Yearly Traffic Safety Analysis

170 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Grundy County recorded 170 total crashes, a 29.8% decrease from the 242 crashes reported in 2019. This overall reduction in incidents was the most notable year-over-year shift. While total crashes, injuries (60 in 2020 vs. 72 in 2019), and fatalities (2 vs. 3) all declined, the number of fatal crashes remained constant at 2 for both years.

170

▼ -29.8%was 242

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

60

▼ -16.7%was 72

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

Trend Summary

Crash data for Grundy County indicates a significant downward trend year-over-year. Total crashes fell by 29.8%, from 242 in 2019 to 170 in 2020. This decline was accompanied by a 16.7% reduction in total injuries (from 72 to 60) and a decrease in total fatalities from 3 to 2.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 3↓-33.3%

60

Motorists Injured

Prior: 70↓-14.3%

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

Temporal crash patterns shifted between the two periods. In 2020, the peak days for crashes were Thursday and Friday, each with 31 incidents, a change from 2019 when Tuesday was the peak day with 40 crashes. The peak hour for crashes also moved earlier in the day, shifting from 6 p.m. in 2019 (22 crashes) to 3 p.m. in 2020 (16 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

Although the number of fatal crashes was unchanged at 2, the fatal crash rate increased from 0.83% in 2019 to 1.18% in 2020 because of the lower total number of crashes. The proportion of crashes resulting in serious injuries more than doubled, rising from 1.7% of all crashes (4 incidents) in 2019 to 4.7% (8 incidents) in 2020. Conversely, the share of minor injury crashes decreased from 11.2% to 8.2%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.2%
→0.0%prior 2
Serious Injury8serious injury crashes4.7%
↑100.0%prior 4
Minor Injury14minor injury crashes8.2%
↓-48.1%prior 27
Possible Injury18possible injury crashes10.6%
↓-28.0%prior 25
No Injury128no injury crashes75.3%
↓-30.4%prior 184

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

The leading contributing factors remained consistent year-over-year, though their counts decreased in line with the overall trend. Collisions involving an animal were the top factor in both periods, with the count dropping from 81 crashes in 2019 to 60 in 2020. 'Lost Control' was the second-ranked factor in both years, with its count decreasing from 25 to 17. Crashes attributed to 'Driving too fast for conditions' saw a substantial reduction, falling from 17 incidents in 2019 to 4 in 2020.

Officer-Reported Primary Contributing Cause

Animal60 (35.3%)↓-25.9%prior 81
Lost Control17 (10%)↓-32.0%prior 25
Ran off road - left11 (6.5%)↓-35.3%prior 17
Ran off road - straight9 (5.3%)→0.0%prior 9
Other (explain in narrative): Other6 (3.5%)↓-40.0%prior 10
Operating vehicle in an reckless, erratic, careless, negligent manner5 (2.9%)
Driver Distraction: Other interior distraction5 (2.9%)↓-16.7%prior 6
Ran Stop Sign5 (2.9%)→0.0%prior 5
FTYROW: From stop sign4 (2.4%)↓-55.6%prior 9
Other (explain in narrative): No improper action4 (2.4%)

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

Road & Environmental Conditions

A significant year-over-year change occurred in road surface conditions. While the number of crashes on dry roads was identical at 93 for both years, they represented a larger share of total crashes in 2020 (54.7%) compared to 2019 (38.4%). Crashes on snow or ice-covered roads decreased substantially, from a combined 54 incidents in 2019 to 13 in 2020. Lighting conditions as a percentage of total crashes remained stable, with daylight crashes accounting for 46.5% in 2020 and 47.1% in 2019.

Weather

Clear82 (67.2%)
↓-11.8%prior 93
Cloudy21 (17.2%)
↓-44.7%prior 38
Snow7 (5.7%)
↓-50.0%prior 14
Rain6 (4.9%)
→0.0%prior 6
Blowing Snow2 (1.6%)
↓-81.8%prior 11
Severe Winds2 (1.6%)
Fog, smoke, smog1 (0.8%)
Freezing rain/drizzle1 (0.8%)
↓-83.3%prior 6

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

Lighting

Daylight79 (64.2%)
↓-30.7%prior 114
Dark - roadway not lighted31 (25.2%)
↓-20.5%prior 39
Dawn5 (4.1%)
Dark - roadway lighted4 (3.3%)
↓-63.6%prior 11
Dusk4 (3.3%)
↓-20.0%prior 5

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

Road Surface

Dry93 (76.2%)
→0.0%prior 93
Snow9 (7.4%)
↓-70.0%prior 30
Wet9 (7.4%)
↓-52.6%prior 19
Gravel7 (5.7%)
↑40.0%prior 5
Ice/frost4 (3.3%)
↓-83.3%prior 24

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 the number of vehicles from both manufacturers decreasing in 2020. A notable demographic shift occurred in the age of persons involved; the number of individuals in the 16-20 age group dropped by 50.6%, from 79 in 2019 to 39 in 2020. Reductions were seen across most other age groups, but the change in the youngest driver category was the most pronounced.

Top Vehicle Makes (226 vehicles)

1
FORD45 (19.9%)
↓-33.8%prior 68
2
CHEV43 (19%)
↓-27.1%prior 59
3
CHEVROLET11 (4.9%)
↓-21.4%prior 14
4
TOYO8 (3.5%)
↓-46.7%prior 15
5
DODG8 (3.5%)
↓-42.9%prior 14
6
JEEP7 (3.1%)
↓-36.4%prior 11
7
RAM7 (3.1%)
8
CHRY7 (3.1%)
↓-41.7%prior 12
9
HOND7 (3.1%)
↑40.0%prior 5
10
BUIC6 (2.7%)
→0.0%prior 6

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

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

Sex Distribution (215 persons with recorded sex)

Male129 (60.0%)
↓-33.5%prior 194
Female86 (40.0%)
↓-32.8%prior 128

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 10, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 170
  • Total persons involved: 346
  • Total vehicles involved: 226

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 10, 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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