Yearly Traffic Safety Analysis

96 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Howard County recorded 96 total crashes, a 5.5% increase from the 91 crashes reported in 2019. While total fatalities remained constant at one, the number of injuries decreased slightly from 23 to 21. A primary driver of the overall increase in collisions was a rise in crashes involving animals, which grew from 29 incidents in 2019 to 37 in 2020.

96

▲ 5.5%was 91

Total Crash Events

1

Persons Killed

21

▼ -8.7%was 23

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

Crash trends in Howard County showed a slight increase year-over-year, with total collisions rising by 5.5% from 91 in 2019 to 96 in 2020. Despite the increase in crashes, the number of people injured decreased by 8.7% from 23 to 21. Fatalities held steady with one death recorded in both periods.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

2

Cyclists Injured

Prior: 0→%

19

Motorists Injured

Prior: 21↓-9.5%

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

Temporal crash patterns shifted between the two years. In 2020, crashes peaked on Fridays and Saturdays with 20 incidents each, a change from 2019 when Thursdays and Saturdays were the busiest days with 16 crashes each. The peak hour for crashes also evolved; while the 6 a.m. hour was a high-frequency time in both periods, a new peak emerged at 11 a.m. in 2020 with 9 crashes, replacing the 2 p.m. peak observed in the prior year.

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

The crash severity profile changed between 2019 and 2020. Although the number of fatal crashes was unchanged at one, the proportion of serious injury crashes rose from 1.1% of all incidents in 2019 to 5.2% in 2020. Conversely, crashes resulting in minor injuries saw their share decrease from 13.2% in 2019 to 4.2% in 2020. The total number of non-injury crashes increased from 71 to 77.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1%
→0.0%prior 1
Serious Injury5serious injury crashes5.2%
↑400.0%prior 1
Minor Injury4minor injury crashes4.2%
↓-66.7%prior 12
Possible Injury9possible injury crashes9.4%
↑50.0%prior 6
No Injury77no injury crashes80.2%
↑8.5%prior 71

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 with animals remained the leading contributing factor in both years, with the count increasing by 27.6% from 29 crashes in 2019 to 37 in 2020. Consequently, the share of crashes attributed to animals grew from 31.9% to 38.5% of all incidents. In contrast, incidents where a driver lost control decreased from 9 to 7, and crashes involving a vehicle running off the road straight decreased from 7 to 4.

Officer-Reported Primary Contributing Cause

Animal37 (38.5%)↑27.6%prior 29
Lost Control7 (7.3%)↓-22.2%prior 9
Other (explain in narrative): Other5 (5.2%)→0.0%prior 5
Ran off road - straight4 (4.2%)↓-42.9%prior 7
Followed too close4 (4.2%)
Driving too fast for conditions4 (4.2%)↓-20.0%prior 5
Ran Stop Sign4 (4.2%)
Operating vehicle in an reckless, erratic, careless, negligent manner3 (3.1%)
Improper Backing2 (2.1%)
Driver Distraction: Exterior distraction2 (2.1%)

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

Road & Environmental Conditions

Crashes were significantly more likely to occur in clear weather and on dry roads in 2020 compared to the prior year. The proportion of crashes in clear weather increased from 33.0% to 40.6%, while collisions on dry road surfaces rose from 33.0% to 45.8% of the total. Correspondingly, crashes on adverse road surfaces like snow, ice, or slush dropped from 36.3% of all incidents in 2019 to 20.8% in 2020. Crashes in dark, unlighted conditions also decreased from 18 incidents to 14.

Weather

Clear39 (59.1%)
↑30.0%prior 30
Cloudy20 (30.3%)
↑25.0%prior 16
Snow4 (6.1%)
↓-50.0%prior 8
Rain2 (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

Daylight41 (62.1%)
↓-4.7%prior 43
Dark - roadway not lighted14 (21.2%)
↓-22.2%prior 18
Dusk5 (7.6%)
Dawn3 (4.5%)
Dark - roadway lighted2 (3.0%)
Dark - unknown roadway lighting1 (1.5%)

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

Road Surface

Dry44 (66.7%)
↑46.7%prior 30
Ice/frost9 (13.6%)
→0.0%prior 9
Wet7 (10.6%)
Snow3 (4.5%)
↓-72.7%prior 11
Gravel2 (3.0%)
↓-66.7%prior 6
Slush1 (1.5%)
↓-85.7%prior 7

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 two most common vehicle makes involved in crashes in both periods, with the number of Fords increasing from 24 to 27 and combined Chevrolet models rising from 26 to 31. Analysis of persons involved in crashes shows a significant demographic shift toward older age groups. The number of individuals aged 65 and older nearly doubled from 17 in 2019 to 33 in 2020, and the 45-54 age group also saw a notable rise from 22 to 32 persons involved.

Top Vehicle Makes (131 vehicles)

1
FORD27 (20.6%)
↑12.5%prior 24
2
CHEVROLET17 (13%)
↑112.5%prior 8
3
CHEV14 (10.7%)
↓-22.2%prior 18
4
JEEP8 (6.1%)
↑33.3%prior 6
5
GMC6 (4.6%)
6
DODG5 (3.8%)
↓-28.6%prior 7
7
BUIC5 (3.8%)
→0.0%prior 5
8
INTERNATIONA5 (3.8%)
9
FREIGHTLINER4 (3.1%)
10
TOYT4 (3.1%)

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

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

Sex Distribution (121 persons with recorded sex)

Male80 (66.1%)
↓-2.4%prior 82
Female41 (33.9%)
↑10.8%prior 37

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: 96
  • Total persons involved: 195
  • Total vehicles involved: 131

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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