Yearly Traffic Safety Analysis

272 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Jones County recorded 272 total crashes, an 11.1% decrease from the 306 crashes documented in 2016. This overall reduction in collisions was accompanied by a significant drop in traffic fatalities, which fell from 3 in the prior year to 1 in the current year. While total crashes and injuries declined, crashes attributed to driving under the influence (DUI) increased from 5 to 9.

272

▼ -11.1%was 306

Total Crash Events

1

▼ -66.7%was 3

Persons Killed

76

▼ -7.3%was 82

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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

Crash data for Jones County indicates a downward trend year-over-year. Total crashes fell by 11.1%, from 306 in 2016 to 272 in 2017. This trend was accompanied by a decrease in both total injuries, which fell 7.3% from 82 to 76, and total fatalities, which dropped from 3 to 1.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Cyclists Killed

Prior: 0→%

0

Motorists Killed

Prior: 3↓-100.0%

1

Pedestrians Injured

Prior: 3↓-66.7%

0

Cyclists Injured

Prior: 1↓-100.0%

75

Motorists Injured

Prior: 78↓-3.8%

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 temporal patterns of crashes shifted between the two periods. The peak day for collisions moved from Saturday (55 crashes) in 2016 to Wednesday (50 crashes) in 2017. A more pronounced change occurred in the peak hour, which shifted from 9 p.m. in the prior year (27 crashes) to 6 a.m. in the current year (22 crashes), indicating a change from late-evening to early-morning crash concentration.

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

The severity of crashes shows a mixed year-over-year picture, despite a drop in total incidents. The rate of fatal crashes decreased from 0.65% of all crashes in 2016 to 0.37% in 2017. However, the proportion of crashes resulting in a serious injury increased from 3.3% to 5.1% of the total, and possible injury crashes rose from a 9.2% share to a 12.5% share. Consequently, the share of crashes with no injuries decreased from 78.8% to 75.0%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
↓-50.0%prior 2
Serious Injury14serious injury crashes5.1%
↑40.0%prior 10
Minor Injury19minor injury crashes7%
↓-24.0%prior 25
Possible Injury34possible injury crashes12.5%
↑21.4%prior 28
No Injury204no injury crashes75%
↓-15.4%prior 241

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

Collisions involving an animal remained the leading contributing factor in both periods, with the count increasing from 118 incidents in 2016 to 126 in 2017. The second-ranked factor, 'Lost Control', saw a decrease in incidents from 25 to 20. A notable change was the significant drop in crashes attributed to 'Driving too fast for conditions', which fell from 19 incidents in 2016 to just 5 in 2017. Crashes involving 'Driver Distraction: Other interior distraction' doubled in count from 4 to 8 incidents.

Officer-Reported Primary Contributing Cause

Animal126 (46.3%)↑6.8%prior 118
Lost Control20 (7.4%)↓-20.0%prior 25
Ran off road - straight14 (5.1%)↓-6.7%prior 15
FTYROW: From stop sign13 (4.8%)↓-27.8%prior 18
Operating vehicle in an reckless, erratic, careless, negligent manner8 (2.9%)↑33.3%prior 6
Ran off road - left8 (2.9%)↓-20.0%prior 10
Driver Distraction: Other interior distraction8 (2.9%)
Other (explain in narrative): Other7 (2.6%)↓-22.2%prior 9
Followed too close6 (2.2%)↓-50.0%prior 12
Exceeded authorized speed6 (2.2%)

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

Road & Environmental Conditions

While the overall number of crashes decreased, the conditions under which they occurred showed some shifts. The proportion of crashes happening in rainy weather increased from 1.3% of total crashes in 2016 to 4.4% in 2017. Conversely, crashes on snow-covered roads decreased as a share of the total, from 5.2% to 2.2%. The proportion of crashes in daylight conditions decreased from 41.5% to 36.0%, while those in unlit, dark conditions saw a slight proportional increase from 18.6% to 19.5%.

Weather

Clear99 (57.9%)
↓-16.8%prior 119
Cloudy47 (27.5%)
↓-29.9%prior 67
Rain12 (7.0%)
Snow6 (3.5%)
↓-33.3%prior 9
Freezing rain/drizzle3 (1.8%)
Severe Winds2 (1.2%)
Other (explain in narrative)1 (0.6%)
Fog, smoke, smog1 (0.6%)

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

Lighting

Daylight98 (57.3%)
↓-22.8%prior 127
Dark - roadway not lighted53 (31.0%)
↓-7.0%prior 57
Dark - roadway lighted11 (6.4%)
↓-21.4%prior 14
Dusk7 (4.1%)
Dawn2 (1.2%)
↓-66.7%prior 6

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

Road Surface

Dry128 (74.4%)
↓-9.2%prior 141
Wet21 (12.2%)
↓-8.7%prior 23
Gravel9 (5.2%)
↓-40.0%prior 15
Snow6 (3.5%)
↓-62.5%prior 16
Ice/frost4 (2.3%)
↓-66.7%prior 12
Slush2 (1.2%)
Mud, dirt1 (0.6%)
Other (explain in narrative)1 (0.6%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes shifted year-over-year. While Ford and Chevrolet were the top two makes in both periods, Chevrolet vehicles (79 total, combining 'CHEV' and 'CHEVROLET') became the most frequently involved make in 2017, surpassing Ford (57), which had been the top make in 2016 with 95 vehicles. The age demographics of persons involved in crashes remained relatively stable; the 26-34 age group was the most represented in both years, though their total count decreased from 83 to 66, in line with the overall drop in persons involved.

Top Vehicle Makes (356 vehicles)

1
FORD57 (16%)
↓-40.0%prior 95
2
CHEV55 (15.4%)
↑71.9%prior 32
3
CHEVROLET24 (6.7%)
↓-46.7%prior 45
4
DODG17 (4.8%)
↑70.0%prior 10
5
CHRY15 (4.2%)
↑150.0%prior 6
6
GMC15 (4.2%)
↑25.0%prior 12
7
JEEP12 (3.4%)
↑50.0%prior 8
8
BUIC12 (3.4%)
↑71.4%prior 7
9
CHRYSLER11 (3.1%)
↑37.5%prior 8
10
DODGE10 (2.8%)
↓-47.4%prior 19

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

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

Sex Distribution (216 persons with recorded sex)

Male141 (65.3%)
↓-30.2%prior 202
Female75 (34.7%)
↓-31.8%prior 110

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: 272
  • Total persons involved: 417
  • Total vehicles involved: 356

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

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