Yearly Traffic Safety Analysis

761 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Des Moines County, total vehicle crashes decreased by 12.8% from 873 in 2019 to 761 in 2020. This overall reduction was accompanied by decreases in both total injuries, which fell from 184 to 180, and fatalities, which dropped from 3 to 2. The most notable shift was this overall decline in crash volume across the county.

761

▼ -12.8%was 873

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

180

▼ -2.2%was 184

Persons Injured

2

▼ -33.3%was 3

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 Des Moines County indicates a downward trend year-over-year. Total crashes fell by 12.8%, from 873 in 2019 to 761 in 2020. This trend included a slight decrease in total injuries from 184 to 180 and a reduction in fatalities from 3 to 2.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Cyclists Killed

Prior: 0→%

1

Motorists Killed

Prior: 3↓-66.7%

7

Pedestrians Injured

Prior: 3↑133.3%

5

Cyclists Injured

Prior: 1↑400.0%

168

Motorists Injured

Prior: 180↓-6.7%

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 timing of crashes shifted between the two periods. In 2020, the peak day for crashes was Tuesday with 123 incidents, a change from 2019 when Monday was the peak day with 140 crashes. Similarly, the peak hour for collisions moved an hour earlier, from 4 p.m. in 2019 (70 crashes) to 3 p.m. in 2020 (61 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

While the total number of crashes decreased, the proportion of crashes involving an injury increased. In 2020, 23.3% of crashes resulted in a possible, minor, or serious injury, compared to 18.6% in 2019. Conversely, the share of no-injury crashes fell from 81.1% to 76.5%. Fatal crashes decreased from 3 in 2019 to 2 in 2020, and the fatal crash rate per 100 crashes dropped from 0.34 to 0.26.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.3%
↓-33.3%prior 3
Serious Injury14serious injury crashes1.8%
↑7.7%prior 13
Minor Injury59minor injury crashes7.8%
↑11.3%prior 53
Possible Injury104possible injury crashes13.7%
↑8.3%prior 96
No Injury582no injury crashes76.5%
↓-17.8%prior 708

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 factor in both periods was 'Animal,' though the count of these incidents decreased by 19.5% from 159 in 2019 to 128 in 2020. Other top factors also saw decreases, including 'Followed too close' (from 58 to 42 crashes) and 'Driving too fast for conditions' (from 48 to 33 crashes). In contrast, crashes attributed to 'Ran off road - straight' increased by 61.1%, from 18 incidents in 2019 to 29 in 2020.

Officer-Reported Primary Contributing Cause

Animal128 (16.8%)↓-19.5%prior 159
Other (explain in narrative): Other63 (8.3%)↓-6.0%prior 67
FTYROW: From stop sign44 (5.8%)↓-4.3%prior 46
Followed too close42 (5.5%)↓-27.6%prior 58
Ran off road - left42 (5.5%)↓-12.5%prior 48
Driver Distraction: Other interior distraction38 (5%)↓-28.3%prior 53
Driving too fast for conditions33 (4.3%)↓-31.3%prior 48
Ran off road - straight29 (3.8%)↑61.1%prior 18
FTYROW: Making left turn26 (3.4%)↓-23.5%prior 34
Operating vehicle in an reckless, erratic, careless, negligent manner26 (3.4%)↑73.3%prior 15

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

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with 'Clear' weather and 'Dry' road surfaces predominating in both periods. Crashes in daylight accounted for the majority of incidents in both years, though their share of the total fell from 58.8% in 2019 to 54.9% in 2020. Notably, while total crashes decreased, the number of incidents on 'Ice/frost' surfaces increased from 27 to 33.

Weather

Clear453 (69.4%)
↓-0.9%prior 457
Cloudy120 (18.4%)
↓-27.7%prior 166
Rain39 (6.0%)
↑14.7%prior 34
Snow27 (4.1%)
↓-37.2%prior 43
Freezing rain/drizzle10 (1.5%)
↓-37.5%prior 16
Severe Winds2 (0.3%)
Other (explain in narrative)1 (0.2%)
Fog, smoke, smog1 (0.2%)
↓-80.0%prior 5

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

Lighting

Daylight418 (63.8%)
↓-18.5%prior 513
Dark - roadway lighted124 (18.9%)
↓-6.1%prior 132
Dark - roadway not lighted77 (11.8%)
↑37.5%prior 56
Dusk25 (3.8%)
↑25.0%prior 20
Dawn6 (0.9%)
↓-45.5%prior 11
Dark - unknown roadway lighting5 (0.8%)

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

Road Surface

Dry497 (76.0%)
↓-1.6%prior 505
Wet77 (11.8%)
↓-26.7%prior 105
Ice/frost33 (5.0%)
↑22.2%prior 27
Snow20 (3.1%)
↓-69.2%prior 65
Gravel20 (3.1%)
↑53.8%prior 13
Slush5 (0.8%)
↓-61.5%prior 13
Mud, dirt1 (0.2%)
Other (explain in narrative)1 (0.2%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, Ford and Chevrolet, remained consistent across both years. An analysis of person data shows a shift in age demographics. The number of persons aged 65 and older involved in crashes decreased from 234 in 2019 to 168 in 2020, and their share of all persons with a known age dropped from 14.6% to 12.7%.

Top Vehicle Makes (1,245 vehicles)

1
FORD195 (15.7%)
↓-13.3%prior 225
2
CHEV179 (14.4%)
↓-13.1%prior 206
3
CHEVROLET106 (8.5%)
↑10.4%prior 96
4
DODG70 (5.6%)
↓-10.3%prior 78
5
JEEP53 (4.3%)
↓-5.4%prior 56
6
GMC52 (4.2%)
↓-11.9%prior 59
7
NR50 (4%)
↑6.4%prior 47
8
KIA42 (3.4%)
↓-32.3%prior 62
9
BUIC37 (3%)
→0.0%prior 37
10
HOND34 (2.7%)
↑3.0%prior 33

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

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

Sex Distribution (1,024 persons with recorded sex)

Male573 (56.0%)
↓-14.5%prior 670
Female451 (44.0%)
↓-24.6%prior 598

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: 761
  • Total persons involved: 1,682
  • Total vehicles involved: 1,245

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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