Yearly Traffic Safety Analysis

116 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Ida County recorded 116 total vehicle crashes, a 14.1% decrease from the 135 crashes documented in 2019. This overall reduction was accompanied by a drop in both fatalities, from three to one, and total injuries, from 58 to 37. The most significant year-over-year shift was a 31.8% decrease in the count of crashes attributed to animals, which fell from 44 to 30 incidents.

116

▼ -14.1%was 135

Total Crash Events

1

▼ -66.7%was 3

Persons Killed

37

▼ -36.2%was 58

Persons Injured

1

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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Ida County showed a notable downward trend from 2019 to 2020. The total number of crashes decreased by 14.1%, falling from 135 to 116. This positive trend extended to crash outcomes, with total injuries declining by 36.2% (from 58 to 37) and fatalities decreasing from three to one.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 3↓-66.7%

1

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

35

Motorists Injured

Prior: 58↓-39.7%

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 remained relatively stable year-over-year. Thursday was the peak day for crashes in both 2020 (26 crashes) and 2019 (27 crashes). The peak hour for collisions shifted slightly, moving from 5 p.m. in 2019 (13 crashes) to 6 p.m. in 2020 (10 crashes), with a lower volume of incidents during the busiest hour.

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

Crash severity decreased from 2019 to 2020. The total number of fatalities fell from three to one, while the number of fatal crashes remained constant at one. The proportion of crashes resulting in any form of injury (serious, minor, or possible) declined from 29.6% of all crashes in 2019 to 25.0% in 2020. Specifically, crashes involving serious injuries dropped from seven to five.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.9%
→0.0%prior 1
Serious Injury5serious injury crashes4.3%
↓-28.6%prior 7
Minor Injury6minor injury crashes5.2%
↓-25.0%prior 8
Possible Injury18possible injury crashes15.5%
↓-28.0%prior 25
No Injury86no injury crashes74.1%
↓-8.5%prior 94

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

In both periods, 'Animal' was the leading contributing factor, though its count decreased by 31.8% from 44 crashes in 2019 to 30 in 2020. The second most common factor in 2019, 'FTYROW: From stop sign,' saw a 60% reduction in count, from 10 crashes to just 4 in 2020. Conversely, 'Lost Control' incidents saw a slight increase from 7 to 8, becoming the second-leading factor in 2020, tied with 'Other'.

Officer-Reported Primary Contributing Cause

Animal30 (25.9%)↓-31.8%prior 44
Lost Control8 (6.9%)↑14.3%prior 7
Other (explain in narrative): Other8 (6.9%)↑33.3%prior 6
Driving too fast for conditions7 (6%)↑16.7%prior 6
Improper Backing6 (5.2%)→0.0%prior 6
Ran off road - left6 (5.2%)→0.0%prior 6
Other (explain in narrative): No improper action5 (4.3%)
FTYROW: From stop sign4 (3.4%)↓-60.0%prior 10
Followed too close4 (3.4%)
FTYROW: At uncontrolled intersection3 (2.6%)

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

Road & Environmental Conditions

There was a significant shift in crash conditions year-over-year. Crashes on adverse road surfaces like snow, ice, or wet pavement decreased by 50%, from 36 incidents in 2019 to 18 in 2020. However, crashes occurring in darkness on unlit roadways increased from 16 in 2019 to 22 in 2020. Crashes on dry roads increased slightly from 60 to 67.

Weather

Clear66 (73.3%)
↑10.0%prior 60
Cloudy10 (11.1%)
↓-23.1%prior 13
Snow4 (4.4%)
Rain3 (3.3%)
↓-70.0%prior 10
Blowing Snow2 (2.2%)
↓-60.0%prior 5
Freezing rain/drizzle2 (2.2%)
Severe Winds2 (2.2%)
Fog, smoke, smog1 (1.1%)

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

Lighting

Daylight56 (63.6%)
↓-17.6%prior 68
Dark - roadway not lighted22 (25.0%)
↑37.5%prior 16
Dark - roadway lighted6 (6.8%)
↓-14.3%prior 7
Dusk3 (3.4%)
Dark - unknown roadway lighting1 (1.1%)

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

Road Surface

Dry67 (74.4%)
↑11.7%prior 60
Snow9 (10.0%)
↓-30.8%prior 13
Ice/frost5 (5.6%)
↓-28.6%prior 7
Gravel5 (5.6%)
Wet3 (3.3%)
↓-78.6%prior 14
Mud, dirt1 (1.1%)

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

Vehicles & Demographics

While the top vehicle makes involved in crashes, Ford and Chevrolet, remained consistent, their numbers declined in line with the overall trend. Ford-involved crashes fell from 45 to 31, and Chevrolet-involved crashes decreased from a combined 43 to 40. A notable demographic shift occurred among persons involved in crashes; the number of individuals aged 65 and older dropped from 34 in 2019 to 14 in 2020, and their share of total persons involved was nearly halved from 11.0% to 5.6%.

Top Vehicle Makes (174 vehicles)

1
FORD31 (17.8%)
↓-31.1%prior 45
2
CHEV29 (16.7%)
↓-9.4%prior 32
3
GMC12 (6.9%)
↑20.0%prior 10
4
CHEVROLET11 (6.3%)
→0.0%prior 11
5
DODG8 (4.6%)
↓-20.0%prior 10
6
BUIC7 (4%)
↓-12.5%prior 8
7
TOYT6 (3.4%)
↑20.0%prior 5
8
JEEP5 (2.9%)
9
CHRY4 (2.3%)
10
TOYO4 (2.3%)

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

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

Sex Distribution (159 persons with recorded sex)

Male94 (59.1%)
↓-14.5%prior 110
Female65 (40.9%)
↓-12.2%prior 74

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: 116
  • Total persons involved: 252
  • Total vehicles involved: 174

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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