Yearly Traffic Safety Analysis

175 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Jackson County recorded 175 total crashes, a 9.8% decrease from the 194 crashes reported in 2017. This downward trend was also reflected in crash outcomes, with total fatalities decreasing from 4 to 2 and total injuries declining from 94 to 80 year-over-year. The most notable shift in contributing factors was a tripling in the count of crashes involving interior driver distraction.

175

▼ -9.8%was 194

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

80

▼ -14.9%was 94

Persons Injured

2

▼ -50.0%was 4

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

Traffic crashes in Jackson County showed a declining trend from 2017 to 2018. The total number of crashes fell by 9.8%, from 194 to 175. This decrease was accompanied by a 50% reduction in fatalities, from 4 to 2, and a 14.9% drop in total injuries, from 94 to 80.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 4↓-50.0%

0

Other Killed

Prior: 0→0.0%

79

Motorists Injured

Prior: 90↓-12.2%

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 shifted between the two periods. In 2018, Friday was the definitive peak day with 36 crashes, whereas 2017 saw a three-way tie for the peak day between Wednesday, Friday, and Saturday (33 crashes each). The peak hour for crashes also moved later in the evening, from the 4 PM hour in 2017 (18 crashes) to a tie between the 5 PM and 6 PM hours in 2018 (14 crashes each).

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 decreased from 2017 to 2018. The number of fatal crashes was halved from 4 to 2, causing the fatal crash share to drop from 2.1% to 1.1% of all incidents. Crashes resulting in serious injuries also saw a significant reduction, falling by 50% from 16 in 2017 to 8 in 2018. Consequently, the share of non-injury crashes increased from 60.8% to 65.1% of all crashes.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.1%
↓-50.0%prior 4
Serious Injury8serious injury crashes4.6%
↓-50.0%prior 16
Minor Injury17minor injury crashes9.7%
↓-26.1%prior 23
Possible Injury34possible injury crashes19.4%
↑3.0%prior 33
No Injury114no injury crashes65.1%
↓-3.4%prior 118

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

While 'Lost Control' remained the top contributing factor in both periods with nearly stable counts (31 in 2017 vs. 30 in 2018), the ranking of other factors shifted. Crashes attributed to 'Animal' decreased by count from 18 to 11, falling from the second to the fifth most common factor. Conversely, crashes involving 'Driver Distraction: Other interior distraction' tripled in count from 4 to 12, and incidents of 'Ran off road - straight' increased from 11 to 15.

Officer-Reported Primary Contributing Cause

Lost Control30 (17.1%)↓-3.2%prior 31
FTYROW: From stop sign17 (9.7%)→0.0%prior 17
Ran off road - straight15 (8.6%)↑36.4%prior 11
Driver Distraction: Other interior distraction12 (6.9%)
Animal11 (6.3%)↓-38.9%prior 18
Other (explain in narrative): Other10 (5.7%)↓-28.6%prior 14
Followed too close8 (4.6%)↓-20.0%prior 10
Driving too fast for conditions8 (4.6%)↑60.0%prior 5
Ran Stop Sign6 (3.4%)↓-25.0%prior 8
FTYROW: Making left turn5 (2.9%)↓-37.5%prior 8

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 majority of crashes in both 2018 and 2017 occurred in clear weather and daylight on dry roads, with proportions remaining largely consistent year-over-year. However, there was a notable increase in crashes on adverse road surfaces. The combined count of crashes on roads with ice, frost, or snow rose from 13 in 2017 to 22 in 2018, despite an overall decrease in total crashes.

Weather

Clear104 (60.8%)
↓-13.3%prior 120
Cloudy46 (26.9%)
↑4.5%prior 44
Rain8 (4.7%)
↑14.3%prior 7
Snow7 (4.1%)
Freezing rain/drizzle3 (1.8%)
↓-40.0%prior 5
Fog, smoke, smog3 (1.8%)
↓-50.0%prior 6

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

Lighting

Daylight105 (61.4%)
↓-11.8%prior 119
Dark - roadway not lighted33 (19.3%)
↓-21.4%prior 42
Dark - roadway lighted16 (9.4%)
↑14.3%prior 14
Dawn12 (7.0%)
↑33.3%prior 9
Dusk3 (1.8%)
Dark - unknown roadway lighting2 (1.2%)

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

Road Surface

Dry123 (72.4%)
↓-11.5%prior 139
Wet16 (9.4%)
↓-11.1%prior 18
Ice/frost13 (7.6%)
↑62.5%prior 8
Snow9 (5.3%)
↑80.0%prior 5
Gravel8 (4.7%)
↓-55.6%prior 18
Slush1 (0.6%)

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

Vehicles & Demographics

The demographic and vehicle make data reveals several year-over-year shifts. While Ford was the most common vehicle make in crashes in 2017 (52 vehicles), it was surpassed by Chevrolet-branded vehicles (a combination of 'CHEV' and 'CHEVROLET' records) in 2018, which increased from 72 to 80 vehicles involved. The age distribution of persons in crashes also changed, with a marked decrease in the 16-20 age group (from 67 to 44 persons). Conversely, the number of persons in the 55-64 age group increased from 41 to 56, and the 26-34 age group grew from 33 to 54.

Top Vehicle Makes (285 vehicles)

1
CHEV49 (17.2%)
↑2.1%prior 48
2
FORD46 (16.1%)
↓-11.5%prior 52
3
CHEVROLET31 (10.9%)
↑29.2%prior 24
4
DODG14 (4.9%)
↓-6.7%prior 15
5
JEEP14 (4.9%)
↑100.0%prior 7
6
BUIC11 (3.9%)
↑10.0%prior 10
7
PONT11 (3.9%)
↑10.0%prior 10
8
NISS10 (3.5%)
↑100.0%prior 5
9
HOND8 (2.8%)
↑33.3%prior 6
10
GMC7 (2.5%)
↓-12.5%prior 8

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

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

Sex Distribution (222 persons with recorded sex)

Male129 (58.1%)
↑3.2%prior 125
Female93 (41.9%)
↓-11.4%prior 105

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: 175
  • Total persons involved: 355
  • Total vehicles involved: 285

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

ThatCarHitMe.com · An Injuria.ai Company