Yearly Traffic Safety Analysis

91 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Davis County, total traffic crashes decreased by 22.2% from 117 in 2018 to 91 in 2019. This overall reduction was accompanied by a drop in both injuries, from 46 to 30, and fatalities, which fell from 3 in the prior year to 1 in the current year. The most common contributing factor in both periods remained collisions with animals.

91

▼ -22.2%was 117

Total Crash Events

1

▼ -66.7%was 3

Persons Killed

30

▼ -34.8%was 46

Persons Injured

1

▼ -66.7%was 3

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 safety metrics in Davis County showed a general improvement year-over-year. The total number of crashes fell from 117 in 2018 to 91 in 2019, a 22.2% decrease. Correspondingly, the number of persons injured in these incidents declined from 46 to 30, and fatalities were reduced from 3 to 1.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 2↓-50.0%

0

Other Killed

Prior: 0→0.0%

28

Motorists Injured

Prior: 44↓-36.4%

2

Other Injured

Prior: 1↑100.0%

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 showed some shifts between the two years. The peak day for crashes moved from Tuesday in 2018, with 20 incidents, to Friday in 2019, with 17 incidents. While the 5 PM hour was a peak time in both years, its crash count dropped from 14 to 11. In 2019, the 2 PM hour also emerged as a joint peak time, accounting for 11 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

Crash severity decreased from 2018 to 2019. The number of fatal crashes fell from 3 to 1, and the fatal crash rate declined from 2.6% to 1.1% of all crashes. The total count of crashes resulting in any level of injury (serious, minor, or possible) dropped from 31 in 2018 to 17 in 2019. Consequently, the share of no-injury crashes increased from 70.9% of all incidents in 2018 to 80.2% in 2019.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.1%
↓-66.7%prior 3
Serious Injury2serious injury crashes2.2%
→0.0%prior 2
Minor Injury5minor injury crashes5.5%
↓-58.3%prior 12
Possible Injury10possible injury crashes11%
↓-41.2%prior 17
No Injury73no injury crashes80.2%
↓-12.0%prior 83

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 were the leading contributing factor in both 2018 and 2019, though the count of such incidents decreased from 44 to 35. The count of crashes attributed to 'Lost Control' also fell from 11 to 7. Notably, crashes due to 'Ran off road - straight' increased from 1 in 2018 to 6 in 2019, while incidents of 'Followed too close' saw a significant drop from 9 to 3.

Officer-Reported Primary Contributing Cause

Animal35 (38.5%)↓-20.5%prior 44
Lost Control7 (7.7%)↓-36.4%prior 11
Ran off road - straight6 (6.6%)
FTYROW: From stop sign5 (5.5%)↓-44.4%prior 9
Driver Distraction: Other interior distraction4 (4.4%)
Ran Traffic Signal3 (3.3%)
Driving too fast for conditions3 (3.3%)
Followed too close3 (3.3%)↓-66.7%prior 9
Made improper turn3 (3.3%)
Driver Distraction: Exterior distraction2 (2.2%)

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 lighting and weather conditions remained relatively stable year-over-year. The proportion of crashes in daylight was 41.9% in 2018 and 42.9% in 2019. However, there was a shift in road surface conditions. The share of crashes on dry surfaces decreased from 74.0% of reported conditions in 2018 to 59.4% in 2019, while the share on ice or frost increased from 5.2% to 17.2%.

Weather

Clear43 (68.3%)
↓-23.2%prior 56
Cloudy9 (14.3%)
↓-25.0%prior 12
Rain4 (6.3%)
Snow3 (4.8%)
Freezing rain/drizzle2 (3.2%)
Fog, smoke, smog1 (1.6%)
Severe Winds1 (1.6%)

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

Lighting

Daylight39 (60.9%)
↓-20.4%prior 49
Dark - roadway not lighted16 (25.0%)
↓-27.3%prior 22
Dawn4 (6.3%)
Dark - roadway lighted2 (3.1%)
Dusk2 (3.1%)
Dark - unknown roadway lighting1 (1.6%)

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

Road Surface

Dry38 (59.4%)
↓-33.3%prior 57
Ice/frost11 (17.2%)
Wet6 (9.4%)
↓-14.3%prior 7
Snow5 (7.8%)
↓-16.7%prior 6
Gravel4 (6.3%)

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

Vehicles & Demographics

The top three vehicle makes involved in crashes—Chevrolet, Ford, and Dodge—were the same in both 2018 and 2019, with counts for each decreasing in line with the overall trend. Analysis of persons involved in crashes shows a demographic shift in age representation. The most represented age group moved from the 26-34 bracket in 2018 (38 people) to the 65+ bracket in 2019 (38 people), as the count for the latter group increased from 21 in the prior year.

Top Vehicle Makes (125 vehicles)

1
FORD25 (20%)
↓-34.2%prior 38
2
CHEV20 (16%)
↓-16.7%prior 24
3
CHEVROLET14 (11.2%)
↓-12.5%prior 16
4
DODG10 (8%)
↑25.0%prior 8
5
DODGE5 (4%)
↓-44.4%prior 9
6
NISS5 (4%)
7
PONT4 (3.2%)
↓-33.3%prior 6
8
CHRY3 (2.4%)
9
TOYT2 (1.6%)
10
BUIC2 (1.6%)
↓-60.0%prior 5

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

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

Sex Distribution (120 persons with recorded sex)

Male76 (63.3%)
↑13.4%prior 67
Female44 (36.7%)
↓-21.4%prior 56

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: 91
  • Total persons involved: 187
  • Total vehicles involved: 125

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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