Yearly Traffic Safety Analysis

307 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Harrison County, total crashes decreased from 337 in 2019 to 307 in 2020, an 8.9% reduction. While overall collisions, injuries (112 to 100), and fatalities (2 to 1) fell, the number of crashes involving a driver under the influence (DUI) doubled from 10 to 20 over the same period.

307

▼ -8.9%was 337

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

100

▼ -10.7%was 112

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

Trend Summary

Traffic incidents in Harrison County showed a downward trend from 2019 to 2020. The total number of crashes decreased by 8.9%, from 337 to 307. This decline was also reflected in the number of people injured, which fell by 10.7% from 112 to 100, and fatalities, which decreased from 2 to 1.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Motorists Killed

Prior: 2↓-100.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

99

Motorists Injured

Prior: 109↓-9.2%

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 timing of crashes shifted between the two periods. The peak day for collisions moved from Wednesday (58 crashes) in 2019 to Friday (57 crashes) in 2020. More notably, the peak hour for crashes shifted from the 6 p.m. evening hour (27 crashes) in the prior year to the 7 a.m. morning hour (24 crashes) in the current 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

While the number of fatal crashes decreased from 2 in 2019 to 1 in 2020, the count of serious injury crashes increased from 13 to 16. As a proportion of all crashes, serious injury incidents rose from 3.9% in 2019 to 5.2% in 2020. Conversely, the number of crashes involving minor or possible injuries declined year-over-year.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
↓-50.0%prior 2
Serious Injury16serious injury crashes5.2%
↑23.1%prior 13
Minor Injury26minor injury crashes8.5%
↓-18.8%prior 32
Possible Injury31possible injury crashes10.1%
↓-26.2%prior 42
No Injury233no injury crashes75.9%
↓-6.0%prior 248

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 an animal remained the top contributing factor in both periods, though the count decreased from 76 in 2019 to 61 in 2020. 'Lost Control' was the second-most cited factor in both years with 40 incidents each. Notably, crashes attributed to 'Followed too close' increased by 87.5% in count, rising from 16 to 30 incidents and becoming the third most common factor in 2020. In contrast, crashes from 'Driving too fast for conditions' fell from 35 to 27.

Officer-Reported Primary Contributing Cause

Animal61 (19.9%)↓-19.7%prior 76
Lost Control40 (13%)→0.0%prior 40
Followed too close30 (9.8%)↑87.5%prior 16
Driving too fast for conditions27 (8.8%)↓-22.9%prior 35
Ran off road - straight19 (6.2%)↓-24.0%prior 25
Ran off road - left14 (4.6%)↑16.7%prior 12
Operating vehicle in an reckless, erratic, careless, negligent manner9 (2.9%)↑28.6%prior 7
FTYROW: From stop sign8 (2.6%)↓-20.0%prior 10
Other (explain in narrative): Other7 (2.3%)↓-61.1%prior 18
Other (explain in narrative): Disregarded Warning Sign7 (2.3%)↑40.0%prior 5

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 in both 2019 and 2020 predominantly occurred in clear weather, during daylight, and on dry roads. The proportion of incidents occurring under these ideal conditions was higher in 2020 compared to 2019. For instance, crashes on dry roads accounted for 61.6% of the total in 2020, up from a 53.4% share in 2019, while crashes in clear weather increased from a 51.9% share to 60.3%.

Weather

Clear185 (71.4%)
↑5.7%prior 175
Cloudy31 (12.0%)
↓-22.5%prior 40
Snow15 (5.8%)
↓-34.8%prior 23
Rain11 (4.2%)
↑37.5%prior 8
Freezing rain/drizzle7 (2.7%)
↓-41.7%prior 12
Fog, smoke, smog5 (1.9%)
→0.0%prior 5
Blowing Snow3 (1.2%)
↓-50.0%prior 6
Severe Winds1 (0.4%)
Other (explain in narrative)1 (0.4%)

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

Lighting

Daylight168 (64.6%)
↓-7.7%prior 182
Dark - roadway not lighted62 (23.8%)
↓-6.1%prior 66
Dark - roadway lighted21 (8.1%)
↑110.0%prior 10
Dawn5 (1.9%)
Dusk4 (1.5%)
↓-60.0%prior 10

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

Road Surface

Dry189 (72.4%)
↑5.0%prior 180
Wet27 (10.3%)
↓-6.9%prior 29
Snow15 (5.7%)
↓-31.8%prior 22
Ice/frost13 (5.0%)
↓-45.8%prior 24
Slush8 (3.1%)
Gravel7 (2.7%)
↓-30.0%prior 10
Mud, dirt2 (0.8%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford, Chevrolet (as 'CHEV' and 'CHEVROLET'), and Dodge (as 'DODG' and 'DODGE') being the most frequent in both years. An analysis of persons involved in crashes shows a shift in age demographics, with the number of individuals aged 65 and older decreasing from 86 in 2019 to 60 in 2020. The 16-20 and 21-25 age groups saw slight increases in the number of people involved in crashes.

Top Vehicle Makes (434 vehicles)

1
FORD69 (15.9%)
↓-17.9%prior 84
2
CHEV57 (13.1%)
↓-20.8%prior 72
3
CHEVROLET39 (9%)
↑11.4%prior 35
4
FREIGHTLINER20 (4.6%)
↑33.3%prior 15
5
DODG19 (4.4%)
↑11.8%prior 17
6
GMC17 (3.9%)
↓-10.5%prior 19
7
PETERBILT14 (3.2%)
↑133.3%prior 6
8
DODGE13 (3%)
↓-7.1%prior 14
9
NISS12 (2.8%)
↑71.4%prior 7
10
HONDA11 (2.5%)
↑37.5%prior 8

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

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

Sex Distribution (398 persons with recorded sex)

Male274 (68.8%)
→0.0%prior 274
Female124 (31.2%)
↓-12.1%prior 141

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: 307
  • Total persons involved: 607
  • Total vehicles involved: 434

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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Harrison County, IA Crash Report — 2020 | ThatCarHitMe.com