-45.8%prior 24
8
GMC12 (3.1%)
↓-7.7%prior 13
9
CHRY12 (3.1%)
↓-29.4%prior 17
10
DODGE10 (2.6%)
↓-23.1%prior 13

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

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

Sex Distribution (356 persons with recorded sex)

Male211 (59.3%)
↑23.4%prior 171
Female145 (40.7%)
↑25.0%prior 116

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: 282
  • Total persons involved: 575
  • Total vehicles involved: 388

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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