Yearly Traffic Safety Analysis

192 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Chickasaw County recorded 192 total crashes, a 31.5% increase from the 146 crashes reported in 2015. The most significant year-over-year shift was a sharp rise in crash severity, with total fatalities increasing from one in 2015 to five in 2016, and fatal crashes rising from one to three.

192

▲ 31.5%was 146

Total Crash Events

5

▲ 400.0%was 1

Persons Killed

52

▲ 48.6%was 35

Persons Injured

3

▲ 200.0%was 1

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 Chickasaw County indicates a rising trend in 2016 compared to the prior year. Total crashes increased by 31.5%, from 146 in 2015 to 192 in 2016. This was accompanied by a 48.6% increase in injuries, from 35 to 52, and a 400% increase in fatalities, from one to five.

Vulnerable Road User Casualties

5

Motorists Killed

Prior: 1↑400.0%

52

Motorists Injured

Prior: 34↑52.9%

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. While the peak hour for crashes remained 5 p.m. in both 2016 (25 crashes) and 2015 (14 crashes), the peak day shifted from Monday (30 crashes) in 2015 to Thursday (33 crashes) in 2016. Crashes during the 5 p.m. peak hour increased by 78.6% year-over-year.

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

Crash severity increased in 2016 compared to 2015. The number of fatal crashes rose from one to three, with the corresponding share of total crashes increasing from 0.7% to 1.6%. While the number of serious injury crashes remained stable at four, crashes involving possible injuries increased from 9 to 15. Overall, crashes resulting in no injury represented a slightly smaller proportion of the total, decreasing from 80.1% in 2015 to 79.2% in 2016.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.6%
↑200.0%prior 1
Serious Injury4serious injury crashes2.1%
→0.0%prior 4
Minor Injury18minor injury crashes9.4%
↑20.0%prior 15
Possible Injury15possible injury crashes7.8%
↑66.7%prior 9
No Injury152no injury crashes79.2%
↑29.9%prior 117

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

While collisions involving an animal remained the top contributing factor in both years, the count was stable, moving from 65 crashes in 2015 to 63 in 2016. Several other factors saw significant increases in crash counts year-over-year. Crashes attributed to 'Driving too fast for conditions' increased by 250%, from 4 to 14 incidents. Similarly, 'Lost Control' incidents rose from 12 to 20, and crashes from 'Followed too close' increased from 3 to 10.

Officer-Reported Primary Contributing Cause

Animal63 (32.8%)↓-3.1%prior 65
Lost Control20 (10.4%)↑66.7%prior 12
Driving too fast for conditions14 (7.3%)
Followed too close10 (5.2%)
FTYROW: From stop sign7 (3.6%)
FTYROW: From driveway7 (3.6%)
Ran off road - left6 (3.1%)↓-25.0%prior 8
Ran off road - straight6 (3.1%)↑20.0%prior 5
Ran Stop Sign5 (2.6%)
Made improper turn5 (2.6%)

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 and daylight remained relatively stable year-over-year. However, there was a notable increase in crashes during adverse road surface conditions in 2016. Incidents on roads with ice or frost quadrupled, rising from 4 in 2015 to 16 in 2016. Similarly, crashes on snowy surfaces increased from 13 to 18.

Weather

Clear91 (64.1%)
↑31.9%prior 69
Cloudy24 (16.9%)
↓-7.7%prior 26
Snow11 (7.7%)
Blowing Snow6 (4.2%)
Rain5 (3.5%)
↓-16.7%prior 6
Freezing rain/drizzle4 (2.8%)
Other (explain in narrative)1 (0.7%)

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

Lighting

Daylight92 (65.2%)
↑37.3%prior 67
Dark - roadway not lighted28 (19.9%)
↓-20.0%prior 35
Dusk8 (5.7%)
Dark - roadway lighted7 (5.0%)
Dawn5 (3.5%)
Dark - unknown roadway lighting1 (0.7%)

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

Road Surface

Dry81 (57.4%)
↑3.8%prior 78
Snow18 (12.8%)
↑38.5%prior 13
Ice/frost16 (11.3%)
Wet11 (7.8%)
↓-8.3%prior 12
Gravel10 (7.1%)
Slush5 (3.5%)

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

Vehicles & Demographics

The vehicle makes most frequently involved in crashes remained consistent, with Chevrolet and Ford leading in both 2016 and 2015. The number of vehicles from these top makes involved in crashes increased, reflecting the overall rise in incidents. An analysis of persons involved shows the 16-20 age group had a notable increase in representation, growing from 28 individuals in 2015 to 53 in 2016. The 45-54 age group remained the most frequently involved demographic in both years, with their numbers increasing from 42 to 56.

Top Vehicle Makes (283 vehicles)

1
CHEVROLET52 (18.4%)
↑33.3%prior 39
2
FORD39 (13.8%)
↑34.5%prior 29
3
CHEV23 (8.1%)
↑130.0%prior 10
4
GMC22 (7.8%)
↑57.1%prior 14
5
DODGE19 (6.7%)
↑26.7%prior 15
6
BUICK11 (3.9%)
→0.0%prior 11
7
CHRYSLER10 (3.5%)
↑42.9%prior 7
8
TOYOTA10 (3.5%)
↑66.7%prior 6
9
INTERNATIONA9 (3.2%)
10
PONTIAC8 (2.8%)
↓-33.3%prior 12

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

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

Sex Distribution (234 persons with recorded sex)

Male140 (59.8%)
↑11.1%prior 126
Female94 (40.2%)
↑44.6%prior 65

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: 192
  • Total persons involved: 317
  • Total vehicles involved: 283

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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