Yearly Traffic Safety Analysis

88 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Howard County, traffic crashes decreased by 20% from 110 in 2015 to 88 in 2016. This overall reduction was accompanied by a 42% drop in total injuries, from 60 to 35, and a decrease in fatalities from two to one. The most notable year-over-year shift was the reduction in total injuries and a change in the primary day for crashes.

88

▼ -20.0%was 110

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

35

▼ -41.7%was 60

Persons Injured

1

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 · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Howard County indicates a downward trend year-over-year. Total crashes fell from 110 in 2015 to 88 in 2016, a 20% decrease. Similarly, the number of people injured in these incidents declined from 60 to 35, and fatalities were reduced from two to one.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 2↓-100.0%

1

Other Killed

Prior: 0→%

2

Cyclists Injured

Prior: 0→%

33

Motorists Injured

Prior: 58↓-43.1%

0

Other Injured

Prior: 1↓-100.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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 a distinct shift between the two periods. In 2016, Monday was the peak day for crashes with 17 incidents, whereas 2015 saw peaks on Wednesday and Saturday, each with 23 crashes. The peak hour for collisions remained consistent at 5 p.m. in both years, though the volume decreased from 12 crashes in 2015 to 10 in 2016.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes and fatalities declined, the severity profile of crashes shifted. The number of fatal crashes remained constant at one in both 2015 and 2016, but the fatal crash rate increased from 0.91% to 1.14%. Crashes resulting in serious injuries increased from four to six, representing a larger share of total incidents (3.6% in 2015 vs. 6.8% in 2016). Conversely, crashes involving minor injuries saw a significant drop from 20 in 2015 to eight in 2016.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.1%
→0.0%prior 1
Serious Injury6serious injury crashes6.8%
↑50.0%prior 4
Minor Injury8minor injury crashes9.1%
↓-60.0%prior 20
Possible Injury14possible injury crashes15.9%
↓-12.5%prior 16
No Injury59no injury crashes67%
↓-14.5%prior 69

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the top contributing factor in both years, though the count decreased from 34 in 2015 to 31 in 2016. Despite this drop in count, animal-related incidents made up a larger share of crashes in 2016 at 35.2% compared to 30.9% in the prior year. The number of crashes attributed to 'Ran off road - straight' doubled from five to 10, making it the second-most common factor in 2016. Incidents involving 'Lost Control' decreased from seven to five.

Officer-Reported Primary Contributing Cause

Animal31 (35.2%)↓-8.8%prior 34
Ran off road - straight10 (11.4%)↑100.0%prior 5
Ran off road - left6 (6.8%)→0.0%prior 6
Lost Control5 (5.7%)↓-28.6%prior 7
Followed too close5 (5.7%)
FTYROW: From stop sign3 (3.4%)
Other (explain in narrative): Other3 (3.4%)
Crossed centerline (undivided)2 (2.3%)
Other (explain in narrative): Vision obstructed2 (2.3%)
Other (explain in narrative): No improper action2 (2.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Year-over-year, crashes decreased across most reported conditions, including those on dry roads (38 to 35) and in clear weather (50 to 34). However, the proportion of crashes occurring on dry roads increased from 34.5% in 2015 to 39.8% in 2016. While crashes in snowy weather decreased from eight to four, incidents on icy or frosty surfaces increased from five to seven.

Weather

Clear34 (56.7%)
↓-32.0%prior 50
Cloudy13 (21.7%)
↓-13.3%prior 15
Snow4 (6.7%)
↓-50.0%prior 8
Freezing rain/drizzle2 (3.3%)
Fog, smoke, smog2 (3.3%)
Blowing Snow2 (3.3%)
Other (explain in narrative)1 (1.7%)
Rain1 (1.7%)
Severe Winds1 (1.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight39 (65.0%)
↓-25.0%prior 52
Dark - roadway not lighted11 (18.3%)
↓-21.4%prior 14
Dark - roadway lighted4 (6.7%)
↓-42.9%prior 7
Dawn4 (6.7%)
Dusk2 (3.3%)
↓-60.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry35 (58.3%)
↓-7.9%prior 38
Ice/frost7 (11.7%)
↑40.0%prior 5
Snow5 (8.3%)
↓-64.3%prior 14
Gravel5 (8.3%)
↓-64.3%prior 14
Wet4 (6.7%)
↓-42.9%prior 7
Slush3 (5.0%)
Water (standing or moving)1 (1.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes showed some changes year-over-year. Ford was the most common make in both periods with 20 vehicles involved. The combined count for Chevrolet and 'Chev' vehicles decreased from 37 in 2015 to 26 in 2016. There was also a notable shift in the age of persons involved in crashes; the 16-20 age group's involvement dropped from 38 individuals to 21, while the 55-64 and 65+ age groups became the most represented, each with 23 individuals in 2016.

Top Vehicle Makes (120 vehicles)

1
FORD20 (16.7%)
→0.0%prior 20
2
CHEVROLET17 (14.2%)
↓-10.5%prior 19
3
CHEV9 (7.5%)
↓-50.0%prior 18
4
BUICK7 (5.8%)
↑40.0%prior 5
5
GMC7 (5.8%)
↓-12.5%prior 8
6
DODGE7 (5.8%)
↑16.7%prior 6
7
DODG5 (4.2%)
8
BUIC4 (3.3%)
9
PONTIAC4 (3.3%)
↓-42.9%prior 7
10
PONT3 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (81 persons with recorded sex)

Male48 (59.3%)
↓-44.2%prior 86
Female33 (40.7%)
↓-36.5%prior 52

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 10, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 88
  • Total persons involved: 145
  • Total vehicles involved: 120

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: 2016." Published September 10, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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