Yearly Traffic Safety Analysis

1,021 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Dallas County, total crashes decreased from 1,348 in 2019 to 1,021 in 2020, a 24.3% reduction. Despite the overall drop in collisions, the number of fatalities increased significantly, rising from 3 in 2019 to 9 in 2020.

1,021

▼ -24.3%was 1,348

Total Crash Events

9

▲ 200.0%was 3

Persons Killed

333

▼ -21.8%was 426

Persons Injured

7

▲ 133.3%was 3

Fatal Crash Events

Note: "Persons Killed" (9) counts individual fatalities across all crash events. "Fatal" in the severity table below (7) 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 Dallas County shows a year-over-year decrease in total incidents. The total number of crashes fell by 24.3%, from 1,348 in 2019 to 1,021 in 2020. Similarly, total injuries declined from 426 to 333, while total fatalities increased from 3 to 9.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

8

Motorists Killed

Prior: 3↑166.7%

4

Pedestrians Injured

Prior: 3↑33.3%

4

Cyclists Injured

Prior: 4→0.0%

325

Motorists Injured

Prior: 419↓-22.4%

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 showed some changes between the two periods. The peak day for crashes shifted from Monday in 2019 (234 crashes) to Friday in 2020 (186 crashes). The peak hour for collisions remained consistent at 5 p.m. in both years, though the volume of crashes during that hour decreased from 128 in 2019 to 90 in 2020.

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 total crashes decreased, the severity of crashes increased year-over-year. The number of fatal crashes more than doubled, rising from 3 in 2019 to 7 in 2020, and the fatal crash rate increased from 0.22% to 0.69%. The number of serious injury crashes fell from 28 to 17. The proportion of crashes resulting in no injury remained largely unchanged, accounting for 75.2% in 2019 and 74.1% in 2020.

Severity is per crash event (most severe injury). 7 fatal crash events resulted in 9 persons killed.

Outcome by Severity (Crash Events)

Fatal7fatal crashes0.7%
↑133.3%prior 3
Serious Injury17serious injury crashes1.7%
↓-39.3%prior 28
Minor Injury98minor injury crashes9.6%
↓-19.7%prior 122
Possible Injury142possible injury crashes13.9%
↓-21.5%prior 181
No Injury757no injury crashes74.1%
↓-25.3%prior 1,014

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 ranking of top contributing factors shifted between 2019 and 2020. In 2020, collisions with animals became the leading factor with 188 incidents, an increase in count from 179 in 2019. 'Followed too close,' the top factor in 2019 with 197 crashes, saw its count decrease by 33% to 132 crashes in 2020, making it the second-leading cause. 'Driving too fast for conditions' remained the third-most common factor in both years, with its crash count dropping from 111 to 76.

Officer-Reported Primary Contributing Cause

Animal188 (18.4%)↑5.0%prior 179
Followed too close132 (12.9%)↓-33.0%prior 197
Driving too fast for conditions76 (7.4%)↓-31.5%prior 111
Other (explain in narrative): Other55 (5.4%)↓-38.9%prior 90
Lost Control52 (5.1%)↓-17.5%prior 63
FTYROW: Making left turn41 (4%)↓-37.9%prior 66
Operating vehicle in an reckless, erratic, careless, negligent manner37 (3.6%)↑68.2%prior 22
Ran off road - straight36 (3.5%)↓-29.4%prior 51
Ran off road - left35 (3.4%)↓-35.2%prior 54
FTYROW: From stop sign34 (3.3%)↓-54.1%prior 74

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 proportion of crashes occurring under different environmental conditions saw some shifts. Crashes on adverse road surfaces (wet, snow, or ice) decreased significantly, accounting for 20.6% of all crashes in 2020 (210 incidents) compared to 28.0% in 2019 (377 incidents). The share of crashes happening in daylight decreased from 63.4% to 57.4%, while the proportion of crashes in dark conditions rose from 20.4% to 22.8%. Crashes in clear weather remained proportionally stable, representing 57.0% of incidents in 2020 versus 55.1% in 2019.

Weather

Clear582 (67.6%)
↓-21.7%prior 743
Cloudy137 (15.9%)
↓-45.4%prior 251
Snow62 (7.2%)
↓-10.1%prior 69
Rain33 (3.8%)
↓-50.0%prior 66
Freezing rain/drizzle22 (2.6%)
↓-8.3%prior 24
Blowing Snow12 (1.4%)
↓-60.0%prior 30
Fog, smoke, smog6 (0.7%)
↓-25.0%prior 8
Other (explain in narrative)3 (0.3%)
Severe Winds2 (0.2%)
Sleet, hail1 (0.1%)

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

Lighting

Daylight586 (67.9%)
↓-31.5%prior 855
Dark - roadway lighted116 (13.4%)
↓-22.7%prior 150
Dark - roadway not lighted115 (13.3%)
↓-3.4%prior 119
Dusk28 (3.2%)
↓-15.2%prior 33
Dawn16 (1.9%)
↓-55.6%prior 36
Dark - unknown roadway lighting2 (0.2%)
↓-66.7%prior 6

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

Road Surface

Dry625 (72.4%)
↓-21.9%prior 800
Wet85 (9.8%)
↓-49.1%prior 167
Snow73 (8.5%)
↓-14.1%prior 85
Ice/frost46 (5.3%)
↓-58.6%prior 111
Gravel24 (2.8%)
↑41.2%prior 17
Slush6 (0.7%)
↓-57.1%prior 14
Mud, dirt3 (0.3%)
Other (explain in narrative)1 (0.1%)

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

Vehicles & Demographics

An analysis of vehicles involved shows that the most common makes remained consistent year-over-year, with Chevrolet and Ford leading in both periods. The total number of vehicles involved in crashes decreased from 2,371 in 2019 to 1,698 in 2020, reflecting the overall trend. Regarding the age of persons involved, the proportion of individuals aged 16-20 decreased from 15.4% of all persons in 2019 to 13.3% in 2020. Conversely, the 26-34 age group saw its representation increase from 16.8% to 18.8% of all persons involved.

Top Vehicle Makes (1,698 vehicles)

1
FORD268 (15.8%)
↓-28.9%prior 377
2
CHEV189 (11.1%)
↓-36.8%prior 299
3
CHEVROLET122 (7.2%)
↓-0.8%prior 123
4
JEEP90 (5.3%)
↓-19.6%prior 112
5
TOYT69 (4.1%)
↓-55.8%prior 156
6
DODG64 (3.8%)
↓-32.6%prior 95
7
HOND53 (3.1%)
↓-47.5%prior 101
8
TOYOTA49 (2.9%)
↓-14.0%prior 57
9
GMC48 (2.8%)
↓-25.0%prior 64
10
DODGE44 (2.6%)
↑2.3%prior 43

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

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

Sex Distribution (1,593 persons with recorded sex)

Male911 (57.2%)
↓-26.1%prior 1,233
Female682 (42.8%)
↓-33.5%prior 1,025

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: 1,021
  • Total persons involved: 2,253
  • Total vehicles involved: 1,698

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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