Yearly Traffic Safety Analysis

216 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Appanoose County recorded 216 total crashes, a 6.1% decrease from the 230 crashes reported in 2015. Despite the overall decline in collisions, the number of fatalities more than doubled, increasing from 2 in 2015 to 5 in 2016. This rise in fatalities occurred alongside a 30.3% reduction in total injuries, which fell from 109 to 76.

216

▼ -6.1%was 230

Total Crash Events

5

▲ 150.0%was 2

Persons Killed

76

▼ -30.3%was 109

Persons Injured

4

▲ 100.0%was 2

Fatal Crash Events

Note: "Persons Killed" (5) 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 Appanoose County showed a downward trend, decreasing by 6.1% from 230 in 2015 to 216 in 2016. This was accompanied by a significant 30.3% drop in the number of people injured, from 109 to 76. However, this positive trend did not extend to crash severity, as fatalities rose from 2 to 5 year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 2↑150.0%

5

Pedestrians Injured

Prior: 2↑150.0%

71

Motorists Injured

Prior: 106↓-33.0%

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

Friday remained the day with the most crashes in both periods, though the count dropped from 61 in 2015 to 44 in 2016. The peak hour for crashes in 2015 was 6 PM, with 16 incidents. In 2016, the evening period remained a high-risk time, with both 6 PM and 7 PM recording the highest number of crashes at 16 each.

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 declined, their severity increased in 2016. The number of fatal crashes doubled from 2 to 4, causing the share of fatal crashes to rise from 0.9% to 1.9% of all incidents. Conversely, the proportion of crashes resulting in any type of injury fell from 35.7% in 2015 to 25.9% in 2016. This was mainly due to a decrease in minor and possible injury crashes, while the share of serious injury crashes remained stable at approximately 5%.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.9%
↑100.0%prior 2
Serious Injury11serious injury crashes5.1%
→0.0%prior 11
Minor Injury15minor injury crashes6.9%
↓-55.9%prior 34
Possible Injury30possible injury crashes13.9%
↓-18.9%prior 37
No Injury156no injury crashes72.2%
↑6.8%prior 146

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 leading contributing factor in both years, with the count of such crashes increasing by 23% from 61 in 2015 to 75 in 2016. 'Lost Control' was the second most common factor in both periods, with a stable count of 24 and 25 crashes, respectively. 'Ran off road - straight' saw a significant 75% increase in count, rising from 12 incidents in 2015 to 21 in 2016, making it the third most cited factor in the current period.

Officer-Reported Primary Contributing Cause

Animal75 (34.7%)↑23.0%prior 61
Lost Control25 (11.6%)↑4.2%prior 24
Ran off road - straight21 (9.7%)↑75.0%prior 12
Ran off road - left10 (4.6%)↓-33.3%prior 15
Other (explain in narrative): Other8 (3.7%)↑14.3%prior 7
Driving too fast for conditions7 (3.2%)↑16.7%prior 6
Driver Distraction: Other interior distraction7 (3.2%)↓-36.4%prior 11
Followed too close7 (3.2%)
FTYROW: From stop sign6 (2.8%)↓-33.3%prior 9
Made improper turn5 (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

The proportion of crashes occurring in clear weather decreased from 55.7% in 2015 to 48.1% in 2016, while crashes on dry roads fell from 61.3% to 57.4%. Conversely, the share of incidents during adverse weather conditions (such as cloudy, rain, or snow) increased from 23.9% to 30.1% year-over-year. The distribution of crashes by lighting conditions remained relatively stable, with daylight crashes accounting for just under half of all incidents in both years.

Weather

Clear104 (61.5%)
↓-18.8%prior 128
Cloudy44 (26.0%)
↑29.4%prior 34
Rain9 (5.3%)
↓-25.0%prior 12
Snow9 (5.3%)
↑50.0%prior 6
Fog, smoke, smog2 (1.2%)
Freezing rain/drizzle1 (0.6%)

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

Lighting

Daylight101 (59.4%)
↓-9.8%prior 112
Dark - roadway not lighted46 (27.1%)
↑7.0%prior 43
Dark - roadway lighted11 (6.5%)
↓-47.6%prior 21
Dawn6 (3.5%)
Dusk5 (2.9%)
→0.0%prior 5
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

Dry124 (72.5%)
↓-12.1%prior 141
Wet16 (9.4%)
↓-11.1%prior 18
Snow15 (8.8%)
↑114.3%prior 7
Gravel9 (5.3%)
↑12.5%prior 8
Ice/frost7 (4.1%)
↑16.7%prior 6

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

Vehicles & Demographics

Chevrolet and Ford remained the top two vehicle makes involved in crashes in both years, although both saw a reduction in counts from 2015 to 2016. In 2016, Toyota replaced Dodge as the third most frequently involved vehicle make. An analysis of persons involved in crashes shows a notable decrease in the 16-20 age group, with their count falling from 83 in 2015 to 59 in 2016. The 26-34 age group represented the largest share of persons involved in 2016 at 20.2%, an increase from their 18.6% share in the prior year.

Top Vehicle Makes (304 vehicles)

1
FORD50 (16.4%)
↓-16.7%prior 60
2
CHEV33 (10.9%)
↓-23.3%prior 43
3
CHEVROLET32 (10.5%)
↑18.5%prior 27
4
TOYT18 (5.9%)
5
PONT15 (4.9%)
↓-11.8%prior 17
6
DODGE12 (3.9%)
↓-20.0%prior 15
7
DODG11 (3.6%)
↓-56.0%prior 25
8
JEEP10 (3.3%)
↓-52.4%prior 21
9
TOYOTA10 (3.3%)
↑11.1%prior 9
10
GMC9 (3%)
↓-30.8%prior 13

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

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

Sex Distribution (219 persons with recorded sex)

Male113 (51.6%)
↓-39.6%prior 187
Female106 (48.4%)
↑1.9%prior 104

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: 216
  • Total persons involved: 355
  • Total vehicles involved: 304

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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