Yearly Traffic Safety Analysis

206 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Clarke County recorded 206 total crashes, a slight decrease from the 210 crashes reported in 2017. Despite the small drop in total incidents, the number of people injured rose from 48 in 2017 to 62 in 2018, an increase of 29.2%. The number of fatalities also increased from 3 to 4 year-over-year, and the number of serious injury crashes tripled from 2 to 6.

206

▼ -1.9%was 210

Total Crash Events

4

▲ 33.3%was 3

Persons Killed

62

▲ 29.2%was 48

Persons Injured

3

▲ 50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (4) 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 · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, the total number of crashes in Clarke County remained relatively stable, decreasing by 1.9% from 210 in 2017 to 206 in 2018. However, the severity of these incidents increased, with total injuries rising by 29.2% (from 48 to 62) and total fatalities increasing from 3 to 4.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 3↑33.3%

0

Other Killed

Prior: 0→0.0%

2

Pedestrians Injured

Prior: 1↑100.0%

59

Motorists Injured

Prior: 47↑25.5%

1

Other Injured

Prior: 0→%

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 temporal patterns of crashes showed some shifts between the two years. The most common day for a crash moved from Friday (38 crashes) in 2017 to Sunday (35 crashes) in 2018. The peak hour for crashes also shifted earlier, from 6 p.m. in 2017 (16 crashes) to 3 p.m. in 2018 (19 crashes). November remained the month with the highest crash volume in both periods, with 35 crashes in 2017 and 28 in 2018.

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

While total crashes slightly decreased, the severity of outcomes worsened in 2018 compared to 2017. The number of fatal crashes increased from 2 to 3, and the number of serious injury crashes tripled from 2 to 6. Consequently, the share of crashes resulting in serious injury rose from 1.0% in 2017 to 2.9% in 2018. The proportion of crashes with no injuries decreased from 80.0% in 2017 to 76.2% in 2018.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.5%
↑50.0%prior 2
Serious Injury6serious injury crashes2.9%
↑200.0%prior 2
Minor Injury24minor injury crashes11.7%
↑33.3%prior 18
Possible Injury16possible injury crashes7.8%
↓-20.0%prior 20
No Injury157no injury crashes76.2%
↓-6.5%prior 168

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 top contributing factor in both periods, though the count of such incidents decreased by 20% from 70 in 2017 to 56 in 2018. Several other factors saw notable shifts in their crash counts. Crashes attributed to "Driving too fast for conditions" increased from 4 incidents in 2017 to 15 in 2018, a 275% increase in count. Conversely, crashes involving "Failure to Yield Right of Way from a stop sign" decreased from 12 to 4.

Officer-Reported Primary Contributing Cause

Animal56 (27.2%)↓-20.0%prior 70
Ran off road - straight21 (10.2%)↑31.3%prior 16
Lost Control19 (9.2%)↑11.8%prior 17
Driving too fast for conditions15 (7.3%)
Ran off road - left10 (4.9%)↑11.1%prior 9
Followed too close8 (3.9%)↑33.3%prior 6
Other (explain in narrative): Other8 (3.9%)↓-52.9%prior 17
FTYROW: Making left turn7 (3.4%)→0.0%prior 7
Made improper turn6 (2.9%)
Operating vehicle in an reckless, erratic, careless, negligent manner6 (2.9%)→0.0%prior 6

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

Road & Environmental Conditions

The conditions under which crashes occurred shifted toward more adverse weather and road surfaces in 2018. While crashes on "Dry" road surfaces remained most frequent, their count dropped from 123 in 2017 to 90 in 2018. In contrast, crashes on "Ice/frost" surfaces tripled from 5 to 15, and crashes on "Snow" covered roads more than doubled from 5 to 13. Similarly, crashes during "Snow" weather conditions increased from 8 to 15 year-over-year, while crashes in "Clear" weather decreased from 93 to 74.

Weather

Clear74 (46.5%)
↓-20.4%prior 93
Cloudy45 (28.3%)
↓-2.2%prior 46
Snow15 (9.4%)
↑87.5%prior 8
Rain14 (8.8%)
↑55.6%prior 9
Fog, smoke, smog5 (3.1%)
Freezing rain/drizzle4 (2.5%)
Blowing Snow2 (1.3%)

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

Lighting

Daylight96 (59.3%)
↓-6.8%prior 103
Dark - roadway not lighted39 (24.1%)
→0.0%prior 39
Dark - roadway lighted18 (11.1%)
↑38.5%prior 13
Dusk6 (3.7%)
→0.0%prior 6
Dawn3 (1.9%)

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

Road Surface

Dry90 (56.3%)
↓-26.8%prior 123
Wet26 (16.3%)
↑30.0%prior 20
Ice/frost15 (9.4%)
↑200.0%prior 5
Snow13 (8.1%)
↑160.0%prior 5
Gravel11 (6.9%)
↑10.0%prior 10
Slush4 (2.5%)
Water (standing or moving)1 (0.6%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford and Chevrolet models leading in both 2017 and 2018. Ford vehicles were involved in 49 crashes in 2017 and 51 in 2018. There was a notable shift in the age demographics of people involved in crashes; the 65+ age group saw a significant increase in representation, from 37 individuals in 2017 to 56 in 2018. Conversely, involvement of the 55-64 age group decreased from 57 to 40 individuals.

Top Vehicle Makes (283 vehicles)

1
FORD51 (18%)
↑4.1%prior 49
2
CHEV44 (15.5%)
↑22.2%prior 36
3
CHEVROLET21 (7.4%)
↓-32.3%prior 31
4
DODG13 (4.6%)
↓-18.8%prior 16
5
TOYO13 (4.6%)
6
DODGE11 (3.9%)
↓-26.7%prior 15
7
GMC8 (2.8%)
8
BUIC8 (2.8%)
↓-20.0%prior 10
9
FREIGHTLINER8 (2.8%)
↑33.3%prior 6
10
JEEP7 (2.5%)
→0.0%prior 7

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

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

Sex Distribution (211 persons with recorded sex)

Male146 (69.2%)
↑2.8%prior 142
Female65 (30.8%)
↓-28.6%prior 91

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: 206
  • Total persons involved: 351
  • Total vehicles involved: 283

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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