Yearly Traffic Safety Analysis

107 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Keokuk County, total crashes increased from 91 in 2015 to 107 in 2016, a rise of approximately 17.6%. While total fatalities decreased from 2 to 1, the number of injuries rose by 40.6%, from 32 in the prior year to 45 in the current year. This increase in both total collisions and resulting injuries represents the most significant year-over-year shift in the data.

107

▲ 17.6%was 91

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

45

▲ 40.6%was 32

Persons Injured

1

▼ -50.0%was 2

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 trends in Keokuk County show an increase year-over-year. Total crashes rose by 17.6%, from 91 in 2015 to 107 in 2016. Concurrently, the number of people injured in these incidents increased by 40.6%, while fatalities decreased from two to one.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 2↓-50.0%

45

Motorists Injured

Prior: 30↑50.0%

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 shifted between 2015 and 2016. The most common day for crashes changed from Monday, with 22 incidents in the prior year, to Sunday, with 17 incidents in the current year. The peak time for collisions also moved later into the evening, from the 5 p.m. and 7 p.m. hours in 2015 (10 crashes each) to the 4 p.m. and 10 p.m. hours in 2016 (10 crashes 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

Year-over-year, the number of fatal crashes in Keokuk County decreased from two to one, with their share of total crashes falling from 2.2% to 0.9%. However, crashes resulting in serious injuries increased significantly, rising from 2 incidents (2.2% of total) in 2015 to 7 incidents (6.5% of total) in 2016. The number of minor and possible injury crashes also saw slight increases in count, though their proportion of all crashes remained relatively stable.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.9%
↓-50.0%prior 2
Serious Injury7serious injury crashes6.5%
↑250.0%prior 2
Minor Injury11minor injury crashes10.3%
↑10.0%prior 10
Possible Injury15possible injury crashes14%
↑15.4%prior 13
No Injury73no injury crashes68.2%
↑14.1%prior 64

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 an animal remained the leading contributing factor in both periods, increasing in count from 37 crashes in 2015 to 44 in 2016. 'Lost Control' also held its position as the second most frequent factor, rising from 10 to 11 incidents. A notable change was the increase in crashes attributed to 'Failure to Yield Right of Way: From stop sign,' which grew from 1 crash in the prior year to 6 crashes in the current year. Conversely, crashes related to 'Driving too fast for conditions' decreased from 5 to 3.

Officer-Reported Primary Contributing Cause

Animal44 (41.1%)↑18.9%prior 37
Lost Control11 (10.3%)↑10.0%prior 10
Ran off road - straight9 (8.4%)↑80.0%prior 5
FTYROW: From stop sign6 (5.6%)
Followed too close4 (3.7%)
Driving too fast for conditions3 (2.8%)↓-40.0%prior 5
Operating vehicle in an reckless, erratic, careless, negligent manner3 (2.8%)
Driver Distraction: Other interior distraction3 (2.8%)
Improper Backing2 (1.9%)
Exceeded authorized speed2 (1.9%)

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

Road & Environmental Conditions

Crashes occurring in clear weather and on dry roads increased from 2015 to 2016. Collisions on dry surfaces rose from 36 to 53, and those in clear weather increased from 36 to 41. Conversely, incidents involving adverse road conditions like snow or ice decreased, falling from a combined 14 crashes in 2015 to 4 in 2016. Crashes in daylight conditions also saw an increase, rising from 34 to 46 incidents year-over-year.

Weather

Clear41 (66.1%)
↑13.9%prior 36
Cloudy18 (29.0%)
↑125.0%prior 8
Freezing rain/drizzle2 (3.2%)
Blowing Snow1 (1.6%)

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

Lighting

Daylight46 (70.8%)
↑35.3%prior 34
Dark - roadway not lighted12 (18.5%)
↑33.3%prior 9
Dark - roadway lighted4 (6.2%)
↓-42.9%prior 7
Dawn3 (4.6%)

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

Road Surface

Dry53 (81.5%)
↑47.2%prior 36
Wet6 (9.2%)
↓-25.0%prior 8
Ice/frost3 (4.6%)
Gravel2 (3.1%)
Snow1 (1.5%)
↓-90.0%prior 10

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

Vehicles & Demographics

Ford and Chevrolet were the two most common vehicle makes involved in crashes in both years, with Chevrolet's involvement increasing from 20 vehicles in 2015 to 31 in 2016. An analysis of persons involved shows a shift in age demographics. While the 16-20 age group had high involvement in both periods (38 in 2015 and 29 in 2016), the 45-54 age group saw its involvement more than double, increasing from 12 individuals in the prior year to 30 in the current year.

Top Vehicle Makes (139 vehicles)

1
FORD23 (16.5%)
↓-8.0%prior 25
2
CHEVROLET18 (12.9%)
↑125.0%prior 8
3
CHEV13 (9.4%)
↑8.3%prior 12
4
DODG9 (6.5%)
5
GMC7 (5%)
↓-36.4%prior 11
6
DODGE7 (5%)
↓-30.0%prior 10
7
PETERBILT5 (3.6%)
8
CHRYSLER5 (3.6%)
9
PONT4 (2.9%)
10
OLDSMOBILE4 (2.9%)

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

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

Sex Distribution (104 persons with recorded sex)

Male57 (54.8%)
↓-20.8%prior 72
Female47 (45.2%)
↑20.5%prior 39

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: 107
  • Total persons involved: 165
  • Total vehicles involved: 139

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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