Yearly Traffic Safety Analysis

113 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Shelby County, total traffic crashes decreased by 5.8% from 120 incidents in 2018 to 113 in 2019. Despite the drop in overall crash volume, the most notable year-over-year change was a significant increase in crash severity, with fatalities rising from zero in 2018 to five in 2019.

113

▼ -5.8%was 120

Total Crash Events

5

Persons Killed

53

▲ 20.5%was 44

Persons Injured

5

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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

While the total number of crashes in Shelby County saw a modest decline from 120 in 2018 to 113 in 2019, the outcomes grew more severe. Total injuries increased by 20.5%, from 44 to 53, and the county recorded five fatalities in 2019 after having none in the prior year.

Vulnerable Road User Casualties

5

Motorists Killed

Prior: 0→%

53

Motorists Injured

Prior: 43↑23.3%

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 patterns shifted slightly between the two periods. In 2019, the peak day for crashes was Friday with 21 incidents, a change from 2018 when Saturday was the peak day, also with 21 incidents. The peak hour for collisions moved from 12 p.m. in 2018 (11 crashes) to 2 p.m. in 2019 (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 worsened significantly in 2019 compared to 2018. The county recorded five fatal crashes, accounting for 4.4% of all incidents, whereas there were no fatal crashes in the previous year. Consequently, the proportion of crashes resulting in no injury decreased from 71.7% in 2018 to 61.1% in 2019, while crashes involving some level of injury (including fatal) rose.

Outcome by Severity (Crash Events)

Fatal5fatal crashes4.4%
Serious Injury5serious injury crashes4.4%
→0.0%prior 5
Minor Injury18minor injury crashes15.9%
→0.0%prior 18
Possible Injury16possible injury crashes14.2%
↑45.5%prior 11
No Injury69no injury crashes61.1%
↓-19.8%prior 86

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

In 2019, collisions involving an animal became the leading contributing factor with 18 incidents, an increase from 13 incidents in 2018. "Lost Control" remained a consistent factor, accounting for 12 crashes in both years. Crashes attributed to "Followed too close" saw a notable increase, rising from 2 in 2018 to 7 in 2019. Conversely, crashes from "Ran off road - straight" decreased from 14 incidents in 2018 to just 3 in 2019.

Officer-Reported Primary Contributing Cause

Animal18 (15.9%)↑38.5%prior 13
Lost Control12 (10.6%)→0.0%prior 12
Other (explain in narrative): Other12 (10.6%)↑33.3%prior 9
FTYROW: From stop sign11 (9.7%)↑10.0%prior 10
Ran off road - left10 (8.8%)↑42.9%prior 7
Followed too close7 (6.2%)
Operating vehicle in an reckless, erratic, careless, negligent manner7 (6.2%)
Ran Stop Sign4 (3.5%)
Driving too fast for conditions3 (2.7%)↓-40.0%prior 5
FTYROW: Other (explain in narrative)3 (2.7%)

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 majority of crashes in both 2019 and 2018 occurred under similar conditions: on dry roads (71 and 69 crashes, respectively) and in clear weather (67 and 68 crashes, respectively). Crashes in daylight decreased from 81 in 2018 to 73 in 2019. There was a slight increase in crashes occurring in dark but lighted roadway conditions, which rose from 5 incidents in 2018 to 9 in 2019.

Weather

Clear67 (67.0%)
↓-1.5%prior 68
Cloudy20 (20.0%)
→0.0%prior 20
Blowing Snow3 (3.0%)
Freezing rain/drizzle3 (3.0%)
↓-50.0%prior 6
Snow3 (3.0%)
Rain2 (2.0%)
Severe Winds1 (1.0%)
Fog, smoke, smog1 (1.0%)

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

Lighting

Daylight73 (71.6%)
↓-9.9%prior 81
Dark - roadway not lighted16 (15.7%)
↓-5.9%prior 17
Dark - roadway lighted9 (8.8%)
↑80.0%prior 5
Dawn3 (2.9%)
Dusk1 (1.0%)

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

Road Surface

Dry71 (68.9%)
↑2.9%prior 69
Ice/frost10 (9.7%)
↑11.1%prior 9
Wet7 (6.8%)
↓-36.4%prior 11
Gravel6 (5.8%)
↓-25.0%prior 8
Snow5 (4.9%)
→0.0%prior 5
Slush3 (2.9%)
Mud, dirt1 (1.0%)

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 showed some changes year-over-year. Ford became the most common make with 45 vehicles in 2019, up from 29 in 2018, overtaking Chevrolet, which had a stable count of 41 vehicles in 2019 compared to 40 in 2018. The age distribution of persons involved also shifted, with the 55-64 age group seeing a significant increase from 28 individuals in 2018 to 49 in 2019.

Top Vehicle Makes (192 vehicles)

1
FORD45 (23.4%)
↑55.2%prior 29
2
CHEV28 (14.6%)
↓-9.7%prior 31
3
CHEVROLET13 (6.8%)
↑44.4%prior 9
4
BUIC10 (5.2%)
5
DODG8 (4.2%)
↓-33.3%prior 12
6
DODGE7 (3.6%)
↓-36.4%prior 11
7
CHRY6 (3.1%)
→0.0%prior 6
8
JEEP5 (2.6%)
→0.0%prior 5
9
GMC4 (2.1%)
↓-42.9%prior 7
10
PONT4 (2.1%)
↓-42.9%prior 7

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

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

Sex Distribution (167 persons with recorded sex)

Male100 (59.9%)
↑17.6%prior 85
Female67 (40.1%)
↑26.4%prior 53

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: 113
  • Total persons involved: 282
  • Total vehicles involved: 192

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

ThatCarHitMe.com · An Injuria.ai Company