Yearly Traffic Safety Analysis

146 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Jackson County recorded 146 vehicle crashes, a 5.8% decrease from the 155 crashes reported in 2019. Despite the overall decline in collisions, the number of fatalities increased from two in 2019 to three in 2020. Total injuries remained unchanged at 59 for both years.

146

▼ -5.8%was 155

Total Crash Events

3

▲ 50.0%was 2

Persons Killed

59

Persons Injured

3

▲ 50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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

Overall, traffic crashes in Jackson County saw a downward trend, decreasing by 5.8% from 155 in 2019 to 146 in 2020. While the total number of collisions fell, the human cost did not follow the same pattern. The number of people injured remained constant at 59, and fatalities rose from two to three year-over-year.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 2↓-50.0%

1

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 2↓-50.0%

57

Motorists Injured

Prior: 57→0.0%

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 significantly between the two periods. In 2020, the highest number of crashes occurred on Mondays (30), a change from 2019 when Saturday was the peak day with 31 crashes. The peak hour for collisions also moved from 9 a.m. in 2019 (15 crashes) to the weekday commute hours of 8 a.m. and 5 p.m. in 2020, which both recorded 12 crashes each.

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 in 2020. The number of fatal crashes rose from two in 2019 to three in 2020, increasing their share of all crashes from 1.3% to 2.1%. Similarly, serious injury crashes increased from seven to eight. Crashes resulting in only possible injury saw a notable decrease from 29 in 2019 to 22 in 2020.

Outcome by Severity (Crash Events)

Fatal3fatal crashes2.1%
↑50.0%prior 2
Serious Injury8serious injury crashes5.5%
↑14.3%prior 7
Minor Injury15minor injury crashes10.3%
↑7.1%prior 14
Possible Injury22possible injury crashes15.1%
↓-24.1%prior 29
No Injury98no injury crashes67.1%
↓-4.9%prior 103

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 primary contributing factors showed some shifts between the two years, although 'Lost Control' remained the leading cause with an identical count of 22 crashes in both 2019 and 2020. Crashes attributed to 'Driving too fast for conditions' decreased from 12 incidents in 2019 to 7 in 2020. In contrast, crashes involving 'FTYROW: From stop sign' increased from 10 to 12. The count for crashes involving an 'Animal' remained unchanged at 12.

Officer-Reported Primary Contributing Cause

Lost Control22 (15.1%)→0.0%prior 22
Ran off road - straight14 (9.6%)→0.0%prior 14
FTYROW: From stop sign12 (8.2%)↑20.0%prior 10
Animal12 (8.2%)→0.0%prior 12
Other (explain in narrative): Other11 (7.5%)↓-26.7%prior 15
Ran off road - left8 (5.5%)↑33.3%prior 6
Followed too close7 (4.8%)↓-22.2%prior 9
Driving too fast for conditions7 (4.8%)↓-41.7%prior 12
Driver Distraction: Other interior distraction6 (4.1%)↓-33.3%prior 9
Ran Stop Sign4 (2.7%)↓-20.0%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 predominantly occurred in clear weather and on dry roads in both periods, but there were notable shifts. The number of crashes on icy or frosty roads was more than halved, dropping from 18 in 2019 to 8 in 2020. Correspondingly, crashes on dry surfaces increased from 93 to 101. The count of crashes in daylight conditions decreased from 103 to 92, while the count of crashes in unlit dark conditions remained constant at 25.

Weather

Clear92 (65.2%)
↑7.0%prior 86
Cloudy33 (23.4%)
↓-21.4%prior 42
Rain7 (5.0%)
→0.0%prior 7
Snow6 (4.3%)
→0.0%prior 6
Freezing rain/drizzle1 (0.7%)
Severe Winds1 (0.7%)
Fog, smoke, smog1 (0.7%)

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

Lighting

Daylight92 (65.2%)
↓-10.7%prior 103
Dark - roadway not lighted25 (17.7%)
→0.0%prior 25
Dark - roadway lighted16 (11.3%)
↑23.1%prior 13
Dusk7 (5.0%)
Dawn1 (0.7%)

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

Road Surface

Dry101 (70.6%)
↑8.6%prior 93
Wet14 (9.8%)
↓-22.2%prior 18
Gravel10 (7.0%)
↓-16.7%prior 12
Ice/frost8 (5.6%)
↓-55.6%prior 18
Snow6 (4.2%)
↓-14.3%prior 7
Mud, dirt2 (1.4%)
Slush2 (1.4%)

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

Vehicles & Demographics

An analysis of persons involved in crashes shows a significant demographic shift, with the 16-20 age group becoming the most frequently involved group in 2020 (47 people), an increase from 38 in 2019. Conversely, the number of people aged 65 and older involved in crashes decreased from 52 to 39. Regarding vehicle makes, Ford and Chevrolet remained the top two most common vehicles in crashes for both years, though their counts decreased from 50 to 36 for Ford and 42 to 31 for 'CHEV', respectively. 'DODG' vehicles involved in crashes increased from 15 to 20.

Top Vehicle Makes (230 vehicles)

1
FORD36 (15.7%)
↓-28.0%prior 50
2
CHEV31 (13.5%)
↓-26.2%prior 42
3
DODG20 (8.7%)
↑33.3%prior 15
4
CHEVROLET20 (8.7%)
↑42.9%prior 14
5
GMC14 (6.1%)
↑27.3%prior 11
6
JEEP9 (3.9%)
↑50.0%prior 6
7
BUIC7 (3%)
↓-22.2%prior 9
8
TOYOTA6 (2.6%)
→0.0%prior 6
9
CHRY5 (2.2%)
↓-44.4%prior 9
10
PONT5 (2.2%)

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

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

Sex Distribution (214 persons with recorded sex)

Male142 (66.4%)
↓-10.7%prior 159
Female72 (33.6%)
→0.0%prior 72

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: 146
  • Total persons involved: 307
  • Total vehicles involved: 230

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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