Yearly Traffic Safety Analysis

153 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Palo Alto County recorded 153 total vehicle crashes, a 12.5% increase from the 136 crashes documented in 2018. Despite the rise in total incidents, the number of fatalities decreased from two in the prior year to one in the current year. The most notable shift was a significant increase in crashes attributed to drivers losing control, which more than doubled from 8 to 21 incidents.

153

▲ 12.5%was 136

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

55

▲ 1.9%was 54

Persons Injured

1

▼ -50.0%was 2

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 crashes in Palo Alto County increased by 12.5% in 2019, rising to 153 incidents from 136 in 2018. However, the severity of these incidents lessened, with total fatalities dropping by half from two to one. The number of injuries remained stable, with 55 in 2019 compared to 54 in the previous year.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 1→0.0%

55

Motorists Injured

Prior: 52↑5.8%

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, Friday became the most frequent day for crashes with 28 incidents, a change from 2018 when Tuesday was the peak day with 29 incidents. The peak hour for collisions also moved earlier, from 9 p.m. in 2018 (10 crashes) to 6 p.m. in 2019 (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

While total crashes increased, their overall severity decreased in 2019. The number of fatal crashes was halved, from two in 2018 to one in 2019, with fatalities also dropping from two to one. The proportion of crashes involving any level of injury (serious, minor, or possible) fell from 29.4% of all crashes in 2018 to 25.5% in 2019. Consequently, crashes resulting in no injuries increased from 69.1% to 73.9% of the total.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
↓-50.0%prior 2
Serious Injury3serious injury crashes2%
↓-40.0%prior 5
Minor Injury18minor injury crashes11.8%
↑5.9%prior 17
Possible Injury18possible injury crashes11.8%
→0.0%prior 18
No Injury113no injury crashes73.9%
↑20.2%prior 94

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 increasing by 18.4% from 38 incidents in 2018 to 45 in 2019. The most significant year-over-year change was in crashes attributed to 'Lost Control,' which saw a 162.5% increase in count from 8 to 21, becoming the second-most common factor in 2019. Conversely, crashes due to failure to yield at an uncontrolled intersection decreased from 9 in 2018 to 4 in 2019.

Officer-Reported Primary Contributing Cause

Animal45 (29.4%)↑18.4%prior 38
Lost Control21 (13.7%)↑162.5%prior 8
Driving too fast for conditions15 (9.8%)↑25.0%prior 12
Ran off road - straight10 (6.5%)→0.0%prior 10
Driver Distraction: Other interior distraction6 (3.9%)
FTYROW: From driveway6 (3.9%)
FTYROW: From stop sign5 (3.3%)
Other (explain in narrative): Other5 (3.3%)
Ran off road - left5 (3.3%)
FTYROW: At uncontrolled intersection4 (2.6%)↓-55.6%prior 9

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 distribution of crashes across environmental conditions remained largely consistent between 2018 and 2019. In both periods, a majority of crashes occurred during daylight hours (46.4% in 2019 vs. 44.9% in 2018) and on dry road surfaces (41.8% in 2019 vs. 43.4% in 2018). There was no notable year-over-year shift in the proportion of crashes occurring in adverse weather, lighting, or road surface conditions.

Weather

Clear63 (57.8%)
↑3.3%prior 61
Cloudy26 (23.9%)
↑4.0%prior 25
Snow7 (6.4%)
↓-36.4%prior 11
Freezing rain/drizzle4 (3.7%)
Rain4 (3.7%)
Blowing Snow3 (2.8%)
Sleet, hail2 (1.8%)

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

Lighting

Daylight71 (62.8%)
↑16.4%prior 61
Dark - roadway not lighted27 (23.9%)
↑17.4%prior 23
Dark - roadway lighted8 (7.1%)
↓-27.3%prior 11
Dawn4 (3.5%)
↓-20.0%prior 5
Dusk3 (2.7%)
↓-40.0%prior 5

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

Road Surface

Dry64 (57.1%)
↑8.5%prior 59
Snow16 (14.3%)
↓-15.8%prior 19
Ice/frost16 (14.3%)
↑60.0%prior 10
Gravel7 (6.3%)
→0.0%prior 7
Wet5 (4.5%)
↓-28.6%prior 7
Slush3 (2.7%)
Mud, dirt1 (0.9%)

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

Vehicles & Demographics

Ford and Chevrolet remained the top two vehicle makes involved in crashes for both years, with the number of Chevrolet vehicles increasing from 33 to 53. An analysis of persons involved in crashes shows a demographic shift toward younger individuals. The share of persons aged 16-25 increased from 20.4% of all persons involved in 2018 to 25.9% in 2019. Conversely, the proportion of persons aged 65 and older decreased from 16.6% to 9.9%.

Top Vehicle Makes (208 vehicles)

1
FORD44 (21.2%)
↑10.0%prior 40
2
CHEV36 (17.3%)
↑33.3%prior 27
3
CHEVROLET17 (8.2%)
↑183.3%prior 6
4
TOYT9 (4.3%)
↑28.6%prior 7
5
CHRY9 (4.3%)
6
GMC9 (4.3%)
↓-18.2%prior 11
7
PONT7 (3.4%)
↑40.0%prior 5
8
BUIC6 (2.9%)
↓-14.3%prior 7
9
PETERBILT5 (2.4%)
10
TOYOTA5 (2.4%)

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

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

Sex Distribution (195 persons with recorded sex)

Male123 (63.1%)
↑38.2%prior 89
Female72 (36.9%)
↑44.0%prior 50

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: 153
  • Total persons involved: 313
  • Total vehicles involved: 208

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

ThatCarHitMe.com · An Injuria.ai Company