Yearly Traffic Safety Analysis

41 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Adams County recorded 41 traffic crashes, an increase of 7.9% from the 38 crashes reported in 2016. While there were no fatalities in either period, the number of total injuries rose from 14 to 19. A notable shift occurred in crash severity, with the number of crashes involving a serious injury tripling from 2 in 2016 to 6 in 2017.

41

▲ 7.9%was 38

Total Crash Events

0

Persons Killed

19

▲ 35.7%was 14

Persons Injured

0

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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 Adams County increased from 38 in 2016 to 41 in 2017, a rise of 7.9%. This upward trend was also reflected in injuries, which grew by 35.7% from 14 to 19. Fatalities remained at zero for both years.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

19

Motorists Injured

Prior: 14↑35.7%

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 2016 and 2017. The peak day for crashes moved from Monday and Wednesday (8 crashes each) in 2016 to Friday (11 crashes) in 2017. Similarly, the peak hour for collisions shifted from the late afternoon and early evening hours of 4 p.m., 5 p.m., and 8 p.m. in 2016 to later in the evening at 9 p.m. (5 crashes) in 2017.

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

Crash severity increased from 2016 to 2017, although no fatal crashes were recorded in either year. The number of crashes resulting in serious injuries tripled, rising from 2 in 2016 to 6 in 2017. Consequently, the share of crashes involving serious injuries grew from 5.3% to 14.6% year-over-year, while the proportion of crashes with minor injuries decreased from 21.1% to 14.6%.

Outcome by Severity (Crash Events)

Serious Injury6serious injury crashes14.6%
↑200.0%prior 2
Minor Injury6minor injury crashes14.6%
↓-25.0%prior 8
Possible Injury3possible injury crashes7.3%
↑50.0%prior 2
No Injury26no injury crashes63.4%
→0.0%prior 26

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 by 33.3% from 12 crashes in 2016 to 16 in 2017. 'Lost Control' became a more prominent factor, increasing from just 1 crash in 2016 to 5 crashes in 2017, making it the second-most common factor. In the prior year, the second-most cited factor was 'Other' with 4 crashes.

Officer-Reported Primary Contributing Cause

Animal16 (39%)↑33.3%prior 12
Lost Control5 (12.2%)
Other (explain in narrative): Other4 (9.8%)
Ran off road - straight2 (4.9%)
Ran Stop Sign2 (4.9%)
Ran off road - left2 (4.9%)
FTYROW: Making left turn1 (2.4%)
FTYROW: Other (explain in narrative)1 (2.4%)
Driver Distraction: Reaching for object(s)/fallen object(s)1 (2.4%)
Operating vehicle in an reckless, erratic, careless, negligent manner1 (2.4%)

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 most notable change in crash conditions was related to lighting. While daylight was the predominant condition in 2016, accounting for 20 of 38 crashes, in 2017 crashes were more evenly split between daylight (13 crashes) and dark, unlighted roadways (12 crashes). The proportion of crashes on dry road surfaces remained relatively stable, accounting for 60.5% of crashes in 2016 and 58.5% in 2017.

Weather

Clear23 (71.9%)
↑21.1%prior 19
Cloudy5 (15.6%)
↓-16.7%prior 6
Rain2 (6.3%)
Fog, smoke, smog1 (3.1%)
Freezing rain/drizzle1 (3.1%)

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

Lighting

Daylight13 (40.6%)
↓-35.0%prior 20
Dark - roadway not lighted12 (37.5%)
↑33.3%prior 9
Dusk4 (12.5%)
Dark - roadway lighted2 (6.3%)
Dawn1 (3.1%)

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

Road Surface

Dry24 (77.4%)
↑4.3%prior 23
Wet4 (12.9%)
Gravel2 (6.5%)
Ice/frost1 (3.2%)

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

Vehicles & Demographics

Analysis of vehicles and persons involved shows shifts in both make and age demographics. Chevrolet (16 vehicles) and Ford (14 vehicles) were the most common makes in 2017, with both seeing an increase in involvement from 2016, when 9 Chevrolet and 12 Ford vehicles were in crashes. The age distribution of persons involved also changed, with the 16-20 age group seeing a significant increase from 5 individuals in 2016 to 13 in 2017. Conversely, involvement for the 21-25 age group decreased from 12 to 7 persons.

Top Vehicle Makes (53 vehicles)

1
FORD14 (26.4%)
↑16.7%prior 12
2
CHEV9 (17%)
3
CHEVROLET7 (13.2%)
→0.0%prior 7
4
GMC4 (7.5%)
5
JEEP3 (5.7%)
6
PONTIAC2 (3.8%)
7
HYUN2 (3.8%)
8
HYUNDAI1 (1.9%)
9
KENWORTH1 (1.9%)
10
PONT1 (1.9%)

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

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

Sex Distribution (33 persons with recorded sex)

Female17 (51.5%)
↑54.5%prior 11
Male16 (48.5%)
↓-33.3%prior 24

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: 41
  • Total persons involved: 57
  • Total vehicles involved: 53

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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