Yearly Traffic Safety Analysis

312 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Mahaska County recorded 312 total crashes, a 4.4% increase from the 299 crashes reported in 2015. Despite the rise in total incidents and a corresponding 8.5% increase in injuries from 117 to 127, the number of fatalities decreased from 7 to 5 year-over-year.

312

▲ 4.3%was 299

Total Crash Events

5

▼ -28.6%was 7

Persons Killed

127

▲ 8.5%was 117

Persons Injured

5

▲ 25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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 Mahaska County trended upward, with a 4.4% increase from 299 incidents in 2015 to 312 in 2016. This was accompanied by an 8.5% rise in total injuries, from 117 to 127. However, total fatalities saw a decrease, falling from 7 in the prior period to 5 in the current period.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 7↓-42.9%

0

Other Killed

Prior: 0→0.0%

4

Pedestrians Injured

Prior: 1↑300.0%

3

Cyclists Injured

Prior: 2↑50.0%

118

Motorists Injured

Prior: 113↑4.4%

2

Other Injured

Prior: 1↑100.0%

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 showed some year-over-year shifts. While Friday remained the peak day for crashes in both 2016 (65 crashes) and 2015 (55 crashes), the peak hour moved one hour earlier to 3 p.m. in 2016 from 4 p.m. in the prior year. Crashes on Mondays saw a notable increase, rising from 39 to 53 incidents, while Wednesday and Thursday crashes saw a decrease.

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 rate of fatal crashes increased from 1.3% of all incidents in 2015 to 1.6% in 2016, with the total count of fatal crashes rising from 4 to 5. The proportion of crashes resulting in any injury remained relatively stable, increasing from 31.1% to 32.3%. Within injury crashes, the share of serious injury crashes decreased from 4.3% to 3.5%, while the proportion of possible injury crashes grew from 16.4% to 19.2%.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.6%
↑25.0%prior 4
Serious Injury11serious injury crashes3.5%
↓-15.4%prior 13
Minor Injury30minor injury crashes9.6%
↓-3.2%prior 31
Possible Injury60possible injury crashes19.2%
↑22.4%prior 49
No Injury206no injury crashes66%
↑2.0%prior 202

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

Failure to yield from a stop sign remained the top contributing factor in both periods, with the count of such incidents increasing from 27 in 2015 to 34 in 2016. 'Lost Control' was the second most common factor in both years, with counts of 26 and 25, respectively. Notably, crashes attributed to 'Driving too fast for conditions' decreased from 21 to 15, while those involving 'Ran off road - left' more than doubled, from 8 incidents in 2015 to 19 in 2016.

Officer-Reported Primary Contributing Cause

FTYROW: From stop sign34 (10.9%)↑25.9%prior 27
Lost Control25 (8%)↓-3.8%prior 26
Other (explain in narrative): Other20 (6.4%)↑17.6%prior 17
FTYROW: Making left turn20 (6.4%)↑42.9%prior 14
Ran off road - left19 (6.1%)↑137.5%prior 8
Followed too close17 (5.4%)↓-10.5%prior 19
Driving too fast for conditions15 (4.8%)↓-28.6%prior 21
Animal13 (4.2%)↓-18.8%prior 16
Ran Stop Sign13 (4.2%)↑44.4%prior 9
Ran off road - straight12 (3.8%)→0.0%prior 12

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 were predominantly reported in clear weather and on dry roads in both years, though there was a notable year-over-year decrease in crashes occurring under adverse conditions. The proportion of crashes on non-dry road surfaces (wet, snow, ice) fell from 23.4% in 2015 to 15.7% in 2016. Similarly, crashes during adverse weather like rain or snow accounted for 9.0% of incidents in 2016, down from 13.0% in the prior year. Lighting conditions remained consistent, with about 71% of crashes in both periods occurring in daylight.

Weather

Clear201 (65.9%)
↑2.0%prior 197
Cloudy76 (24.9%)
↑43.4%prior 53
Snow10 (3.3%)
→0.0%prior 10
Rain7 (2.3%)
↓-61.1%prior 18
Freezing rain/drizzle4 (1.3%)
↓-20.0%prior 5
Fog, smoke, smog3 (1.0%)
Severe Winds2 (0.7%)
Other (explain in narrative)1 (0.3%)
Blowing Snow1 (0.3%)

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

Lighting

Daylight222 (72.3%)
↑4.7%prior 212
Dark - roadway not lighted38 (12.4%)
→0.0%prior 38
Dark - roadway lighted30 (9.8%)
↓-6.3%prior 32
Dawn8 (2.6%)
↑33.3%prior 6
Dusk7 (2.3%)
Dark - unknown roadway lighting2 (0.7%)

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

Road Surface

Dry250 (81.7%)
↑22.5%prior 204
Wet25 (8.2%)
↓-35.9%prior 39
Snow15 (4.9%)
↓-21.1%prior 19
Ice/frost7 (2.3%)
↓-36.4%prior 11
Gravel6 (2.0%)
↓-57.1%prior 14
Slush2 (0.7%)
Mud, dirt1 (0.3%)

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

Vehicles & Demographics

An analysis of vehicle makes involved in crashes shows a shift in the top rankings. Ford vehicle involvements increased from 94 in 2015 to 125 in 2016, making it the most frequent make in the current period. The age distribution of persons involved in crashes remained consistent year-over-year, with the 16-20 age group being the largest cohort in both 2015 (98 persons) and 2016 (97 persons).

Top Vehicle Makes (536 vehicles)

1
FORD125 (23.3%)
↑33.0%prior 94
2
CHEV62 (11.6%)
↓-19.5%prior 77
3
CHEVROLET55 (10.3%)
↑103.7%prior 27
4
DODG32 (6%)
↓-23.8%prior 42
5
JEEP20 (3.7%)
↑17.6%prior 17
6
DODGE18 (3.4%)
↓-18.2%prior 22
7
PONT17 (3.2%)
→0.0%prior 17
8
GMC15 (2.8%)
↓-21.1%prior 19
9
KIA13 (2.4%)
↑160.0%prior 5
10
PONTIAC13 (2.4%)

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

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

Sex Distribution (400 persons with recorded sex)

Male227 (56.8%)
↓-15.6%prior 269
Female173 (43.3%)
↓-1.1%prior 175

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: 312
  • Total persons involved: 612
  • Total vehicles involved: 536

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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