Yearly Traffic Safety Analysis

230 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Delaware County recorded 230 total crashes, a 15.1% decrease from the 271 crashes reported in 2018. The most significant year-over-year change was the reduction in fatalities, which dropped from four in 2018 to zero in 2019.

230

▼ -15.1%was 271

Total Crash Events

0

▼ -100.0%was 4

Persons Killed

69

Persons Injured

0

▼ -100.0%was 4

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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, traffic crashes in Delaware County showed a downward trend from 2018 to 2019, with total incidents decreasing by 15.1% from 271 to 230. While the total number of people injured remained unchanged at 69, there was a notable improvement in crash outcomes, as the number of fatalities fell from four to zero.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 4↓-100.0%

2

Pedestrians Injured

Prior: 1↑100.0%

2

Cyclists Injured

Prior: 2→0.0%

65

Motorists Injured

Prior: 66↓-1.5%

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 timing of crashes showed some shifts between the two periods. The peak day for collisions moved from Monday (55 crashes) in 2018 to Friday (43 crashes) in 2019. However, the peak hour for crashes remained consistent at 6 p.m. in both years, with the incident count for that hour decreasing from 24 in 2018 to 21 in 2019.

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 improved significantly from 2018 to 2019, with fatal crashes dropping from four to zero. The number of crashes involving serious injuries increased slightly from five to seven, while those with minor injuries decreased from 28 to 21. Despite a drop in the total number of crashes, the proportion of crashes resulting in some form of injury remained stable, accounting for 21.0% of crashes in 2018 and 21.7% in 2019.

Outcome by Severity (Crash Events)

Serious Injury7serious injury crashes3%
↑40.0%prior 5
Minor Injury21minor injury crashes9.1%
↓-25.0%prior 28
Possible Injury22possible injury crashes9.6%
↓-8.3%prior 24
No Injury180no injury crashes78.3%
↓-14.3%prior 210

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 with animals remained the top contributing factor in both periods, though the count of such incidents decreased from 89 in 2018 to 77 in 2019. 'Lost Control' moved up to become the second-ranked factor in 2019, with its count increasing from 24 to 27 crashes. Notably, incidents where a driver 'Followed too close' increased from 13 to 21, while crashes from 'Failure to yield from a stop sign' were more than halved, dropping from a count of 15 to 7.

Officer-Reported Primary Contributing Cause

Animal77 (33.5%)↓-13.5%prior 89
Lost Control27 (11.7%)↑12.5%prior 24
Followed too close21 (9.1%)↑61.5%prior 13
Ran off road - straight13 (5.7%)↓-31.6%prior 19
Driving too fast for conditions12 (5.2%)↓-7.7%prior 13
Ran off road - left7 (3%)→0.0%prior 7
FTYROW: From stop sign7 (3%)↓-53.3%prior 15
Made improper turn5 (2.2%)↓-37.5%prior 8
Driver Distraction: Other interior distraction5 (2.2%)
Ran Stop Sign5 (2.2%)↓-16.7%prior 6

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 environmental conditions remained largely stable year-over-year. In both 2019 and 2018, roughly half of all crashes occurred in clear weather on dry roads. Crashes in dark, unlighted conditions accounted for approximately 18.5% of the total in both periods. Similarly, the share of crashes on roads affected by snow, ice, or slush was consistent, making up 13.5% of crashes in 2019 compared to 12.9% in 2018.

Weather

Clear113 (65.7%)
↓-19.3%prior 140
Cloudy31 (18.0%)
↓-11.4%prior 35
Snow8 (4.7%)
↓-27.3%prior 11
Blowing Snow5 (2.9%)
Rain5 (2.9%)
↓-44.4%prior 9
Fog, smoke, smog3 (1.7%)
Freezing rain/drizzle3 (1.7%)
Severe Winds2 (1.2%)
Sleet, hail2 (1.2%)

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

Lighting

Daylight111 (64.5%)
↓-9.8%prior 123
Dark - roadway not lighted43 (25.0%)
↓-14.0%prior 50
Dark - roadway lighted9 (5.2%)
↓-10.0%prior 10
Dusk5 (2.9%)
↓-50.0%prior 10
Dawn4 (2.3%)
↓-33.3%prior 6

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

Road Surface

Dry119 (69.2%)
↓-13.8%prior 138
Wet18 (10.5%)
↑20.0%prior 15
Ice/frost14 (8.1%)
↑55.6%prior 9
Snow13 (7.6%)
↓-27.8%prior 18
Slush4 (2.3%)
↓-50.0%prior 8
Gravel4 (2.3%)
↓-63.6%prior 11

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Chevrolet and Ford being the most common in both 2019 and 2018, followed by Dodge. The number of vehicles from these top makes decreased, in line with the overall reduction in total vehicles involved from 381 to 322. An analysis of the age of persons involved in crashes reveals a significant demographic shift: the number of individuals aged 16-20 dropped from 73 to 45, while those in the 26-34 age group increased from 57 to 83.

Top Vehicle Makes (322 vehicles)

1
CHEV56 (17.4%)
↓-29.1%prior 79
2
FORD54 (16.8%)
↓-27.0%prior 74
3
DODG23 (7.1%)
↓-11.5%prior 26
4
CHEVROLET16 (5%)
↓-5.9%prior 17
5
HOND14 (4.3%)
6
GMC10 (3.1%)
↓-37.5%prior 16
7
KIA10 (3.1%)
↑100.0%prior 5
8
CHRY10 (3.1%)
↓-23.1%prior 13
9
JEEP9 (2.8%)
↓-35.7%prior 14
10
TOYT9 (2.8%)
→0.0%prior 9

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

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

Sex Distribution (303 persons with recorded sex)

Male170 (56.1%)
↓-6.6%prior 182
Female133 (43.9%)
↑3.9%prior 128

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: 230
  • Total persons involved: 461
  • Total vehicles involved: 322

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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