Yearly Traffic Safety Analysis

864 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Des Moines County, total crashes decreased from 920 in 2015 to 864 in 2016, a 6.1% reduction. Despite the overall drop in collisions and a 12.2% decrease in total injuries from 278 to 244, the number of fatalities recorded rose from 4 to 5 year-over-year.

864

▼ -6.1%was 920

Total Crash Events

5

▲ 25.0%was 4

Persons Killed

244

▼ -12.2%was 278

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 Des Moines County showed a downward trend between 2015 and 2016. The total number of crashes fell by 6.1%, from 920 to 864. This trend was mirrored by a 12.2% decrease in total injuries, which dropped from 278 to 244.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 3↑66.7%

0

Other Killed

Prior: 0→0.0%

7

Pedestrians Injured

Prior: 10↓-30.0%

3

Cyclists Injured

Prior: 6↓-50.0%

233

Motorists Injured

Prior: 260↓-10.4%

1

Other Injured

Prior: 2↓-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 saw some shifts between 2015 and 2016. The peak day for crashes moved from Friday (156 crashes) in 2015 to Saturday (143 crashes) in 2016. The peak hour for collisions remained consistent at 3 PM in both periods, with 88 crashes in 2015 and 90 in 2016.

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, the severity of incidents shifted year-over-year. The number of fatal crashes increased from 4 in 2015 to 5 in 2016, raising the fatal crash rate from 0.43% to 0.58% of all crashes. The proportion of crashes resulting in minor injuries grew from 7.1% to 8.2%, while crashes involving possible injuries decreased as a share of the total from 15.7% to 13.0%.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.6%
↑25.0%prior 4
Serious Injury14serious injury crashes1.6%
↓-12.5%prior 16
Minor Injury71minor injury crashes8.2%
↑9.2%prior 65
Possible Injury112possible injury crashes13%
↓-22.2%prior 144
No Injury662no injury crashes76.6%
↓-4.2%prior 691

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

The leading contributing factors for crashes showed notable changes between 2015 and 2016. Collisions involving an 'Animal' remained the top factor in both years, and the count of such crashes increased by 25.5%, from 102 in 2015 to 128 in 2016. 'Followed too close' became the second-most cited factor in 2016, with its count rising from 62 to 67. Conversely, crashes attributed to 'FTYROW: From stop sign' decreased in count from 63 to 57.

Officer-Reported Primary Contributing Cause

Animal128 (14.8%)↑25.5%prior 102
Followed too close67 (7.8%)↑8.1%prior 62
Other (explain in narrative): Other65 (7.5%)↓-22.6%prior 84
Ran off road - left58 (6.7%)↑3.6%prior 56
FTYROW: From stop sign57 (6.6%)↓-9.5%prior 63
Lost Control43 (5%)↑13.2%prior 38
Ran Traffic Signal33 (3.8%)↑22.2%prior 27
Driving too fast for conditions29 (3.4%)↓-17.1%prior 35
Ran Stop Sign29 (3.4%)↓-12.1%prior 33
FTYROW: Making left turn28 (3.2%)↓-39.1%prior 46

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 in clear weather and daylight conditions remained the most common scenario in both periods, with their proportions of total crashes staying relatively stable. There was a notable decrease in crashes occurring on adverse road surfaces; the number of collisions on snowy, icy, or slushy roads fell from 105 in 2015 to 54 in 2016. Similarly, crashes during rainy conditions decreased from 57 to 33 year-over-year.

Weather

Clear476 (62.2%)
↓-5.6%prior 504
Cloudy217 (28.4%)
↓-3.6%prior 225
Rain33 (4.3%)
↓-42.1%prior 57
Snow22 (2.9%)
↓-24.1%prior 29
Freezing rain/drizzle8 (1.0%)
↓-33.3%prior 12
Fog, smoke, smog4 (0.5%)
↓-20.0%prior 5
Other (explain in narrative)2 (0.3%)
Blowing Snow2 (0.3%)
Severe Winds1 (0.1%)

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

Lighting

Daylight529 (68.9%)
↓-9.9%prior 587
Dark - roadway lighted122 (15.9%)
↓-4.7%prior 128
Dark - roadway not lighted74 (9.6%)
↓-15.9%prior 88
Dawn25 (3.3%)
↑108.3%prior 12
Dusk14 (1.8%)
↓-26.3%prior 19
Dark - unknown roadway lighting4 (0.5%)
↓-60.0%prior 10

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

Road Surface

Dry602 (78.5%)
↓-1.8%prior 613
Wet90 (11.7%)
↓-12.6%prior 103
Snow31 (4.0%)
↓-43.6%prior 55
Gravel18 (2.3%)
↓-5.3%prior 19
Ice/frost17 (2.2%)
↓-55.3%prior 38
Slush6 (0.8%)
↓-50.0%prior 12
Water (standing or moving)3 (0.4%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Chevrolet, Ford, and Dodge being the most frequent in both 2015 and 2016, though the number of crashes for each make decreased. An analysis of persons involved in crashes shows a notable year-over-year change in age distribution. The number of individuals in the 16-20 age group involved in crashes fell from 255 to 194, and the 26-34 age group saw a similar decline from 304 to 224.

Top Vehicle Makes (1,494 vehicles)

1
FORD232 (15.5%)
↓-18.0%prior 283
2
CHEVROLET179 (12%)
↑26.1%prior 142
3
CHEV116 (7.8%)
↓-32.9%prior 173
4
DODG65 (4.4%)
↓-38.1%prior 105
5
NR61 (4.1%)
→0.0%prior 61
6
KIA61 (4.1%)
↑10.9%prior 55
7
TOYOTA54 (3.6%)
↑50.0%prior 36
8
DODGE53 (3.5%)
↑17.8%prior 45
9
GMC51 (3.4%)
↑18.6%prior 43
10
TOYT51 (3.4%)
↑21.4%prior 42

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

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

Sex Distribution (1,079 persons with recorded sex)

Male616 (57.1%)
↓-15.7%prior 731
Female463 (42.9%)
↓-22.1%prior 594

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: 864
  • Total persons involved: 1,711
  • Total vehicles involved: 1,494

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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