Yearly Traffic Safety Analysis

119 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Monroe County, total traffic crashes decreased from 128 in 2015 to 119 in 2016, a reduction of 7%. While the number of fatalities remained stable at two, the total number of injuries saw a significant decline of 38.9%, falling from 36 to 22. The most notable shift in crash causation was the increase in collisions involving animals, which rose from 46 incidents in 2015 to 57 in 2016, becoming the leading contributing factor in nearly half of all crashes in the current period.

119

▼ -7.0%was 128

Total Crash Events

2

Persons Killed

22

▼ -38.9%was 36

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

Overall, Monroe County experienced a downward trend in traffic incidents between 2015 and 2016, with total crashes decreasing by 7% from 128 to 119. This positive trend was more pronounced in crash outcomes, as total injuries dropped by nearly 39% from 36 to 22. The number of fatalities held steady at two for both years.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 2→0.0%

22

Motorists Injured

Prior: 35↓-37.1%

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 in Monroe County showed a notable shift between 2015 and 2016. The peak day for crashes moved from Monday (25 crashes) in 2015 to Tuesday (25 crashes) in 2016. A more significant change occurred in the peak hour, which shifted from 9 p.m. in the prior year (11 crashes) to 3 p.m. in the current year (11 crashes), moving the highest concentration of incidents from the late evening to the afternoon.

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 the number of fatal crashes remained unchanged at two in both 2015 and 2016, the fatal crash rate saw a slight increase from 1.56 to 1.68 per 100 crashes due to the lower overall crash total in 2016. The proportion of crashes resulting in any injury decreased, from 20.3% of all crashes in 2015 (26 incidents) to 13.5% in 2016 (16 incidents). Consequently, the share of crashes with no injuries rose from 78.1% in the prior year to 84.9% in the current year.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.7%
→0.0%prior 2
Minor Injury7minor injury crashes5.9%
↑75.0%prior 4
Possible Injury9possible injury crashes7.6%
↓-50.0%prior 18
No Injury101no injury crashes84.9%
↑1.0%prior 100

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 involving an animal remained the top contributing factor in both periods, with the count increasing by 23.9% from 46 crashes in 2015 to 57 in 2016; its share of total crashes grew from 35.9% to 47.9%. The second most common factor in 2015, 'Lost Control,' saw its incident count cut in half, falling from 12 crashes to 6. In 2016, 'Lost Control' tied for second place with 'FTYROW: From stop sign' and 'Followed too close,' each cited in 6 crashes.

Officer-Reported Primary Contributing Cause

Animal57 (47.9%)↑23.9%prior 46
Lost Control6 (5%)↓-50.0%prior 12
FTYROW: From stop sign6 (5%)↓-14.3%prior 7
Followed too close6 (5%)↓-14.3%prior 7
Ran off road - left4 (3.4%)
Passing: Other passing (explain in narrative)4 (3.4%)
Driver Distraction: Inattentive/lost in thought4 (3.4%)
Other (explain in narrative): Other4 (3.4%)
Ran Stop Sign3 (2.5%)↓-57.1%prior 7
Ran off road - straight3 (2.5%)↓-57.1%prior 7

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

Road & Environmental Conditions

Crashes under adverse conditions decreased notably from 2015 to 2016. The number of incidents on wet, snowy, or icy roads fell from 18 in 2015 to just 5 in 2016. Similarly, crashes during rainy weather dropped from 6 to zero. There was also a significant reduction in crashes occurring in dark, unlighted conditions, which decreased from 28 incidents in 2015 to 12 in 2016.

Weather

Clear55 (79.7%)
↓-21.4%prior 70
Cloudy12 (17.4%)
↓-14.3%prior 14
Freezing rain/drizzle2 (2.9%)

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

Lighting

Daylight54 (77.1%)
↑3.8%prior 52
Dark - roadway not lighted12 (17.1%)
↓-57.1%prior 28
Dark - roadway lighted2 (2.9%)
↓-75.0%prior 8
Dark - unknown roadway lighting1 (1.4%)
Dawn1 (1.4%)

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

Road Surface

Dry60 (85.7%)
↓-4.8%prior 63
Gravel4 (5.7%)
↓-50.0%prior 8
Snow2 (2.9%)
Wet2 (2.9%)
↓-81.8%prior 11
Ice/frost1 (1.4%)
Other (explain in narrative)1 (1.4%)

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

Vehicles & Demographics

An analysis of vehicles and persons involved shows shifts in both make and age demographics. While Ford and Chevrolet remained the most common vehicle makes in crashes, the number of Chevrolet vehicles involved decreased from 47 in 2015 to 35 in 2016, while Ford involvement slightly increased from 32 to 35. Regarding persons involved, there was a sharp drop in the 16-20 age group, from 31 individuals in 2015 to 10 in 2016. Conversely, the 65 and older age group saw an increase in involvement from 25 to 31 persons.

Top Vehicle Makes (170 vehicles)

1
FORD35 (20.6%)
↑9.4%prior 32
2
CHEV18 (10.6%)
↓-30.8%prior 26
3
CHEVROLET17 (10%)
↓-19.0%prior 21
4
DODGE12 (7.1%)
↑71.4%prior 7
5
BUICK10 (5.9%)
6
BUIC9 (5.3%)
↑50.0%prior 6
7
TOYT7 (4.1%)
8
DODG5 (2.9%)
↓-37.5%prior 8
9
CHRYSLER5 (2.9%)
10
JEEP5 (2.9%)
→0.0%prior 5

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

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

Sex Distribution (140 persons with recorded sex)

Male83 (59.3%)
↓-20.2%prior 104
Female57 (40.7%)
↓-20.8%prior 72

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 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 119
  • Total persons involved: 188
  • Total vehicles involved: 170

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 9, 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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