Yearly Traffic Safety Analysis

863 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Des Moines County, the total number of crashes remained nearly stable, with 863 crashes in 2017 compared to 864 in 2016, a decrease of less than 1%. Despite the consistent crash volume, there was a notable improvement in outcomes, as total fatalities dropped by 40% from 5 to 3, and total injuries decreased by 14.8% from 244 to 208 year-over-year.

863

▼ -0.1%was 864

Total Crash Events

3

▼ -40.0%was 5

Persons Killed

208

▼ -14.8%was 244

Persons Injured

3

▼ -40.0%was 5

Fatal Crash Events

Note: "Persons Killed" (3) 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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crash trends in Des Moines County were stable between 2016 and 2017, with total incidents decreasing by a single crash from 864 to 863. While the total number of crashes was virtually unchanged, the severity of these incidents lessened. Fatalities decreased from 5 to 3, and the number of people injured fell from 244 to 208.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 5↓-40.0%

0

Other Killed

Prior: 0→0.0%

5

Pedestrians Injured

Prior: 7↓-28.6%

3

Cyclists Injured

Prior: 3→0.0%

198

Motorists Injured

Prior: 233↓-15.0%

2

Other Injured

Prior: 1↑100.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes showed some shifts between the two periods. The peak day for crashes moved from Saturday (143 crashes) in 2016 to Friday (148 crashes) in 2017. The peak hour for collisions remained the 3 p.m. hour in both years, although the number of crashes during this peak time decreased from 90 in 2016 to 80 in 2017.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased from 2016 to 2017. The number of fatal crashes fell from 5 to 3, and the share of crashes resulting in no injuries increased from 76.6% to 78.0%. While the count of minor injury crashes decreased from 71 to 47, the number of serious injury crashes increased from 14 to 20.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.3%
↓-40.0%prior 5
Serious Injury20serious injury crashes2.3%
↑42.9%prior 14
Minor Injury47minor injury crashes5.4%
↓-33.8%prior 71
Possible Injury120possible injury crashes13.9%
↑7.1%prior 112
No Injury673no injury crashes78%
↑1.7%prior 662

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes remained consistent year-over-year. Collisions involving an animal were the top factor in both periods, with the count increasing from 128 in 2016 to 146 in 2017. Following too closely was the second-most cited factor in both years, though its count decreased from 67 to 61. A notable change was the increase in crashes related to failure to yield while making a left turn, which rose from 28 incidents in 2016 to 42 in 2017.

Officer-Reported Primary Contributing Cause

Animal146 (16.9%)↑14.1%prior 128
Followed too close61 (7.1%)↓-9.0%prior 67
Other (explain in narrative): Other55 (6.4%)↓-15.4%prior 65
Ran off road - left48 (5.6%)↓-17.2%prior 58
Lost Control44 (5.1%)↑2.3%prior 43
FTYROW: From stop sign43 (5%)↓-24.6%prior 57
FTYROW: Making left turn42 (4.9%)↑50.0%prior 28
Driving too fast for conditions39 (4.5%)↑34.5%prior 29
Driver Distraction: Other interior distraction30 (3.5%)↑30.4%prior 23
Ran Stop Sign26 (3%)↓-10.3%prior 29

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions were largely similar between 2016 and 2017. The majority of crashes in both periods occurred on dry roads (602 in 2016, 598 in 2017) and during daylight hours (529 in 2016, 501 in 2017). There was a slight increase in crashes during rainy conditions, from 33 to 44, and on snowy road surfaces, from 31 to 42. Conversely, crashes on wet roads decreased from 90 to 80.

Weather

Clear506 (66.9%)
↑6.3%prior 476
Cloudy167 (22.1%)
↓-23.0%prior 217
Rain44 (5.8%)
↑33.3%prior 33
Snow24 (3.2%)
↑9.1%prior 22
Fog, smoke, smog8 (1.1%)
Blowing Snow3 (0.4%)
Severe Winds2 (0.3%)
Other (explain in narrative)1 (0.1%)
Freezing rain/drizzle1 (0.1%)
↓-87.5%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight501 (66.0%)
↓-5.3%prior 529
Dark - roadway lighted136 (17.9%)
↑11.5%prior 122
Dark - roadway not lighted75 (9.9%)
↑1.4%prior 74
Dusk24 (3.2%)
↑71.4%prior 14
Dawn17 (2.2%)
↓-32.0%prior 25
Dark - unknown roadway lighting6 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry598 (79.4%)
↓-0.7%prior 602
Wet80 (10.6%)
↓-11.1%prior 90
Snow42 (5.6%)
↑35.5%prior 31
Gravel18 (2.4%)
→0.0%prior 18
Ice/frost11 (1.5%)
↓-35.3%prior 17
Slush3 (0.4%)
↓-50.0%prior 6
Other (explain in narrative)1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes were consistent, with Ford, Chevrolet, and Dodge being the most common in both 2016 and 2017. Regarding driver demographics, there were shifts among age groups involved in crashes. The number of persons in the 55-64 age group increased from 153 to 204, while the 45-54 age group saw a decrease from 238 to 182. The 16-20 age group also saw an increase in involvement, from 194 individuals in 2016 to 217 in 2017.

Top Vehicle Makes (1,443 vehicles)

1
FORD241 (16.7%)
↑3.9%prior 232
2
CHEV197 (13.7%)
↑69.8%prior 116
3
CHEVROLET107 (7.4%)
↓-40.2%prior 179
4
DODG95 (6.6%)
↑46.2%prior 65
5
JEEP50 (3.5%)
↑22.0%prior 41
6
KIA50 (3.5%)
↓-18.0%prior 61
7
TOYT48 (3.3%)
↓-5.9%prior 51
8
CHRY45 (3.1%)
↑25.0%prior 36
9
GMC43 (3%)
↓-15.7%prior 51
10
PONT40 (2.8%)
→0.0%prior 40

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (1,074 persons with recorded sex)

Male563 (52.4%)
↓-8.6%prior 616
Female511 (47.6%)
↑10.4%prior 463

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 863
  • Total persons involved: 1,696
  • Total vehicles involved: 1,443

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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