Yearly Traffic Safety Analysis

289 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Jones County recorded 289 total traffic crashes, a slight decrease of 1.4% from the 293 crashes reported in 2019. While overall crash volume remained relatively stable, the number of fatalities increased significantly from one in 2019 to four in 2020. This resulted in a corresponding rise in fatal crashes from one to four over the same period.

289

▼ -1.4%was 293

Total Crash Events

4

▲ 300.0%was 1

Persons Killed

64

▼ -8.6%was 70

Persons Injured

4

▲ 300.0%was 1

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 crash volume in Jones County remained stable, with a minor decrease of 1.4% from 293 crashes in 2019 to 289 in 2020. The number of people injured in these incidents also saw a modest decline of 8.6%, from 70 to 64. However, fatalities saw a significant increase, rising from one in the prior year to four in the current year.

Vulnerable Road User Casualties

4

Motorists Killed

Prior: 1↑300.0%

64

Motorists Injured

Prior: 70↓-8.6%

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 in Jones County shifted between 2019 and 2020. The peak day for crashes moved from Thursday (53 crashes) in 2019 to Friday (54 crashes) in 2020. A more significant change occurred in the peak hour, which shifted from the afternoon commute hours of 3 p.m. and 5 p.m. (23 crashes each) in 2019 to 9 p.m. (24 crashes) 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

In 2020, the severity of crashes worsened despite a slight drop in overall incidents. The number of fatal crashes increased from one in 2019 to four in 2020, raising their share of all crashes from 0.3% to 1.4%. Conversely, the total number of crashes resulting in any level of injury decreased from 62 in 2019 to 48 in 2020. The proportion of crashes with no injuries increased from 78.5% to 82.0% year-over-year.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.4%
↑300.0%prior 1
Serious Injury9serious injury crashes3.1%
↑50.0%prior 6
Minor Injury20minor injury crashes6.9%
↓-31.0%prior 29
Possible Injury19possible injury crashes6.6%
↓-29.6%prior 27
No Injury237no injury crashes82%
↑3.0%prior 230

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 involving an animal remained the top contributing factor in both periods, increasing in count from 127 crashes in 2019 to 137 in 2020. 'Lost Control' was the second most common factor in both years, rising slightly from 23 to 26 incidents. Notably, crashes attributed to 'Ran Stop Sign' increased significantly, from one incident in 2019 to eight in 2020. Conversely, crashes due to 'Driving too fast for conditions' decreased from 15 to six, and 'Followed too close' dropped from 15 to eight.

Officer-Reported Primary Contributing Cause

Animal137 (47.4%)↑7.9%prior 127
Lost Control26 (9%)↑13.0%prior 23
Ran off road - straight13 (4.5%)↑8.3%prior 12
FTYROW: From stop sign12 (4.2%)↑20.0%prior 10
Other (explain in narrative): Other11 (3.8%)↑22.2%prior 9
Followed too close8 (2.8%)↓-46.7%prior 15
Ran Stop Sign8 (2.8%)
Driver Distraction: Other interior distraction8 (2.8%)↑14.3%prior 7
Driving too fast for conditions6 (2.1%)↓-60.0%prior 15
Other (explain in narrative): No improper action6 (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

The majority of crashes in both 2019 and 2020 occurred in clear weather and on dry roads. In 2020, there were 125 crashes on dry roads, down from 137 in the previous year, while crashes on wet roads increased from 15 to 21. Crashes on roads with ice or frost were halved, dropping from 20 in 2019 to 10 in 2020. Regarding lighting, crashes during daylight hours decreased slightly from 124 to 118, and incidents on unlit dark roadways fell from 42 to 34.

Weather

Clear108 (62.4%)
↓-12.2%prior 123
Cloudy34 (19.7%)
↓-8.1%prior 37
Snow12 (6.9%)
↑20.0%prior 10
Rain8 (4.6%)
↑14.3%prior 7
Freezing rain/drizzle5 (2.9%)
↓-16.7%prior 6
Other (explain in narrative)4 (2.3%)
Fog, smoke, smog1 (0.6%)
↓-85.7%prior 7
Blowing Snow1 (0.6%)

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

Lighting

Daylight118 (67.4%)
↓-4.8%prior 124
Dark - roadway not lighted34 (19.4%)
↓-19.0%prior 42
Dusk11 (6.3%)
↑22.2%prior 9
Dark - roadway lighted7 (4.0%)
↓-50.0%prior 14
Dawn5 (2.9%)
↓-16.7%prior 6

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

Road Surface

Dry125 (71.4%)
↓-8.8%prior 137
Wet21 (12.0%)
↑40.0%prior 15
Snow12 (6.9%)
↓-14.3%prior 14
Ice/frost10 (5.7%)
↓-50.0%prior 20
Gravel4 (2.3%)
↓-20.0%prior 5
Slush3 (1.7%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford (85 vehicles) and Chevrolet (73 vehicles) being the most common in 2020, similar to the prior year. The age distribution of persons involved in crashes showed some shifts. The number of individuals in the 21-25 age group increased from 49 to 72, and the 35-44 age group grew from 72 to 91. Conversely, there was a decrease in the involvement of persons aged 45-54 (from 92 to 79) and 55-64 (from 79 to 56).

Top Vehicle Makes (385 vehicles)

1
FORD85 (22.1%)
↑14.9%prior 74
2
CHEV55 (14.3%)
↑25.0%prior 44
3
CHEVROLET18 (4.7%)
↓-40.0%prior 30
4
DODG16 (4.2%)
↓-20.0%prior 20
5
GMC16 (4.2%)
↓-11.1%prior 18
6
TOYO12 (3.1%)
↑9.1%prior 11
7
NISS12 (3.1%)
→0.0%prior 12
8
DODGE12 (3.1%)
↑20.0%prior 10
9
NISSAN9 (2.3%)
10
RAM8 (2.1%)

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

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

Sex Distribution (363 persons with recorded sex)

Male223 (61.4%)
↑1.4%prior 220
Female140 (38.6%)
↓-8.5%prior 153

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: 289
  • Total persons involved: 587
  • Total vehicles involved: 385

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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