Yearly Traffic Safety Analysis

242 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Grundy County, total vehicle crashes increased from 208 in 2018 to 242 in 2019, a 16.3% rise. During this period, the number of fatalities rose from one to three, and total injuries increased from 66 to 72. The most notable year-over-year change was the increase in fatal crashes from one to two and the corresponding increase in persons killed from one to three.

242

▲ 16.3%was 208

Total Crash Events

3

▲ 200.0%was 1

Persons Killed

72

▲ 9.1%was 66

Persons Injured

2

▲ 100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

Crash data indicates a rising trend in Grundy County from 2018 to 2019. Total crashes increased by 16.3%, from 208 to 242 incidents. This upward trend was also reflected in crash outcomes, with total injuries rising by 9.1% from 66 to 72 and fatalities increasing from one to three.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 1↑200.0%

0

Other Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

70

Motorists Injured

Prior: 65↑7.7%

1

Other Injured

Prior: 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 timing of crashes shifted between the two periods. In 2019, the peak day for crashes was Tuesday with 40 incidents, a change from 2018 when Thursday and Friday were the peak days with 35 crashes each. The peak hour also moved later in the day, from 3 p.m. in 2018 (18 crashes) to 6 p.m. in 2019 (22 crashes). Monthly patterns also varied, with February being the month with the most crashes in 2019 (32), whereas November had the highest count in 2018 (36).

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 saw mixed changes year-over-year. The number of fatal crashes doubled from one in 2018 to two in 2019, increasing their share of all crashes from 0.5% to 0.8%. Conversely, the number of serious injury crashes decreased from five to four. While the total count of all injury-related crashes (serious, minor, and possible) rose from 52 to 56, their proportion relative to all crashes fell slightly from 25.0% in 2018 to 23.1% in 2019.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.8%
↑100.0%prior 1
Serious Injury4serious injury crashes1.7%
↓-20.0%prior 5
Minor Injury27minor injury crashes11.2%
↑22.7%prior 22
Possible Injury25possible injury crashes10.3%
→0.0%prior 25
No Injury184no injury crashes76%
↑18.7%prior 155

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 years, increasing from 74 incidents in 2018 to 81 in 2019. The most significant shift was in crashes attributed to a driver losing control, which more than doubled from 12 to 25 incidents, becoming the second most common factor in 2019. Crashes where a driver was going too fast for conditions also increased from 14 to 17, while incidents involving running off the road straight decreased from 13 to 9.

Officer-Reported Primary Contributing Cause

Animal81 (33.5%)↑9.5%prior 74
Lost Control25 (10.3%)↑108.3%prior 12
Ran off road - left17 (7%)↑41.7%prior 12
Driving too fast for conditions17 (7%)↑21.4%prior 14
Other (explain in narrative): Other10 (4.1%)→0.0%prior 10
Ran off road - straight9 (3.7%)↓-30.8%prior 13
FTYROW: From stop sign9 (3.7%)
Driver Distraction: Other interior distraction6 (2.5%)↓-14.3%prior 7
FTYROW: At uncontrolled intersection6 (2.5%)
Followed too close6 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While the number of crashes on clear days and dry roads remained identical at 93 in both 2018 and 2019, crashes under adverse road conditions increased notably. Crashes on snowy roads rose from 18 to 30, and incidents on icy or frosty surfaces more than doubled from 11 to 24. Similarly, crashes on wet roads increased from 9 to 19. The number of crashes occurring in daylight also grew from 88 in 2018 to 114 in 2019.

Weather

Clear93 (53.8%)
→0.0%prior 93
Cloudy38 (22.0%)
↑35.7%prior 28
Snow14 (8.1%)
↓-6.7%prior 15
Blowing Snow11 (6.4%)
Freezing rain/drizzle6 (3.5%)
Rain6 (3.5%)
Fog, smoke, smog3 (1.7%)
Severe Winds1 (0.6%)
Blowing sand, soil, dirt1 (0.6%)

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

Lighting

Daylight114 (65.9%)
↑29.5%prior 88
Dark - roadway not lighted39 (22.5%)
↑5.4%prior 37
Dark - roadway lighted11 (6.4%)
↑83.3%prior 6
Dusk5 (2.9%)
↓-37.5%prior 8
Dawn3 (1.7%)
↓-40.0%prior 5
Dark - unknown roadway lighting1 (0.6%)

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

Road Surface

Dry93 (53.8%)
→0.0%prior 93
Snow30 (17.3%)
↑66.7%prior 18
Ice/frost24 (13.9%)
↑118.2%prior 11
Wet19 (11.0%)
↑111.1%prior 9
Gravel5 (2.9%)
↓-37.5%prior 8
Mud, dirt1 (0.6%)
Slush1 (0.6%)

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 broadly similar year-over-year, with Ford and Chevrolet models leading in both periods. In 2019, Ford-made vehicles were involved in 68 crashes, up from 54 in 2018, while Chevrolet models were involved in 59 crashes, up from 56. Regarding persons involved in crashes, the 35-44 age group saw a significant increase from 39 individuals in 2018 to 82 in 2019. The 16-20 age group also saw a notable rise, from 50 to 79 persons involved.

Top Vehicle Makes (341 vehicles)

1
FORD68 (19.9%)
↑25.9%prior 54
2
CHEV59 (17.3%)
↑5.4%prior 56
3
TOYO15 (4.4%)
4
CHEVROLET14 (4.1%)
↑16.7%prior 12
5
DODG14 (4.1%)
→0.0%prior 14
6
CHRY12 (3.5%)
↑33.3%prior 9
7
JEEP11 (3.2%)
↑22.2%prior 9
8
HYUN9 (2.6%)
9
TOYT8 (2.3%)
→0.0%prior 8
10
GMC8 (2.3%)
↑60.0%prior 5

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 (322 persons with recorded sex)

Male194 (60.2%)
↑56.5%prior 124
Female128 (39.8%)
↑64.1%prior 78

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 10, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 242
  • Total persons involved: 501
  • Total vehicles involved: 341

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 10, 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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