Yearly Traffic Safety Analysis

113 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Palo Alto County recorded 113 total crashes, a 26.1% decrease from the 153 crashes reported in 2019. While total crashes and injuries declined from 55 to 41, the number of fatalities increased from one in 2019 to two in 2020. The most significant change was the overall reduction in collision events across the county, particularly those attributed to loss of control.

113

▼ -26.1%was 153

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

41

▼ -25.5%was 55

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (2) 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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Palo Alto County showed a downward trend from 2019 to 2020. The total number of crashes decreased by 26.1%, from 153 to 113. Correspondingly, the number of people injured fell from 55 to 41, though fatalities rose from one to two.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 1↑100.0%

41

Motorists Injured

Prior: 55↓-25.5%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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 2020, the peak days for crashes were Wednesday and Thursday (22 crashes each), a change from Friday (28 crashes) in 2019. The peak hour for collisions also moved later, from 6 p.m. in 2019 (14 crashes) to 8 p.m. in 2020 (13 crashes). Monthly crash distribution also varied, with 2020 seeing its highest volumes in December (21 crashes) and September (16 crashes), whereas 2019's peaks were in October (19 crashes) and February (18 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the total number of crashes decreased, the severity of outcomes showed a mixed change. The number of fatal crashes remained constant at one in both 2019 and 2020, but the number of fatalities doubled from one to two, causing the fatal crash rate per 100 crashes to increase from 0.65 to 0.88. The proportion of crashes resulting in serious injuries rose from 2.0% in 2019 to 3.5% in 2020. Conversely, the share of crashes involving minor injuries decreased from 11.8% to 7.1% over the same period.

Severity is per crash event (most severe injury). 1 fatal crash events resulted in 2 persons killed.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.9%
→0.0%prior 1
Serious Injury4serious injury crashes3.5%
↑33.3%prior 3
Minor Injury8minor injury crashes7.1%
↓-55.6%prior 18
Possible Injury13possible injury crashes11.5%
↓-27.8%prior 18
No Injury87no injury crashes77%
↓-23.0%prior 113

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in both periods, with 44 crashes in 2020 compared to 45 in 2019; however, its share of all factors rose from 29.4% to 38.9%. The most significant year-over-year change was the sharp decline in crashes attributed to 'Lost Control,' which fell by 76.2% from 21 incidents in 2019 to 5 in 2020. Crashes related to 'Driving too fast for conditions' also decreased by 40%, from 15 in 2019 to 9 in 2020.

Officer-Reported Primary Contributing Cause

Animal44 (38.9%)↓-2.2%prior 45
Driving too fast for conditions9 (8%)↓-40.0%prior 15
Ran off road - straight6 (5.3%)↓-40.0%prior 10
Lost Control5 (4.4%)↓-76.2%prior 21
FTYROW: From stop sign4 (3.5%)↓-20.0%prior 5
Driver Distraction: Other interior distraction4 (3.5%)↓-33.3%prior 6
Other (explain in narrative): Other4 (3.5%)↓-20.0%prior 5
Crossed centerline (undivided)3 (2.7%)
Ran off road - left3 (2.7%)↓-40.0%prior 5
Improper Backing3 (2.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes across environmental conditions remained broadly similar year-over-year, tracking the overall decline in total incidents. Crashes on dry roads decreased from 64 in 2019 to 46 in 2020, and those in daylight fell from 71 to 42. A notable change occurred in crashes on snow-covered roads, which saw a substantial drop from 16 incidents in 2019 to just 3 in 2020. Similarly, collisions on icy or frosty roads declined from 16 to 10.

Weather

Clear40 (59.7%)
↓-36.5%prior 63
Cloudy11 (16.4%)
↓-57.7%prior 26
Rain5 (7.5%)
Blowing Snow4 (6.0%)
Fog, smoke, smog2 (3.0%)
Snow2 (3.0%)
↓-71.4%prior 7
Freezing rain/drizzle2 (3.0%)
Severe Winds1 (1.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight42 (59.2%)
↓-40.8%prior 71
Dark - roadway not lighted21 (29.6%)
↓-22.2%prior 27
Dark - roadway lighted4 (5.6%)
↓-50.0%prior 8
Dawn3 (4.2%)
Dusk1 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry46 (66.7%)
↓-28.1%prior 64
Ice/frost10 (14.5%)
↓-37.5%prior 16
Gravel5 (7.2%)
↓-28.6%prior 7
Wet5 (7.2%)
→0.0%prior 5
Snow3 (4.3%)
↓-81.3%prior 16

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet remained the most common vehicle makes involved in crashes in both periods, with their counts decreasing from 2019 to 2020 in line with the overall trend. An analysis of persons involved in crashes reveals a significant demographic shift. The number of individuals in the 16-20 age group involved in crashes dropped sharply from 52 in 2019 to 21 in 2020. Conversely, involvement for the 26-34 age group increased from 33 individuals to 43 over the same period.

Top Vehicle Makes (152 vehicles)

1
FORD30 (19.7%)
↓-31.8%prior 44
2
CHEV23 (15.1%)
↓-36.1%prior 36
3
CHEVROLET14 (9.2%)
↓-17.6%prior 17
4
TOYT11 (7.2%)
↑22.2%prior 9
5
GMC7 (4.6%)
↓-22.2%prior 9
6
CHRY7 (4.6%)
↓-22.2%prior 9
7
JEEP7 (4.6%)
8
DODGE5 (3.3%)
9
BUIC5 (3.3%)
↓-16.7%prior 6
10
DODG4 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

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

Sex Distribution (143 persons with recorded sex)

Male90 (62.9%)
↓-26.8%prior 123
Female53 (37.1%)
↓-26.4%prior 72

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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: 2020-01-01 through 2020-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 113
  • Total persons involved: 244
  • Total vehicles involved: 152

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: 2020." Published September 9, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2020-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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