Yearly Traffic Safety Analysis

873 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Des Moines County, total crashes decreased from 894 in 2018 to 873 in 2019, a 2.4% reduction. While overall collisions fell, one of the most notable shifts was a 53.8% drop in crashes involving driving under the influence (DUI), which decreased from 39 to 18 incidents year-over-year.

873

▼ -2.3%was 894

Total Crash Events

3

▲ 50.0%was 2

Persons Killed

184

▼ -3.7%was 191

Persons Injured

3

▲ 50.0%was 2

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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Des Moines County showed a slight downward trend from 2018 to 2019, with total incidents decreasing by 2.4% from 894 to 873. The number of people injured also fell by 3.7%, from 191 to 184. In contrast, the number of fatalities recorded increased from 2 in 2018 to 3 in 2019.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 1↑200.0%

3

Pedestrians Injured

Prior: 5↓-40.0%

1

Cyclists Injured

Prior: 4↓-75.0%

180

Motorists Injured

Prior: 182↓-1.1%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes in Des Moines County shifted between the two periods. The peak day for crashes moved from Friday (152 incidents) in 2018 to Monday (140 incidents) in 2019. Similarly, the single busiest hour for collisions shifted from 3 p.m. in 2018 (86 crashes) to 4 p.m. in 2019 (70 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes saw mixed changes year-over-year. Fatal crashes increased from 2 in 2018 to 3 in 2019, raising the fatal crash rate from 0.22% to 0.34%. The count of serious injury crashes also rose from 11 to 13. Conversely, crashes resulting in minor or possible injuries saw a decrease in total count from 161 to 149.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.3%
↑50.0%prior 2
Serious Injury13serious injury crashes1.5%
↑18.2%prior 11
Minor Injury53minor injury crashes6.1%
↓-7.0%prior 57
Possible Injury96possible injury crashes11%
↓-7.7%prior 104
No Injury708no injury crashes81.1%
↓-1.7%prior 720

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the top contributing factor in both periods, with the count increasing from 117 crashes in 2018 to 159 in 2019. While incidents attributed to 'Followed too close' decreased from 83 to 58, crashes involving 'Driver Distraction: Other interior distraction' more than doubled, rising from 23 incidents to 53. Notably, crashes related to driving under the influence (DUI) were not a top-ranked factor but saw a significant count decrease from 39 to 18.

Officer-Reported Primary Contributing Cause

Animal159 (18.2%)↑35.9%prior 117
Other (explain in narrative): Other67 (7.7%)↑3.1%prior 65
Followed too close58 (6.6%)↓-30.1%prior 83
Driver Distraction: Other interior distraction53 (6.1%)↑130.4%prior 23
Driving too fast for conditions48 (5.5%)↓-7.7%prior 52
Ran off road - left48 (5.5%)↓-22.6%prior 62
FTYROW: From stop sign46 (5.3%)↓-9.8%prior 51
FTYROW: Making left turn34 (3.9%)↓-26.1%prior 46
Lost Control32 (3.7%)↓-17.9%prior 39
Ran Traffic Signal25 (2.9%)↑13.6%prior 22

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The conditions under which crashes occurred remained broadly similar, with most incidents in both years happening in clear weather on dry roads. However, the proportion of crashes in these ideal conditions decreased slightly in 2019, with daylight crashes accounting for 58.8% of the total, down from 64.0% in 2018. There was a corresponding slight proportional increase in crashes occurring during snowy weather and on dark, lighted roadways.

Weather

Clear457 (62.5%)
↓-4.8%prior 480
Cloudy166 (22.7%)
↓-19.8%prior 207
Snow43 (5.9%)
↑43.3%prior 30
Rain34 (4.7%)
↓-22.7%prior 44
Freezing rain/drizzle16 (2.2%)
↑23.1%prior 13
Blowing Snow6 (0.8%)
Fog, smoke, smog5 (0.7%)
↓-50.0%prior 10
Severe Winds2 (0.3%)
Other (explain in narrative)1 (0.1%)
Blowing sand, soil, dirt1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight513 (69.8%)
↓-10.3%prior 572
Dark - roadway lighted132 (18.0%)
↑9.1%prior 121
Dark - roadway not lighted56 (7.6%)
↓-12.5%prior 64
Dusk20 (2.7%)
↓-16.7%prior 24
Dawn11 (1.5%)
↑57.1%prior 7
Dark - unknown roadway lighting3 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry505 (69.1%)
↓-12.0%prior 574
Wet105 (14.4%)
↑1.0%prior 104
Snow65 (8.9%)
↑3.2%prior 63
Ice/frost27 (3.7%)
↓-12.9%prior 31
Slush13 (1.8%)
↑44.4%prior 9
Gravel13 (1.8%)
↑116.7%prior 6
Mud, dirt2 (0.3%)
Sand1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

An analysis of persons involved in crashes shows a demographic shift, with an increased count of individuals from the 16-20 and 26-34 age groups, while the number of persons aged 65 and older decreased from 242 to 234. Regarding vehicle makes, Chevrolet became the most frequently involved manufacturer in 2019 with 302 vehicles, surpassing Ford, which saw its involvement decrease from 277 vehicles in 2018 to 225 in 2019. The number of Dodge vehicles involved in crashes also declined from 132 to 109.

Top Vehicle Makes (1,459 vehicles)

1
FORD225 (15.4%)
↓-18.8%prior 277
2
CHEV206 (14.1%)
↑13.8%prior 181
3
CHEVROLET96 (6.6%)
↑12.9%prior 85
4
DODG78 (5.3%)
↓-17.0%prior 94
5
TOYO63 (4.3%)
↑31.3%prior 48
6
KIA62 (4.2%)
↓-6.1%prior 66
7
GMC59 (4%)
↓-14.5%prior 69
8
JEEP56 (3.8%)
↑33.3%prior 42
9
NR47 (3.2%)
↓-33.8%prior 71
10
PONT37 (2.5%)
↑5.7%prior 35

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (1,268 persons with recorded sex)

Male670 (52.8%)
↑11.1%prior 603
Female598 (47.2%)
↑11.4%prior 537

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 873
  • Total persons involved: 1,933
  • Total vehicles involved: 1,459

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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