Yearly Traffic Safety Analysis

617 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Marion County recorded 617 total crashes, an increase of 10.2% from the 560 crashes reported in 2018. Despite the rise in total incidents, the number of fatalities decreased significantly from 5 in 2018 to 2 in 2019. The most notable year-over-year shift was this reduction in fatalities, even as overall crash volume grew.

617

▲ 10.2%was 560

Total Crash Events

2

▼ -60.0%was 5

Persons Killed

171

▼ -4.5%was 179

Persons Injured

2

▼ -60.0%was 5

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

Overall crash volume in Marion County increased by 10.2% from 560 incidents in 2018 to 617 in 2019. However, this rise in total crashes was accompanied by a decrease in the most severe outcomes, with total injuries falling by 4.5% from 179 to 171 and fatalities dropping from 5 to 2.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 5↓-60.0%

5

Pedestrians Injured

Prior: 1↑400.0%

1

Cyclists Injured

Prior: 6↓-83.3%

165

Motorists Injured

Prior: 172↓-4.1%

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

Temporal crash patterns shifted slightly year-over-year. Friday remained the day with the most crashes in both 2018 (95 incidents) and 2019 (128 incidents). However, the peak hour for crashes moved from the 3 p.m. hour in 2018, which saw 45 incidents, to the 5 p.m. hour in 2019, with 56 incidents.

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 overall severity of crashes decreased from 2018 to 2019. The number of fatal crashes fell from 5 to 2, and the share of crashes resulting in a serious injury also declined from 3.9% to 3.1%. Correspondingly, the proportion of crashes with no reported injuries increased from 72.9% of all incidents in 2018 to 76.8% in 2019.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.3%
↓-60.0%prior 5
Serious Injury19serious injury crashes3.1%
↓-13.6%prior 22
Minor Injury49minor injury crashes7.9%
↓-24.6%prior 65
Possible Injury73possible injury crashes11.8%
↑21.7%prior 60
No Injury474no injury crashes76.8%
↑16.2%prior 408

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 an animal remained the leading contributing factor for crashes in both periods, with the count increasing from 170 in 2018 to 196 in 2019. The top five factors were largely consistent, with 'Lost Control' also increasing in count from 42 to 47 incidents. Notably, crashes attributed to 'Followed too close' decreased from 40 to 36, while incidents involving 'Failure to yield right of way from a stop sign' increased from 28 to 37.

Officer-Reported Primary Contributing Cause

Animal196 (31.8%)↑15.3%prior 170
Lost Control47 (7.6%)↑11.9%prior 42
Other (explain in narrative): Other39 (6.3%)↑14.7%prior 34
FTYROW: From stop sign37 (6%)↑32.1%prior 28
Followed too close36 (5.8%)↓-10.0%prior 40
Driving too fast for conditions25 (4.1%)↑4.2%prior 24
Ran off road - straight24 (3.9%)↑50.0%prior 16
Driver Distraction: Other interior distraction22 (3.6%)↓-15.4%prior 26
Ran off road - left19 (3.1%)↑72.7%prior 11
Operating vehicle in an reckless, erratic, careless, negligent manner15 (2.4%)↑50.0%prior 10

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 distribution of crashes across different conditions showed some changes year-over-year. While clear weather and dry road surfaces still accounted for the majority of incidents, their share of total crashes decreased slightly. In 2019, 52.7% of crashes occurred on dry roads, down from 56.6% in 2018. Crashes on adverse surfaces like ice, wet, or snow increased in count from 86 to 105. Lighting conditions remained relatively stable, with crashes in daylight accounting for just over half of all incidents in both years.

Weather

Clear286 (62.9%)
↓-3.4%prior 296
Cloudy110 (24.2%)
↑23.6%prior 89
Rain16 (3.5%)
↑6.7%prior 15
Freezing rain/drizzle15 (3.3%)
↑114.3%prior 7
Snow14 (3.1%)
↑27.3%prior 11
Blowing Snow7 (1.5%)
↑16.7%prior 6
Fog, smoke, smog4 (0.9%)
Severe Winds2 (0.4%)
Sleet, hail1 (0.2%)

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

Lighting

Daylight311 (67.6%)
↑6.9%prior 291
Dark - roadway not lighted85 (18.5%)
→0.0%prior 85
Dark - roadway lighted34 (7.4%)
↑9.7%prior 31
Dusk16 (3.5%)
↑6.7%prior 15
Dawn14 (3.0%)
↑133.3%prior 6

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

Road Surface

Dry325 (71.1%)
↑2.5%prior 317
Ice/frost35 (7.7%)
↑150.0%prior 14
Wet34 (7.4%)
↓-24.4%prior 45
Snow33 (7.2%)
↑43.5%prior 23
Gravel26 (5.7%)
↑8.3%prior 24
Slush2 (0.4%)
Mud, dirt1 (0.2%)
Other (explain in narrative)1 (0.2%)

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

Vehicles & Demographics

The vehicle makes most frequently involved in crashes remained consistent, with Ford and Chevrolet vehicles leading in both 2018 and 2019. The count of Chevrolet vehicles involved in crashes rose from 178 to 189, and Ford vehicles increased from 168 to 186. Analysis of persons involved in crashes shows a shift in age demographics; the proportion of individuals aged 35-44 grew from 13.1% of all persons in 2018 to 16.5% in 2019.

Top Vehicle Makes (918 vehicles)

1
FORD186 (20.3%)
↑10.7%prior 168
2
CHEV126 (13.7%)
↑0.8%prior 125
3
DODG65 (7.1%)
↑38.3%prior 47
4
CHEVROLET63 (6.9%)
↑18.9%prior 53
5
JEEP32 (3.5%)
→0.0%prior 32
6
TOYT32 (3.5%)
↑10.3%prior 29
7
GMC30 (3.3%)
↑50.0%prior 20
8
BUIC24 (2.6%)
↑4.3%prior 23
9
CHRY23 (2.5%)
↑9.5%prior 21
10
DODGE22 (2.4%)
↑83.3%prior 12

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

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

Sex Distribution (865 persons with recorded sex)

Male497 (57.5%)
↑48.4%prior 335
Female368 (42.5%)
↑35.3%prior 272

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: 617
  • Total persons involved: 1,287
  • Total vehicles involved: 918

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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