Yearly Traffic Safety Analysis

409 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Bremer County recorded 409 total traffic crashes, a 14.6% increase from the 357 crashes documented in 2018. This rise was accompanied by an increase in fatalities from 3 to 4 year-over-year. A notable change was the increase in crashes attributed to animal-related incidents, which grew in count from 129 in 2018 to 164 in 2019.

409

▲ 14.6%was 357

Total Crash Events

4

▲ 33.3%was 3

Persons Killed

82

▼ -3.5%was 85

Persons Injured

4

▲ 33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 trends in Bremer County showed a notable increase from 2018 to 2019. The total number of crashes rose by 52, from 357 to 409, representing a 14.6% year-over-year increase. While the total number of injuries remained stable, decreasing slightly from 85 to 82, the number of fatalities increased from 3 to 4.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 3→0.0%

0

Pedestrians Injured

Prior: 0→0.0%

2

Cyclists Injured

Prior: 0→%

80

Motorists Injured

Prior: 85↓-5.9%

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 temporal patterns of crashes remained largely consistent between the two periods. Monday continued to be the peak day for crashes, with counts rising from 67 in 2018 to 72 in 2019. The 5 p.m. hour remained the peak time for incidents, but the volume of crashes during this hour increased significantly, from 29 in 2018 to 42 in 2019. Additionally, crashes during the 6 a.m. hour saw a substantial increase from 10 to 28 incidents year-over-year.

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 shifted slightly in 2019. The number of fatal crashes increased from 3 to 4, and the corresponding fatal crash rate rose from 0.84% to 0.98%. While the count of serious injury crashes remained stable at 5 incidents in both years, their share of total crashes decreased from 1.4% to 1.2%. The proportion of crashes resulting in no injuries grew from 80.7% (288 crashes) in 2018 to 83.1% (340 crashes) in 2019.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1%
↑33.3%prior 3
Serious Injury5serious injury crashes1.2%
→0.0%prior 5
Minor Injury25minor injury crashes6.1%
↓-7.4%prior 27
Possible Injury35possible injury crashes8.6%
↑2.9%prior 34
No Injury340no injury crashes83.1%
↑18.1%prior 288

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 top contributing factor in both years, with the count of these incidents increasing by 27.1% from 129 in 2018 to 164 in 2019. 'Followed too close' also saw a notable increase in count, rising 34.6% from 26 to 35 crashes. Conversely, crashes attributed to 'Lost Control' decreased in count by 32%, from 25 incidents in 2018 to 17 in 2019. The factor 'Ran off road - straight' grew in count from 14 to 24 crashes year-over-year.

Officer-Reported Primary Contributing Cause

Animal164 (40.1%)↑27.1%prior 129
Followed too close35 (8.6%)↑34.6%prior 26
Ran off road - straight24 (5.9%)↑71.4%prior 14
Driving too fast for conditions21 (5.1%)↑31.3%prior 16
Lost Control17 (4.2%)↓-32.0%prior 25
FTYROW: From stop sign17 (4.2%)↓-5.6%prior 18
Other (explain in narrative): Other16 (3.9%)↑14.3%prior 14
Ran off road - left13 (3.2%)↓-13.3%prior 15
FTYROW: Making left turn10 (2.4%)↓-16.7%prior 12
Driver Distraction: Other interior distraction8 (2%)↑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 occurring under adverse conditions saw a marked increase in 2019 compared to 2018. The number of incidents on adverse road surfaces (snow, ice, wet, or slush) grew by 35.8%, from 81 to 110 crashes. Similarly, crashes in dark conditions increased by 21% from 57 to 69 incidents. While clear weather and dry roads still accounted for the majority of crashes, their counts did not increase at the same rate as incidents in adverse conditions.

Weather

Clear152 (58.5%)
↑14.3%prior 133
Cloudy51 (19.6%)
↓-19.0%prior 63
Rain17 (6.5%)
↑70.0%prior 10
Snow15 (5.8%)
↓-11.8%prior 17
Blowing Snow14 (5.4%)
↑55.6%prior 9
Freezing rain/drizzle9 (3.5%)
↑28.6%prior 7
Severe Winds1 (0.4%)
Fog, smoke, smog1 (0.4%)

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

Lighting

Daylight182 (70.0%)
↑4.0%prior 175
Dark - roadway not lighted50 (19.2%)
↑16.3%prior 43
Dark - roadway lighted19 (7.3%)
↑35.7%prior 14
Dawn5 (1.9%)
Dusk4 (1.5%)
↓-20.0%prior 5

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

Road Surface

Dry144 (55.4%)
↓-4.0%prior 150
Snow41 (15.8%)
↑51.9%prior 27
Ice/frost35 (13.5%)
↑94.4%prior 18
Wet26 (10.0%)
↓-10.3%prior 29
Slush8 (3.1%)
↑14.3%prior 7
Gravel5 (1.9%)
↓-50.0%prior 10
Other (explain in narrative)1 (0.4%)

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

Vehicles & Demographics

The demographics of persons involved in crashes shifted towards older age groups in 2019. The number of persons aged 45-54 involved in crashes increased by 72.4% (from 76 to 131), and those aged 55-64 increased by 68% (from 75 to 126). The top vehicle makes involved in crashes remained consistent, with Ford and Chevrolet models being the most frequent in both years. The count of Fords involved in crashes rose from 85 in 2018 to 117 in 2019.

Top Vehicle Makes (583 vehicles)

1
FORD117 (20.1%)
↑37.6%prior 85
2
CHEV115 (19.7%)
↑23.7%prior 93
3
TOYT27 (4.6%)
↓-22.9%prior 35
4
CHEVROLET26 (4.5%)
↓-36.6%prior 41
5
DODG23 (3.9%)
↑15.0%prior 20
6
CHRY19 (3.3%)
↑18.8%prior 16
7
GMC19 (3.3%)
↑18.8%prior 16
8
PONT18 (3.1%)
↑38.5%prior 13
9
HOND18 (3.1%)
↑38.5%prior 13
10
JEEP17 (2.9%)
↓-5.6%prior 18

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 (548 persons with recorded sex)

Male325 (59.3%)
↑44.4%prior 225
Female223 (40.7%)
↑31.2%prior 170

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: 409
  • Total persons involved: 843
  • Total vehicles involved: 583

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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