Yearly Traffic Safety Analysis

142 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Page County recorded 142 total vehicle crashes, a 4.7% decrease from the 149 crashes reported in 2018. While total crashes and injuries (40, down from 43) saw a slight decline and fatalities remained unchanged at 2, the number of crashes involving a driver under the influence doubled from 3 in 2018 to 6 in 2019.

142

▼ -4.7%was 149

Total Crash Events

2

Persons Killed

40

▼ -7.0%was 43

Persons Injured

2

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

Overall, traffic safety metrics in Page County showed a slight improvement from 2018 to 2019. The total number of crashes fell by 4.7%, from 149 to 142. Similarly, the number of people injured in these incidents decreased by 7.0% from 43 to 40, while fatalities held steady at two for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

1

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

38

Motorists Injured

Prior: 43↓-11.6%

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 Page County shifted between 2018 and 2019. The day with the most crashes moved from Tuesday (27 crashes) in 2018 to Friday (28 crashes) in 2019. The peak hour for collisions also changed, moving from the 4 PM hour in the prior year (18 crashes) to the 9 AM hour in the current year (14 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

Crash severity patterns remained broadly similar year-over-year, with two fatal crashes recorded in both 2019 and 2018. The fatal crash rate saw a minor increase from 1.34 to 1.41 per 100 crashes. The proportion of crashes resulting in minor injuries increased from 5.4% in 2018 to 9.9% in 2019, while crashes with possible injuries decreased from 14.1% to 12.7%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.4%
→0.0%prior 2
Serious Injury5serious injury crashes3.5%
→0.0%prior 5
Minor Injury14minor injury crashes9.9%
↑75.0%prior 8
Possible Injury18possible injury crashes12.7%
↓-14.3%prior 21
No Injury103no injury crashes72.5%
↓-8.8%prior 113

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

The leading contributing factors for crashes shifted between 2018 and 2019. Collisions involving an animal became the top factor in 2019, increasing from 17 to 25 incidents. Conversely, 'Ran off road - left', which was the leading cause in 2018 with 20 crashes, saw its count drop to 9 in 2019. 'Driving too fast for conditions' increased from 6 to 10 crashes, while 'Failure to yield from a stop sign' incidents decreased from 14 to 11.

Officer-Reported Primary Contributing Cause

Animal25 (17.6%)↑47.1%prior 17
FTYROW: From stop sign11 (7.7%)↓-21.4%prior 14
Driving too fast for conditions10 (7%)↑66.7%prior 6
Ran off road - left9 (6.3%)↓-55.0%prior 20
Followed too close9 (6.3%)↑28.6%prior 7
Other (explain in narrative): Other7 (4.9%)↑16.7%prior 6
Driver Distraction: Other interior distraction6 (4.2%)↓-50.0%prior 12
FTYROW: At uncontrolled intersection6 (4.2%)
Lost Control5 (3.5%)↓-64.3%prior 14
Made improper turn4 (2.8%)

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 majority of crashes in both years occurred in clear weather on dry roads during daylight hours. However, there was a notable increase in crashes occurring in dark but lighted conditions, which rose from 5 incidents in 2018 to 16 in 2019. Crashes on snowy roads also saw an increase from 15 in the prior year to 19 in the current year, while incidents on wet roads decreased from 17 to 8.

Weather

Clear88 (70.4%)
↑4.8%prior 84
Cloudy22 (17.6%)
↓-29.0%prior 31
Snow7 (5.6%)
↑16.7%prior 6
Rain5 (4.0%)
↓-28.6%prior 7
Other (explain in narrative)1 (0.8%)
Freezing rain/drizzle1 (0.8%)
Blowing Snow1 (0.8%)

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

Lighting

Daylight89 (70.6%)
↓-12.7%prior 102
Dark - roadway not lighted17 (13.5%)
↓-10.5%prior 19
Dark - roadway lighted16 (12.7%)
↑220.0%prior 5
Dark - unknown roadway lighting3 (2.4%)
Dawn1 (0.8%)

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

Road Surface

Dry86 (69.4%)
↑1.2%prior 85
Snow19 (15.3%)
↑26.7%prior 15
Wet8 (6.5%)
↓-52.9%prior 17
Ice/frost7 (5.6%)
↓-36.4%prior 11
Gravel3 (2.4%)
Slush1 (0.8%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Chevrolet, Ford, and Dodge being the top three in both 2019 and 2018. An analysis of persons involved shows a notable demographic shift. The number of individuals aged 16-20 involved in crashes decreased from 56 in 2018 to 30 in 2019. In contrast, the involvement of persons aged 45-54 more than doubled from 17 to 37, and the 65+ age group also saw an increase from 37 to 49 individuals.

Top Vehicle Makes (234 vehicles)

1
CHEV43 (18.4%)
↑19.4%prior 36
2
FORD37 (15.8%)
↑5.7%prior 35
3
CHEVROLET24 (10.3%)
↓-20.0%prior 30
4
DODG14 (6%)
↓-17.6%prior 17
5
DODGE14 (6%)
6
JEEP8 (3.4%)
↑60.0%prior 5
7
NR7 (3%)
↑40.0%prior 5
8
PONT7 (3%)
→0.0%prior 7
9
TOYOTA6 (2.6%)
↑20.0%prior 5
10
BUICK5 (2.1%)

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

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

Sex Distribution (197 persons with recorded sex)

Male125 (63.5%)
↑22.5%prior 102
Female72 (36.5%)
↓-13.3%prior 83

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: 142
  • Total persons involved: 324
  • Total vehicles involved: 234

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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