Yearly Traffic Safety Analysis

343 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Clay County recorded 343 total crashes, nearly unchanged from the 342 crashes recorded in 2017. While the overall crash volume remained stable, there was a notable shift in outcomes, with total injuries decreasing by 15% from 120 to 102. Additionally, there were no fatal crashes in 2018, compared to one fatal crash in the prior year.

343

▲ 0.3%was 342

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

102

▼ -15.0%was 120

Persons Injured

0

▼ -100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall crash trend in Clay County was stable year-over-year, with total collisions increasing by just one incident from 342 in 2017 to 343 in 2018. Despite the flat trend in crash volume, the severity of outcomes improved, as total injuries fell by 15% and fatalities were eliminated, dropping from one in 2017 to zero in 2018.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

2

Cyclists Injured

Prior: 3↓-33.3%

99

Motorists Injured

Prior: 116↓-14.7%

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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 2018, the peak days for crashes were Monday and Wednesday, each with 61 incidents, a change from 2017 when Friday was the distinct peak day with 77 crashes. The busiest hour for collisions also shifted slightly earlier, moving from 5 p.m. in 2017 (28 crashes) to 4 p.m. in 2018 (29 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity outcomes showed mixed changes year-over-year. Fatal crashes were eliminated in 2018, down from one in 2017, and minor injury crashes fell from 48 to 30. However, the number of crashes resulting in a serious injury increased substantially, rising from 2 in 2017 to 9 in 2018. The share of no-injury crashes grew from 71.9% to 74.3% of all incidents.

Outcome by Severity (Crash Events)

Serious Injury9serious injury crashes2.6%
↑350.0%prior 2
Minor Injury30minor injury crashes8.7%
↓-37.5%prior 48
Possible Injury49possible injury crashes14.3%
↑8.9%prior 45
No Injury255no injury crashes74.3%
↑3.7%prior 246

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in both years, though the count decreased by 17% from 76 incidents in 2017 to 63 in 2018. 'Failure to yield right of way from a stop sign' saw a 23% increase in count, rising from 31 to 38 crashes. 'Driving too fast for conditions' also became more prevalent, with its count increasing by 32% from 22 to 29 incidents.

Officer-Reported Primary Contributing Cause

Animal63 (18.4%)↓-17.1%prior 76
FTYROW: From stop sign38 (11.1%)↑22.6%prior 31
Driving too fast for conditions29 (8.5%)↑31.8%prior 22
Other (explain in narrative): Other21 (6.1%)↓-16.0%prior 25
Followed too close19 (5.5%)↑11.8%prior 17
FTYROW: Making left turn18 (5.2%)↑80.0%prior 10
Ran Stop Sign17 (5%)↑13.3%prior 15
Ran off road - left16 (4.7%)↑60.0%prior 10
FTYROW: At uncontrolled intersection12 (3.5%)↑33.3%prior 9
Lost Control11 (3.2%)↓-15.4%prior 13

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While most crashes in both years occurred in daylight and on dry roads, there was a significant shift in crashes related to adverse road surface conditions. The number of crashes on dry roads decreased from 217 in 2017 to 180 in 2018. Conversely, crashes on roads with ice or frost increased from 20 to 32, and collisions on snowy roads more than doubled from 14 to 31 incidents.

Weather

Clear197 (68.4%)
↓-8.4%prior 215
Cloudy55 (19.1%)
↑5.8%prior 52
Rain12 (4.2%)
↑9.1%prior 11
Snow8 (2.8%)
↑33.3%prior 6
Freezing rain/drizzle7 (2.4%)
↓-12.5%prior 8
Blowing Snow5 (1.7%)
Fog, smoke, smog2 (0.7%)
Other (explain in narrative)1 (0.3%)
Severe Winds1 (0.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash

Lighting

Daylight226 (77.4%)
↑5.1%prior 215
Dark - roadway not lighted34 (11.6%)
↓-17.1%prior 41
Dark - roadway lighted20 (6.8%)
↓-23.1%prior 26
Dusk6 (2.1%)
↓-14.3%prior 7
Dawn3 (1.0%)
↓-57.1%prior 7
Dark - unknown roadway lighting3 (1.0%)
↓-40.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field

Road Surface

Dry180 (61.9%)
↓-17.1%prior 217
Ice/frost32 (11.0%)
↑60.0%prior 20
Snow31 (10.7%)
↑121.4%prior 14
Wet28 (9.6%)
↓-12.5%prior 32
Gravel15 (5.2%)
↑7.1%prior 14
Slush4 (1.4%)
Water (standing or moving)1 (0.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes, primarily Chevrolet and Ford, remained consistent between 2017 and 2018. An analysis of the age of persons involved in crashes reveals a notable increase in the 16-20 age group, which grew from 72 individuals in 2017 to 85 in 2018. In contrast, the number of individuals aged 55-64 and 65+ involved in collisions saw slight decreases.

Top Vehicle Makes (558 vehicles)

1
FORD92 (16.5%)
↑3.4%prior 89
2
CHEV78 (14%)
↓-22.0%prior 100
3
CHEVROLET50 (9%)
↑16.3%prior 43
4
DODG39 (7%)
↑18.2%prior 33
5
TOYT27 (4.8%)
↑68.8%prior 16
6
DODGE22 (3.9%)
↓-18.5%prior 27
7
BUIC22 (3.9%)
↓-12.0%prior 25
8
GMC21 (3.8%)
↓-16.0%prior 25
9
CHRY19 (3.4%)
↓-17.4%prior 23
10
PONT15 (2.7%)
↓-6.3%prior 16

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records

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

Sex Distribution (439 persons with recorded sex)

Male240 (54.7%)
↓-0.4%prior 241
Female199 (45.3%)
↑13.7%prior 175

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 343
  • Total persons involved: 664
  • Total vehicles involved: 558

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: 2018." Published September 9, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2018-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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