Yearly Traffic Safety Analysis

140 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Greene County recorded 140 total crashes, a 10.3% decrease from the 156 crashes reported in 2018. During the same period, fatalities fell from two to one. The most notable year-over-year shift was the decrease in crashes attributed to driving too fast for conditions, which fell from 12 incidents in 2018 to seven in 2019.

140

▼ -10.3%was 156

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

49

▲ 2.1%was 48

Persons Injured

1

▼ -50.0%was 2

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

Overall, traffic crashes in Greene County showed a downward trend from 2018 to 2019. The total number of crashes decreased by 10.3%, from 156 to 140. This decline was accompanied by a reduction in fatalities from two to one, though the total number of injuries remained stable, increasing by one from 48 to 49.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 2↓-50.0%

49

Motorists Injured

Prior: 46↑6.5%

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 temporal patterns of crashes shifted between the two years. In 2019, the peak day for crashes was Thursday with 27 incidents, a change from 2018 when Friday was the peak day with 37 incidents. The peak hour also shifted from 4 p.m. in 2018 (16 crashes) to a tie between the 3 p.m. and 5 p.m. hours in 2019, each with 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

The severity of crashes decreased from 2018 to 2019. The number of fatal crashes was halved, from two in 2018 to one in 2019, and the proportion of fatal crashes fell from 1.3% to 0.7% of all crashes. Similarly, serious injury crashes declined from seven (4.5% of total) to five (3.6% of total). The proportion of crashes resulting in no injuries remained largely consistent, accounting for 74.4% of crashes in 2018 and 75.7% in 2019.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
↓-50.0%prior 2
Serious Injury5serious injury crashes3.6%
↓-28.6%prior 7
Minor Injury15minor injury crashes10.7%
↑7.1%prior 14
Possible Injury13possible injury crashes9.3%
↓-23.5%prior 17
No Injury106no injury crashes75.7%
↓-8.6%prior 116

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 decreased from 52 in 2018 to 47 in 2019. Several other factors saw notable changes in count: crashes from 'Driving too fast for conditions' decreased from 12 to seven, and incidents involving 'Driver Distraction: Other interior distraction' dropped from 11 to four. Conversely, crashes due to 'FTYROW: At uncontrolled intersection' increased from six in 2018 to 10 in 2019.

Officer-Reported Primary Contributing Cause

Animal47 (33.6%)↓-9.6%prior 52
Lost Control11 (7.9%)↓-8.3%prior 12
FTYROW: At uncontrolled intersection10 (7.1%)↑66.7%prior 6
Driving too fast for conditions7 (5%)↓-41.7%prior 12
FTYROW: From stop sign7 (5%)→0.0%prior 7
Ran off road - straight6 (4.3%)↓-33.3%prior 9
Ran off road - left5 (3.6%)
Operating vehicle in an reckless, erratic, careless, negligent manner5 (3.6%)
Other (explain in narrative): Other5 (3.6%)
Ran Stop Sign4 (2.9%)↓-42.9%prior 7

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 proportion of crashes occurring on adverse road surfaces saw a decrease between the two periods. In 2019, 22.1% of crashes happened on wet, snowy, icy, or slush-covered roads (31 out of 140 crashes), compared to 29.5% in 2018 (46 out of 156 crashes). The distribution of crashes by lighting conditions remained relatively stable, with daylight crashes accounting for 49.3% of the total in 2019 versus 51.9% in 2018.

Weather

Clear62 (57.4%)
↓-20.5%prior 78
Cloudy29 (26.9%)
↑11.5%prior 26
Rain9 (8.3%)
Snow4 (3.7%)
↓-63.6%prior 11
Freezing rain/drizzle2 (1.9%)
↓-66.7%prior 6
Blowing Snow1 (0.9%)
Fog, smoke, smog1 (0.9%)

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

Lighting

Daylight69 (63.9%)
↓-14.8%prior 81
Dark - roadway not lighted24 (22.2%)
↓-27.3%prior 33
Dark - roadway lighted9 (8.3%)
↑80.0%prior 5
Dusk4 (3.7%)
↓-33.3%prior 6
Dawn2 (1.9%)

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

Road Surface

Dry72 (66.7%)
↓-12.2%prior 82
Wet13 (12.0%)
↓-23.5%prior 17
Snow11 (10.2%)
↓-26.7%prior 15
Ice/frost6 (5.6%)
↓-40.0%prior 10
Gravel5 (4.6%)
Slush1 (0.9%)

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

Vehicles & Demographics

The ranking of top vehicle makes involved in crashes shifted, with Chevrolet taking the top spot from Ford. Chevrolet-made vehicles were involved in 63 crashes in 2019 (up from 50 in 2018), while Ford vehicle involvements decreased from 50 to 35. Demographically, there was a notable increase in the number of persons aged 55-64 involved in crashes (from 33 to 52) and a decrease for the 35-44 age group (from 51 to 38).

Top Vehicle Makes (199 vehicles)

1
CHEV48 (24.1%)
↑14.3%prior 42
2
FORD35 (17.6%)
↓-30.0%prior 50
3
CHEVROLET15 (7.5%)
↑87.5%prior 8
4
CHRY8 (4%)
5
PONT7 (3.5%)
↓-12.5%prior 8
6
JEEP7 (3.5%)
↓-22.2%prior 9
7
DODG6 (3%)
↓-45.5%prior 11
8
KIA6 (3%)
9
GMC5 (2.5%)
↓-61.5%prior 13
10
TOYOTA5 (2.5%)

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

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

Sex Distribution (188 persons with recorded sex)

Male117 (62.2%)
↑23.2%prior 95
Female71 (37.8%)
↓-21.1%prior 90

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: 140
  • Total persons involved: 290
  • Total vehicles involved: 199

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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