Yearly Traffic Safety Analysis

294 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In 2023, Clay County recorded 294 vehicle crashes, a 6.4% decrease from the 314 crashes reported in 2022. While the number of fatalities remained constant at one, total injuries saw a notable decline of 18.8%, falling from 101 to 82. The most significant shift in contributing factors was a more than doubling in the count of crashes attributed to 'Followed too close,' which rose from 9 to 20 incidents.

294

▼ -6.4%was 314

Total Crash Events

1

Persons Killed

82

▼ -18.8%was 101

Persons Injured

1

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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in traffic incidents in Clay County showed a decrease year-over-year. Total crashes fell by 6.4%, from 314 in 2022 to 294 in 2023. This downward trend was also reflected in the number of people injured, which decreased by 18.8% from 101 to 82 over the same period, while fatalities held steady at one.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

1

Pedestrians Injured

Prior: 4↓-75.0%

1

Cyclists Injured

Prior: 1→0.0%

80

Motorists Injured

Prior: 96↓-16.7%

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained largely consistent year-over-year. Friday was the peak day for crashes in both 2022 (51 crashes) and 2023 (56 crashes). The 5 p.m. hour was also the most frequent time for collisions in both periods, increasing from 24 incidents in 2022 to 28 in 2023.

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the number of fatal crashes was unchanged at one for both years, the distribution of injury crashes shifted. The share of crashes involving serious injuries rose from 1.9% to 2.4%, and minor injury crashes increased from 9.6% to 11.6% of the total. Conversely, crashes categorized with 'Possible Injury' saw a significant drop, decreasing from 42 incidents (13.4% of total) in 2022 to 24 incidents (8.2% of total) in 2023.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
→0.0%prior 1
Serious Injury7serious injury crashes2.4%
↑16.7%prior 6
Minor Injury34minor injury crashes11.6%
↑13.3%prior 30
Possible Injury24possible injury crashes8.2%
↓-42.9%prior 42
No Injury228no injury crashes77.6%
↓-3.0%prior 235

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both periods, with a nearly identical count of 60 in 2023 compared to 59 in 2022. A significant year-over-year change was observed in crashes due to 'Followed too close,' where the count more than doubled from 9 to 20 incidents. In contrast, crashes attributed to 'Driving too fast for conditions' decreased by 23.8% in count, from 21 incidents in 2022 to 16 in 2023.

Officer-Reported Primary Contributing Cause

Animal60 (20.4%)↑1.7%prior 59
Other (explain in narrative): Other38 (12.9%)↓-9.5%prior 42
FTYROW: From stop sign23 (7.8%)↑15.0%prior 20
Followed too close20 (6.8%)↑122.2%prior 9
Driving too fast for conditions16 (5.4%)↓-23.8%prior 21
Lost Control16 (5.4%)↑45.5%prior 11
Ran off road - left15 (5.1%)↑7.1%prior 14
Ran Stop Sign13 (4.4%)↓-7.1%prior 14
FTYROW: Making left turn9 (3.1%)↓-40.0%prior 15
FTYROW: From yield sign8 (2.7%)↑60.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes under clear weather and on dry roads remained the most common scenarios in both years, despite decreases in their absolute counts. The proportion of crashes occurring in rain increased from 2.2% of all incidents in 2022 to 4.1% in 2023. Collisions on roads with ice or frost increased from 17 to 20 incidents, while crashes involving snow decreased from 28 to 21 incidents.

Weather

Clear167 (71.1%)
↓-13.0%prior 192
Cloudy38 (16.2%)
↑11.8%prior 34
Rain12 (5.1%)
↑71.4%prior 7
Snow9 (3.8%)
↓-30.8%prior 13
Fog, smoke, smog4 (1.7%)
Freezing rain/drizzle2 (0.9%)
Severe Winds1 (0.4%)
Other (explain in narrative)1 (0.4%)
Blowing Snow1 (0.4%)
↓-88.9%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight185 (78.4%)
↓-6.6%prior 198
Dark - roadway lighted22 (9.3%)
→0.0%prior 22
Dark - roadway not lighted21 (8.9%)
↓-30.0%prior 30
Dark - unknown roadway lighting5 (2.1%)
Dusk3 (1.3%)
↓-66.7%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry167 (70.5%)
↓-12.1%prior 190
Snow21 (8.9%)
↓-25.0%prior 28
Ice/frost20 (8.4%)
↑17.6%prior 17
Wet17 (7.2%)
↑6.3%prior 16
Gravel9 (3.8%)
→0.0%prior 9
Slush2 (0.8%)
Other (explain in narrative)1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet were the top two vehicle makes involved in crashes in both years, with both seeing a decline in counts; Fords fell from 108 to 94, and Chevrolets dropped from 126 to 108. An analysis of persons involved in crashes reveals a significant demographic shift: the number of individuals in the 55-64 age group fell from 110 to 65, while those in the 65+ age group increased from 100 to 120.

Top Vehicle Makes (481 vehicles)

1
FORD94 (19.5%)
↓-13.0%prior 108
2
CHEV90 (18.7%)
↓-6.3%prior 96
3
DODG28 (5.8%)
↑27.3%prior 22
4
JEEP22 (4.6%)
↓-12.0%prior 25
5
GMC22 (4.6%)
↓-18.5%prior 27
6
TOYO21 (4.4%)
↑90.9%prior 11
7
BUIC19 (4%)
↑11.8%prior 17
8
CHRY18 (3.7%)
↑5.9%prior 17
9
CHEVROLET18 (3.7%)
↓-40.0%prior 30
10
TOYT16 (3.3%)
↑60.0%prior 10

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (448 persons with recorded sex)

Male237 (52.9%)
↓-16.5%prior 284
Female211 (47.1%)
↑12.8%prior 187

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 294
  • Total persons involved: 651
  • Total vehicles involved: 481

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: 2023." Published September 9, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2023-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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