Yearly Traffic Safety Analysis

93 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Hancock County, traffic crashes decreased significantly from 123 incidents in 2017 to 93 in 2018, representing a 24.4% year-over-year reduction. While the number of fatalities remained unchanged at two, total injuries fell by 22.7% from 75 to 58. The most notable shift was the sharp decline in overall crash volume, particularly in crashes attributed to drivers losing control, which fell by nearly half.

93

▼ -24.4%was 123

Total Crash Events

2

Persons Killed

58

▼ -22.7%was 75

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 trend in traffic incidents shows a marked improvement year-over-year. Total crashes in Hancock County fell by 24.4%, from 123 in 2017 to 93 in 2018. This downward trend was also reflected in the number of injuries, which decreased from 75 to 58, while fatalities held steady at two for both periods.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

1

Pedestrians Injured

Prior: 0→%

57

Motorists Injured

Prior: 75↓-24.0%

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 timing of crashes showed some shifts between the two years. While Friday remained a high-frequency day for crashes, the peak day in 2018 was shared between Wednesday and Friday, with 16 incidents each, compared to a clear peak on Friday in 2017 with 25 incidents. The peak hour for crashes shifted slightly earlier, from 4 p.m. in 2017 (13 crashes) to 3 p.m. in 2018 (11 crashes). The month with the highest crash volume also changed, moving from January (21 crashes) in 2017 to May (13 crashes) 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 the absolute number of fatal crashes remained constant at two in both 2017 and 2018, the fatal crash rate increased from 1.63% to 2.15% due to the lower total number of crashes in the current period. The proportion of crashes involving any level of injury was stable, accounting for 40.9% of crashes in 2018 compared to 39.8% in 2017. However, the share of serious injury crashes increased from 2.4% (3 crashes) in the prior year to 4.3% (4 crashes) in the current year.

Outcome by Severity (Crash Events)

Fatal2fatal crashes2.2%
→0.0%prior 2
Serious Injury4serious injury crashes4.3%
↑33.3%prior 3
Minor Injury19minor injury crashes20.4%
↓-13.6%prior 22
Possible Injury13possible injury crashes14%
↓-40.9%prior 22
No Injury55no injury crashes59.1%
↓-25.7%prior 74

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

The leading contributing factors for crashes shifted between periods. In 2018, "Driving too fast for conditions" was the top factor, cited in 16 crashes (17.2% share), a slight increase from 15 crashes in 2017. The previous year's leading cause, "Lost Control," saw its count decrease by 47%, from 17 incidents in 2017 to 9 in 2018, dropping its rank. Similarly, crashes from running off the road straight decreased from 12 to 7.

Officer-Reported Primary Contributing Cause

Driving too fast for conditions16 (17.2%)↑6.7%prior 15
Lost Control9 (9.7%)↓-47.1%prior 17
Ran off road - straight7 (7.5%)↓-41.7%prior 12
FTYROW: From stop sign6 (6.5%)↑20.0%prior 5
FTYROW: At uncontrolled intersection5 (5.4%)
Driver Distraction: Other interior distraction5 (5.4%)↓-16.7%prior 6
Other (explain in narrative): Other5 (5.4%)↓-68.8%prior 16
Ran Stop Sign4 (4.3%)↓-33.3%prior 6
Followed too close3 (3.2%)
Animal3 (3.2%)

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

Road & Environmental Conditions

Crashes in both periods occurred predominantly during daylight (64.5% in 2018 vs. 65.9% in 2017) and on dry roads (54.8% in 2018 vs. 55.3% in 2017), with no significant proportional shifts in lighting or weather conditions. However, there was a notable year-over-year decrease in crashes occurring on icy or frosty roads, which dropped from 18 incidents in 2017 to just 5 in 2018. The proportion of crashes on weather-affected surfaces (snow, ice, wet, slush) decreased from 34.1% in 2017 to 29.0% in 2018.

Weather

Clear53 (60.9%)
↓-23.2%prior 69
Cloudy17 (19.5%)
↓-29.2%prior 24
Snow5 (5.7%)
Blowing Snow4 (4.6%)
Rain4 (4.6%)
↓-33.3%prior 6
Freezing rain/drizzle3 (3.4%)
↓-40.0%prior 5
Fog, smoke, smog1 (1.1%)

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

Lighting

Daylight60 (68.2%)
↓-25.9%prior 81
Dark - roadway not lighted19 (21.6%)
↓-26.9%prior 26
Dark - roadway lighted7 (8.0%)
↑16.7%prior 6
Dawn1 (1.1%)
Dusk1 (1.1%)

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

Road Surface

Dry51 (58.0%)
↓-25.0%prior 68
Snow11 (12.5%)
↓-15.4%prior 13
Wet10 (11.4%)
↓-9.1%prior 11
Gravel10 (11.4%)
↑42.9%prior 7
Ice/frost5 (5.7%)
↓-72.2%prior 18
Slush1 (1.1%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford and Chevrolet models being the most common in both 2018 and 2017, despite a decrease in the absolute number of crashes for both makes. The age demographics of persons involved in crashes showed a shift; the number of individuals in the 16-20 and 21-25 age groups decreased from 36 and 39 respectively in 2017 to 25 and 23 in 2018. Conversely, involvement for the 45-54 and 55-64 age groups increased, from 20 and 22 people respectively in 2017 to 26 and 27 in 2018.

Top Vehicle Makes (150 vehicles)

1
FORD26 (17.3%)
↓-46.9%prior 49
2
CHEV22 (14.7%)
↓-21.4%prior 28
3
DODG13 (8.7%)
↑160.0%prior 5
4
CHEVROLET12 (8%)
↓-25.0%prior 16
5
DODGE7 (4.7%)
↑40.0%prior 5
6
GMC6 (4%)
↑20.0%prior 5
7
PONT6 (4%)
↓-40.0%prior 10
8
DEER4 (2.7%)
9
NISS3 (2%)
10
INTERNATIONA3 (2%)

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

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

Sex Distribution (113 persons with recorded sex)

Male74 (65.5%)
↓-2.6%prior 76
Female39 (34.5%)
↓-37.1%prior 62

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: 93
  • Total persons involved: 197
  • Total vehicles involved: 150

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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