Yearly Traffic Safety Analysis

360 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Hamilton County, total crashes remained stable year-over-year, with 360 incidents in 2017 compared to 354 in 2016, a 1.7% increase. The most notable year-over-year shift was a significant decrease in crash severity. Despite the slight rise in total crashes, fatalities dropped from 5 to 1, and total injuries decreased by 25.6% from 133 to 99.

360

▲ 1.7%was 354

Total Crash Events

1

▼ -80.0%was 5

Persons Killed

99

▼ -25.6%was 133

Persons Injured

1

▼ -80.0%was 5

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

Overall crash volume in Hamilton County saw a minor increase of 1.7%, from 354 crashes in 2016 to 360 in 2017. However, the outcomes of these crashes improved significantly, with total fatalities falling by 80% (from 5 to 1) and total injuries decreasing by 25.6% (from 133 to 99).

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 1↓-100.0%

1

Motorists Killed

Prior: 4↓-75.0%

1

Cyclists Injured

Prior: 0→%

98

Motorists Injured

Prior: 131↓-25.2%

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 showed some consistency and some shifts between the two periods. Friday remained the peak day for crashes in both 2016 (65 crashes) and 2017 (66 crashes). The peak hour for crashes shifted from 10 a.m. in 2016 (27 crashes) to a tie between 11 a.m. and 5 p.m. in 2017 (25 crashes each).

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 significantly decreased from 2016 to 2017. The number of fatal crashes fell from 5 to 1, reducing the fatal crash rate from 1.4% to 0.3% of all crashes. The proportion of crashes resulting in any type of injury (serious, minor, or possible) also declined, from 27.7% of all crashes in 2016 to 22.7% in 2017. Consequently, the share of no-injury crashes rose from 70.9% to 76.9%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
↓-80.0%prior 5
Serious Injury12serious injury crashes3.3%
↑50.0%prior 8
Minor Injury30minor injury crashes8.3%
↓-18.9%prior 37
Possible Injury40possible injury crashes11.1%
↓-24.5%prior 53
No Injury277no injury crashes76.9%
↑10.4%prior 251

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

The top three contributing factors were consistent across both years: animal involvement, driving too fast for conditions, and lost control. Crashes involving an animal, the top factor, increased in count by 15.7% from 70 incidents in 2016 to 81 in 2017. In contrast, crashes attributed to 'driving too fast for conditions' saw a 30% decrease in count, from 50 incidents to 35, while 'lost control' incidents fell from 38 to 33.

Officer-Reported Primary Contributing Cause

Animal81 (22.5%)↑15.7%prior 70
Driving too fast for conditions35 (9.7%)↓-30.0%prior 50
Lost Control33 (9.2%)↓-13.2%prior 38
Ran off road - straight29 (8.1%)↓-9.4%prior 32
FTYROW: From stop sign18 (5%)↑20.0%prior 15
Other (explain in narrative): Other15 (4.2%)↑7.1%prior 14
Ran off road - left15 (4.2%)↑50.0%prior 10
Ran Stop Sign12 (3.3%)↑33.3%prior 9
Followed too close12 (3.3%)↓-45.5%prior 22
Driver Distraction: Other interior distraction11 (3.1%)↑10.0%prior 10

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

Road & Environmental Conditions

There was a decrease in crashes occurring under adverse conditions from 2016 to 2017. Crashes reported during adverse weather (including cloudy, rain, and snow) fell from 142 incidents to 123. Similarly, the number of crashes on non-dry road surfaces like wet, ice, or snow decreased from 120 to 104. The proportion of crashes in daylight was slightly lower, at 50.8% in 2017 compared to 54.5% in 2016.

Weather

Clear165 (57.3%)
↑10.7%prior 149
Cloudy62 (21.5%)
↑8.8%prior 57
Rain20 (6.9%)
↑66.7%prior 12
Snow17 (5.9%)
↓-45.2%prior 31
Freezing rain/drizzle10 (3.5%)
↑100.0%prior 5
Blowing Snow7 (2.4%)
↓-68.2%prior 22
Severe Winds4 (1.4%)
↓-50.0%prior 8
Fog, smoke, smog3 (1.0%)
↓-50.0%prior 6

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

Lighting

Daylight183 (63.8%)
↓-5.2%prior 193
Dark - roadway not lighted52 (18.1%)
↓-1.9%prior 53
Dark - roadway lighted31 (10.8%)
↑24.0%prior 25
Dusk11 (3.8%)
↑10.0%prior 10
Dawn9 (3.1%)
→0.0%prior 9
Dark - unknown roadway lighting1 (0.3%)

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

Road Surface

Dry186 (64.1%)
↑8.1%prior 172
Wet40 (13.8%)
↑66.7%prior 24
Ice/frost33 (11.4%)
↓-26.7%prior 45
Snow21 (7.2%)
↓-43.2%prior 37
Gravel8 (2.8%)
↑14.3%prior 7
Mud, dirt1 (0.3%)
Slush1 (0.3%)
↓-80.0%prior 5

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

Vehicles & Demographics

The top vehicle makes involved in collisions, Ford and Chevrolet, remained consistent in both rank and volume year-over-year. An analysis of the age of persons involved in crashes reveals a shift between periods. The share of individuals in the 16-20 age group decreased from 14.9% of total persons in 2016 to 12.0% in 2017, while the proportion of those in the 26-34 and 65+ age groups increased.

Top Vehicle Makes (519 vehicles)

1
FORD78 (15%)
↓-1.3%prior 79
2
CHEVROLET67 (12.9%)
↓-10.7%prior 75
3
CHEV55 (10.6%)
↑12.2%prior 49
4
GMC17 (3.3%)
↓-22.7%prior 22
5
BUIC17 (3.3%)
↑112.5%prior 8
6
DODG16 (3.1%)
→0.0%prior 16
7
DODGE15 (2.9%)
↓-31.8%prior 22
8
PONT13 (2.5%)
↑30.0%prior 10
9
TOYT13 (2.5%)
↑30.0%prior 10
10
CHRY12 (2.3%)
↑20.0%prior 10

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

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

Sex Distribution (384 persons with recorded sex)

Male235 (61.2%)
↓-5.6%prior 249
Female149 (38.8%)
↑18.3%prior 126

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: 360
  • Total persons involved: 594
  • Total vehicles involved: 519

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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