Yearly Traffic Safety Analysis

194 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Jackson County recorded 194 total vehicle crashes, an increase of 6% from the 183 crashes reported in 2016. While total fatalities remained unchanged at four, the number of crashes resulting in serious injuries more than doubled, rising from 7 in 2016 to 16 in 2017.

194

▲ 6.0%was 183

Total Crash Events

4

Persons Killed

94

▼ -2.1%was 96

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Jackson County increased by 6% from 183 in 2016 to 194 in 2017. Despite this rise in total collisions, the number of people injured saw a slight decrease from 96 to 94, while fatalities held steady at four for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 4→0.0%

2

Pedestrians Injured

Prior: 1↑100.0%

2

Cyclists Injured

Prior: 1↑100.0%

90

Motorists Injured

Prior: 94↓-4.3%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The daily pattern of crashes shifted between the two periods. In 2016, Saturday was the distinct peak day for crashes with 33 incidents. In 2017, the peak broadened, with Wednesday, Friday, and Saturday all sharing the highest frequency at 33 crashes each. The peak hour for collisions also shifted slightly later in the day, moving from 3 p.m. in 2016 (14 crashes) to 4 p.m. in 2017 (18 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the number of fatal crashes remained unchanged at four in both 2016 and 2017, the distribution of injury severity shifted significantly. The count of serious injury crashes more than doubled, increasing from 7 in 2016 to 16 in 2017, representing a rise from 3.8% to 8.2% of all crashes. Conversely, crashes involving possible injuries decreased from 45 incidents (24.6% share) in 2016 to 33 incidents (17.0% share) in 2017.

Outcome by Severity (Crash Events)

Fatal4fatal crashes2.1%
→0.0%prior 4
Serious Injury16serious injury crashes8.2%
↑128.6%prior 7
Minor Injury23minor injury crashes11.9%
↑21.1%prior 19
Possible Injury33possible injury crashes17%
↓-26.7%prior 45
No Injury118no injury crashes60.8%
↑9.3%prior 108

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

'Lost Control' was the leading contributing factor in both years, though its count decreased from 38 incidents in 2016 to 31 in 2017. The most significant year-over-year change was the rise in crashes involving animals, which increased by 50% from 12 incidents in 2016 to 18 in 2017, becoming the second-most common factor. Meanwhile, crashes attributed to 'Failure to Yield from a stop sign' fell from 20 to 17, and 'Ran off road - straight' incidents dropped from 17 to 11.

Officer-Reported Primary Contributing Cause

Lost Control31 (16%)↓-18.4%prior 38
Animal18 (9.3%)↑50.0%prior 12
FTYROW: From stop sign17 (8.8%)↓-15.0%prior 20
Other (explain in narrative): Other14 (7.2%)↑133.3%prior 6
Ran off road - straight11 (5.7%)↓-35.3%prior 17
Followed too close10 (5.2%)↑42.9%prior 7
Ran Stop Sign8 (4.1%)↑14.3%prior 7
FTYROW: Making left turn8 (4.1%)↑14.3%prior 7
Driver Distraction: Exterior distraction7 (3.6%)
Swerving/Evasive Action6 (3.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The environmental conditions under which crashes occurred showed some year-over-year changes, particularly in lighting. While the number of daylight crashes was identical at 119 for both periods, collisions in dark, unlighted conditions increased by 50%, from 28 incidents in 2016 to 42 in 2017. In terms of road surface, crashes on dry roads increased from 123 to 139, consistent with the overall rise in collisions. Weather conditions remained broadly similar, with 'Clear' being the most common condition in both years.

Weather

Clear120 (63.8%)
↑5.3%prior 114
Cloudy44 (23.4%)
↑4.8%prior 42
Rain7 (3.7%)
↓-22.2%prior 9
Fog, smoke, smog6 (3.2%)
Freezing rain/drizzle5 (2.7%)
Snow4 (2.1%)
Other (explain in narrative)1 (0.5%)
Severe Winds1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight119 (63.3%)
→0.0%prior 119
Dark - roadway not lighted42 (22.3%)
↑50.0%prior 28
Dark - roadway lighted14 (7.4%)
↓-22.2%prior 18
Dawn9 (4.8%)
↑80.0%prior 5
Dark - unknown roadway lighting3 (1.6%)
Dusk1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry139 (73.9%)
↑13.0%prior 123
Gravel18 (9.6%)
↑38.5%prior 13
Wet18 (9.6%)
↓-10.0%prior 20
Ice/frost8 (4.3%)
↑33.3%prior 6
Snow5 (2.7%)
↓-44.4%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

Chevrolet and Ford were the two most common vehicle makes involved in crashes during both periods, with Chevrolet-made vehicles increasing from 64 to 72 and Fords from 46 to 52. The age demographics of persons involved in crashes shifted, showing a notable increase in younger individuals; the 16-20 age group grew from 51 to 67 people, and the 21-25 age group increased from 25 to 43 people. Conversely, the number of people aged 26-34 involved in crashes decreased from 47 to 33.

Top Vehicle Makes (314 vehicles)

1
FORD52 (16.6%)
↑13.0%prior 46
2
CHEV48 (15.3%)
↑37.1%prior 35
3
CHEVROLET24 (7.6%)
↓-17.2%prior 29
4
CHRY16 (5.1%)
↑128.6%prior 7
5
DODG15 (4.8%)
↑7.1%prior 14
6
DODGE11 (3.5%)
→0.0%prior 11
7
BUIC10 (3.2%)
↓-23.1%prior 13
8
PONT10 (3.2%)
↓-16.7%prior 12
9
HONDA9 (2.9%)
↑80.0%prior 5
10
GMC8 (2.5%)
→0.0%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (230 persons with recorded sex)

Male125 (54.3%)
↑4.2%prior 120
Female105 (45.7%)
↑38.2%prior 76

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 194
  • Total persons involved: 367
  • Total vehicles involved: 314

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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