Yearly Traffic Safety Analysis

202 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Clarke County recorded 202 total crashes, a 5.6% decrease from the 214 crashes recorded in 2015. Despite the overall drop in collisions, the most significant change was the increase in road fatalities, which rose from zero in the prior year to six in the current period. The number of injuries also increased from 75 to 83.

202

▼ -5.6%was 214

Total Crash Events

6

Persons Killed

83

▲ 10.7%was 75

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 traffic crashes in Clarke County saw a slight decline of 5.6% from 2015 to 2016, decreasing from 214 to 202 incidents. However, the severity of these crashes worsened year-over-year. Total injuries increased by 10.7% from 75 to 83, and total fatalities rose from zero to six.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

6

Motorists Killed

Prior: 0→%

3

Pedestrians Injured

Prior: 2↑50.0%

1

Cyclists Injured

Prior: 1→0.0%

79

Motorists Injured

Prior: 71↑11.3%

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 some shifts between the two periods. In 2016, the peak day for crashes was Tuesday with 37 incidents, changing from Wednesday and Saturday in 2015, which both saw 34 incidents. The peak hour for collisions shifted an hour later to 6 p.m. in 2016 (18 crashes) from 5 p.m. in 2015 (21 crashes). November remained the month with the highest crash volume in both years, with 28 crashes recorded in each period.

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

The severity of crashes increased notably in 2016, with the introduction of 4 fatal crashes, which were absent in 2015. These incidents accounted for 2% of all crashes in the current period. While the count of serious injury crashes decreased slightly from 10 to 9, the proportion of crashes resulting in possible injury dropped from 12.6% (27 crashes) in 2015 to 7.9% (16 crashes) in 2016.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 6 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes2%
Serious Injury9serious injury crashes4.5%
↓-10.0%prior 10
Minor Injury22minor injury crashes10.9%
→0.0%prior 22
Possible Injury16possible injury crashes7.9%
↓-40.7%prior 27
No Injury151no injury crashes74.8%
↓-2.6%prior 155

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 years, though the count of such incidents decreased by 20.6% from 63 in 2015 to 50 in 2016. 'Lost Control' became the second-most cited factor in 2016, with its count increasing by 61.5% from 13 to 21 crashes year-over-year. Conversely, crashes attributed to 'Exceeded authorized speed' dropped from 10 incidents in 2015 to just 2 in 2016.

Officer-Reported Primary Contributing Cause

Animal50 (24.8%)↓-20.6%prior 63
Lost Control21 (10.4%)↑61.5%prior 13
Ran off road - straight15 (7.4%)→0.0%prior 15
Other (explain in narrative): Other12 (5.9%)↑71.4%prior 7
FTYROW: From stop sign11 (5.4%)↑10.0%prior 10
Driving too fast for conditions8 (4%)↓-11.1%prior 9
Followed too close8 (4%)↓-20.0%prior 10
FTYROW: Making left turn6 (3%)↓-14.3%prior 7
Driver Distraction: Inattentive/lost in thought6 (3%)
Ran off road - left6 (3%)↓-33.3%prior 9

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

Road & Environmental Conditions

The proportion of crashes occurring in daylight conditions increased from 44.9% (96 crashes) in 2015 to 56.4% (114 crashes) in 2016. Crashes on wet roads saw a notable decrease, falling from 30 incidents to 19 year-over-year. Collisions during snowy weather increased from 9 to 14, while crashes in rainy conditions dropped from 21 to 7.

Weather

Clear90 (55.2%)
↓-9.1%prior 99
Cloudy46 (28.2%)
↑35.3%prior 34
Snow14 (8.6%)
↑55.6%prior 9
Rain7 (4.3%)
↓-66.7%prior 21
Freezing rain/drizzle3 (1.8%)
Fog, smoke, smog2 (1.2%)
Blowing Snow1 (0.6%)

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

Lighting

Daylight114 (69.9%)
↑18.8%prior 96
Dark - roadway not lighted23 (14.1%)
↓-37.8%prior 37
Dark - roadway lighted12 (7.4%)
↓-42.9%prior 21
Dawn7 (4.3%)
↓-30.0%prior 10
Dusk6 (3.7%)
Dark - unknown roadway lighting1 (0.6%)

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

Road Surface

Dry109 (66.9%)
↑2.8%prior 106
Wet19 (11.7%)
↓-36.7%prior 30
Gravel16 (9.8%)
↑14.3%prior 14
Snow11 (6.7%)
↑10.0%prior 10
Ice/frost4 (2.5%)
Slush2 (1.2%)
↓-66.7%prior 6
Other (explain in narrative)1 (0.6%)
Mud, dirt1 (0.6%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Chevrolet, Ford, and Dodge being the most frequent in both periods. A significant demographic shift occurred among persons involved in crashes, with the 65+ age group nearly doubling from 28 individuals in 2015 to 53 in 2016. Conversely, the number of individuals in the 16-20 and 21-25 age groups involved in crashes decreased substantially, falling from 59 to 41 and 51 to 29, respectively.

Top Vehicle Makes (300 vehicles)

1
FORD49 (16.3%)
→0.0%prior 49
2
CHEVROLET39 (13%)
↑85.7%prior 21
3
CHEV36 (12%)
↓-21.7%prior 46
4
DODGE20 (6.7%)
↑42.9%prior 14
5
DODG12 (4%)
↓-47.8%prior 23
6
CHRY10 (3.3%)
↑100.0%prior 5
7
JEEP9 (3%)
↓-25.0%prior 12
8
BUIC7 (2.3%)
↓-22.2%prior 9
9
HOND7 (2.3%)
10
PONT7 (2.3%)
↑40.0%prior 5

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

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

Sex Distribution (217 persons with recorded sex)

Male131 (60.4%)
↓-12.1%prior 149
Female86 (39.6%)
↓-14.0%prior 100

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: 202
  • Total persons involved: 358
  • Total vehicles involved: 300

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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