Yearly Traffic Safety Analysis

586 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Jasper County recorded 586 total crashes, a 3.2% increase from 568 crashes in 2015. While total crashes rose, fatalities decreased from 5 to 4. The most notable shift was an increase in the number of serious injury crashes, which rose from 18 to 26 year-over-year.

586

▲ 3.2%was 568

Total Crash Events

4

▼ -20.0%was 5

Persons Killed

204

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (4) 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 Jasper County saw a slight upward trend, increasing by 3.2% from 568 in 2015 to 586 in 2016. Despite the rise in total incidents, the number of fatalities decreased from 5 to 4, and the total number of injuries remained constant at 204 for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 5↓-20.0%

3

Pedestrians Injured

Prior: 3→0.0%

4

Cyclists Injured

Prior: 0→%

197

Motorists Injured

Prior: 201↓-2.0%

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 the two periods. The peak day for crashes moved from Tuesday (98 crashes) in 2015 to Saturday (98 crashes) in 2016. Similarly, the peak hour for crashes shifted two hours earlier, from 6 p.m. (48 crashes) in the prior year to 4 p.m. (42 crashes) in the current 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

The number of fatal crashes remained unchanged at 4 incidents in both 2016 and 2015, with the fatal crash rate holding steady at approximately 0.7% of all crashes. However, the distribution of injury severity changed, with serious injury crashes increasing from 18 to 26, a 44.4% rise in count. Crashes resulting in possible injuries decreased from 75 to 62 during the same period.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.7%
→0.0%prior 4
Serious Injury26serious injury crashes4.4%
↑44.4%prior 18
Minor Injury73minor injury crashes12.5%
↑23.7%prior 59
Possible Injury62possible injury crashes10.6%
↓-17.3%prior 75
No Injury421no injury crashes71.8%
↑2.2%prior 412

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 counts of 113 in 2016 and 109 in 2015. The second-most cited factor, 'Lost Control,' saw a significant increase in incidents, rising from 54 crashes in 2015 to 71 in 2016. Crashes attributed to 'Followed too close' also grew from 22 to 33 incidents, while 'Driving too fast for conditions' decreased from 51 to 48 incidents.

Officer-Reported Primary Contributing Cause

Animal113 (19.3%)↑3.7%prior 109
Lost Control71 (12.1%)↑31.5%prior 54
Ran off road - straight61 (10.4%)↑1.7%prior 60
Driving too fast for conditions48 (8.2%)↓-5.9%prior 51
Ran off road - left36 (6.1%)↓-12.2%prior 41
Followed too close33 (5.6%)↑50.0%prior 22
FTYROW: From stop sign28 (4.8%)↑55.6%prior 18
Ran Stop Sign19 (3.2%)↑11.8%prior 17
Other (explain in narrative): Other16 (2.7%)↑77.8%prior 9
Driver Distraction: Other interior distraction14 (2.4%)↓-33.3%prior 21

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

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with 'Clear' weather and 'Dry' road surfaces accounting for the majority of incidents in both periods. However, there was a notable increase in crashes occurring during adverse conditions. Incidents in 'Freezing rain/drizzle' increased from 4 to 21, and crashes on 'Ice/frost' surfaces rose from 35 to 46.

Weather

Clear298 (60.7%)
↑1.0%prior 295
Cloudy100 (20.4%)
↑37.0%prior 73
Snow43 (8.8%)
↓-6.5%prior 46
Freezing rain/drizzle21 (4.3%)
Rain15 (3.1%)
↓-55.9%prior 34
Blowing Snow6 (1.2%)
↓-40.0%prior 10
Fog, smoke, smog4 (0.8%)
↓-33.3%prior 6
Severe Winds2 (0.4%)
Sleet, hail1 (0.2%)
Other (explain in narrative)1 (0.2%)

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

Lighting

Daylight301 (61.1%)
↑4.2%prior 289
Dark - roadway not lighted125 (25.4%)
↑9.6%prior 114
Dark - roadway lighted34 (6.9%)
↓-5.6%prior 36
Dawn19 (3.9%)
↑46.2%prior 13
Dusk12 (2.4%)
↓-29.4%prior 17
Dark - unknown roadway lighting2 (0.4%)

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

Road Surface

Dry326 (66.1%)
↑1.2%prior 322
Ice/frost46 (9.3%)
↑31.4%prior 35
Wet43 (8.7%)
↑2.4%prior 42
Snow40 (8.1%)
↓-18.4%prior 49
Gravel25 (5.1%)
↑25.0%prior 20
Slush8 (1.6%)
Mud, dirt5 (1.0%)

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

Vehicles & Demographics

Ford and Chevrolet remained the top two vehicle makes involved in crashes for both years, with Ford increasing from 141 to 151 incidents and Chevrolet (combining 'CHEV' and 'CHEVROLET') increasing from 154 to 173 incidents. The age distribution of persons involved in crashes showed a decrease in the 16-20 and 21-25 age groups, falling from 125 to 110 and 129 to 108, respectively. Conversely, the 45-54 and 55-64 age groups saw slight increases.

Top Vehicle Makes (857 vehicles)

1
FORD151 (17.6%)
↑7.1%prior 141
2
CHEVROLET97 (11.3%)
↑54.0%prior 63
3
CHEV76 (8.9%)
↓-16.5%prior 91
4
DODGE39 (4.6%)
↓-7.1%prior 42
5
DODG29 (3.4%)
↑7.4%prior 27
6
TOYT25 (2.9%)
↑38.9%prior 18
7
JEEP25 (2.9%)
↑47.1%prior 17
8
GMC24 (2.8%)
↑26.3%prior 19
9
FREIGHTLINER21 (2.5%)
↑40.0%prior 15
10
TOYOTA18 (2.1%)
↓-43.8%prior 32

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

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

Sex Distribution (642 persons with recorded sex)

Male428 (66.7%)
↑1.2%prior 423
Female214 (33.3%)
↓-26.2%prior 290

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: 586
  • Total persons involved: 971
  • Total vehicles involved: 857

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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