Yearly Traffic Safety Analysis

229 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Jefferson County recorded 229 total crashes, a 7.3% decrease from the 247 crashes reported in 2018. The most significant year-over-year change was the reduction in traffic fatalities, which fell from 4 in 2018 to 0 in 2019. Total injuries also saw a decrease from 74 to 64.

229

▼ -7.3%was 247

Total Crash Events

0

▼ -100.0%was 4

Persons Killed

64

▼ -13.5%was 74

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 safety metrics in Jefferson County improved from 2018 to 2019. The total number of crashes decreased by 7.3%, from 247 to 229. This decline was accompanied by a 13.5% reduction in total injuries, from 74 to 64, and a complete elimination of fatalities, which dropped from 4 to 0.

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%

1

Pedestrians Injured

Prior: 0→%

3

Cyclists Injured

Prior: 1↑200.0%

60

Motorists Injured

Prior: 72↓-16.7%

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 2018 and 2019. While Thursday remained the peak day for crashes in both years (44 in 2018 and 43 in 2019), the peak hour for incidents moved earlier. In 2019, the most crashes occurred during the 3 p.m. hour with 21 incidents, a shift from the 5 p.m. peak hour observed in 2018 which had 23 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 improved significantly from 2018 to 2019, with fatal crashes dropping from 4 to 0. While the number of minor injury crashes decreased from 25 to 20, the count of serious injury crashes increased from 6 to 8. Crashes resulting in possible injuries also rose from 24 to 30, representing 13.1% of all incidents in 2019 compared to 9.7% in 2018.

Outcome by Severity (Crash Events)

Serious Injury8serious injury crashes3.5%
↑33.3%prior 6
Minor Injury20minor injury crashes8.7%
↓-20.0%prior 25
Possible Injury30possible injury crashes13.1%
↑25.0%prior 24
No Injury171no injury crashes74.7%
↓-9.0%prior 188

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 in both periods, though the count decreased from 88 in 2018 to 72 in 2019. Crashes attributed to 'Lost Control' were halved, dropping from 32 to 16. Conversely, incidents involving 'Failure to Yield Right of Way from a stop sign' increased by 71.4%, rising from 14 crashes in 2018 to 24 in 2019 and becoming the second most common factor for the year.

Officer-Reported Primary Contributing Cause

Animal72 (31.4%)↓-18.2%prior 88
FTYROW: From stop sign24 (10.5%)↑71.4%prior 14
Lost Control16 (7%)↓-50.0%prior 32
Other (explain in narrative): Other14 (6.1%)↑27.3%prior 11
Driving too fast for conditions13 (5.7%)↓-7.1%prior 14
Followed too close10 (4.4%)→0.0%prior 10
Ran off road - straight9 (3.9%)↓-35.7%prior 14
Ran Stop Sign9 (3.9%)↑50.0%prior 6
Ran off road - left7 (3.1%)→0.0%prior 7
Operating vehicle in an reckless, erratic, careless, negligent manner6 (2.6%)

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 majority of crashes in both 2018 and 2019 occurred in clear weather on dry roads. There was a notable decrease in crashes happening in dark, unlighted conditions, which fell from 38 incidents in 2018 to 17 in 2019. Crashes on wet road surfaces also declined from 22 to 13, while incidents on icy or frosty roads increased from 8 in 2018 to 13 in 2019.

Weather

Clear111 (69.4%)
↓-7.5%prior 120
Cloudy35 (21.9%)
↑40.0%prior 25
Snow6 (3.8%)
↓-14.3%prior 7
Rain5 (3.1%)
↓-37.5%prior 8
Freezing rain/drizzle2 (1.3%)
↓-75.0%prior 8
Blowing Snow1 (0.6%)

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

Lighting

Daylight116 (73.0%)
↑3.6%prior 112
Dark - roadway not lighted17 (10.7%)
↓-55.3%prior 38
Dark - roadway lighted15 (9.4%)
↑25.0%prior 12
Dusk4 (2.5%)
↓-33.3%prior 6
Dark - unknown roadway lighting4 (2.5%)
Dawn3 (1.9%)

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

Road Surface

Dry112 (70.4%)
↓-5.1%prior 118
Wet13 (8.2%)
↓-40.9%prior 22
Ice/frost13 (8.2%)
↑62.5%prior 8
Snow7 (4.4%)
↓-36.4%prior 11
Gravel7 (4.4%)
↓-12.5%prior 8
Slush4 (2.5%)
Mud, dirt2 (1.3%)
Sand1 (0.6%)

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

Vehicles & Demographics

Ford and Chevrolet remained the most common vehicle makes involved in crashes in both years, although both saw a decrease in total vehicle counts from 2018 to 2019. Analysis of persons involved shows a significant demographic shift, with the 65+ age group's involvement rising from 61 individuals in 2018 to 89 in 2019. This made the 65+ demographic the most frequently involved age group in 2019, surpassing the 26-34 age group which was highest in the prior year.

Top Vehicle Makes (350 vehicles)

1
FORD46 (13.1%)
↓-13.2%prior 53
2
CHEV46 (13.1%)
↑12.2%prior 41
3
TOYT33 (9.4%)
↑22.2%prior 27
4
CHEVROLET17 (4.9%)
↓-39.3%prior 28
5
GMC17 (4.9%)
↑41.7%prior 12
6
DODG14 (4%)
↓-36.4%prior 22
7
JEEP13 (3.7%)
↑85.7%prior 7
8
HOND12 (3.4%)
↑140.0%prior 5
9
TOYOTA11 (3.1%)
↑22.2%prior 9
10
DODGE11 (3.1%)
↑22.2%prior 9

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

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

Sex Distribution (318 persons with recorded sex)

Male171 (53.8%)
↑22.1%prior 140
Female147 (46.2%)
↑20.5%prior 122

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: 229
  • Total persons involved: 518
  • Total vehicles involved: 350

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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