Yearly Traffic Safety Analysis

8 CRASHES IN
ARMSTRONG, IA
2019

All metrics benchmarked against2018

In 2019, ARMSTRONG experienced 8 total crashes, a 100% increase compared to the 4 crashes reported in 2018. Total injuries rose from 0 in 2018 to 1 in 2019. The most notable shift was the doubling of total crashes year-over-year, alongside an emergence of DUI-related incidents, which accounted for 2 crashes in 2019 compared to none in 2018.

8

▲ 100.0%was 4

Total Crash Events

0

Persons Killed

1

Persons Injured

0

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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 indicates a significant increase in crash activity, with total crashes rising from 4 in 2018 to 8 in 2019. This represents a 100% increase in the number of crashes year-over-year. Additionally, total injuries increased from 0 in 2018 to 1 in 2019.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 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 temporal patterns of crashes shifted between the two periods. In 2018, the peak day for crashes was Tuesday with 2 incidents, and the peak hour was 3 PM with 2 incidents. In 2019, the peak day shifted, with Sunday, Monday, and Wednesday each recording 2 crashes, and the peak hour moved to 12 AM, also with 2 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

Outcome by Severity (Crash Events)

Possible Injury1possible injury crashes12.5%
No Injury7no injury crashes87.5%

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

Contributing factors saw a complete change in prevalence year-over-year. Factors such as 'FTYROW: From parked position' (2 crashes), 'FTYROW: At uncontrolled intersection' (1 crash), and 'FTYROW: Making left turn' (1 crash) were present in 2018 but not recorded in 2019. Conversely, 2019 saw the emergence of new top factors, each contributing 1 crash, including 'Driver Distraction: Other interior distraction', 'Failed to keep in proper lane', and 'Improper Backing'.

Officer-Reported Primary Contributing Cause

Driver Distraction: Other interior distraction1 (12.5%)
Failed to keep in proper lane1 (12.5%)
FTYROW: From driveway1 (12.5%)
Illegally Parked/Unattended1 (12.5%)
Improper Backing1 (12.5%)
Operating vehicle in an reckless, erratic, careless, negligent manner1 (12.5%)
Other (explain in narrative): Other1 (12.5%)

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

Road & Environmental Conditions

Crash conditions showed shifts across weather, lighting, and road surface. Crashes occurring in clear weather increased from 2 in 2018 to 6 in 2019, while crashes on dry road surfaces increased from 2 to 5. Regarding lighting, daylight crashes decreased from 3 in 2018 to 2 in 2019, but crashes in 'Dark - roadway not lighted' increased from 0 to 3, indicating a shift towards more nighttime incidents.

Weather

Clear6 (85.7%)
Snow1 (14.3%)

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

Lighting

Dark - roadway not lighted3 (37.5%)
Daylight2 (25.0%)
Dark - roadway lighted1 (12.5%)
Dark - unknown roadway lighting1 (12.5%)
Dusk1 (12.5%)

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

Road Surface

Dry5 (62.5%)
Snow2 (25.0%)
Ice/frost1 (12.5%)

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

Vehicles & Demographics

Top Vehicle Makes (15 vehicles)

1
FORD4 (26.7%)
2
CHEVROLET2 (13.3%)
3
FORE1 (6.7%)
4
FRHT1 (6.7%)
5
JEEP1 (6.7%)
6
NISS1 (6.7%)
7
PONT1 (6.7%)
8
PONTIAC1 (6.7%)
9
TOYO1 (6.7%)
10
VOLK1 (6.7%)

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

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

Sex Distribution (8 persons with recorded sex)

Female4 (50.0%)
→0.0%prior 4
Male4 (50.0%)
↑33.3%prior 3

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: ARMSTRONG, IA
  • Total crash records analyzed: 8
  • Total persons involved: 23
  • Total vehicles involved: 15

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). "ARMSTRONG, 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/armstrong/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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