Yearly Traffic Safety Analysis

310 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Hardin County recorded 310 total crashes, a 5.8% increase from the 293 crashes reported in 2018. While the number of fatal crashes remained stable at 4, the number of people injured rose significantly by 38.0%, from 79 in 2018 to 109 in 2019. The most notable year-over-year shift was this substantial increase in total injuries.

310

▲ 5.8%was 293

Total Crash Events

5

▲ 25.0%was 4

Persons Killed

109

▲ 38.0%was 79

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 crash trends in Hardin County showed an increase between 2018 and 2019. Total crashes rose by 5.8%, from 293 to 310 incidents. This increase was accompanied by a 38.0% rise in injuries and a 25.0% rise in fatalities year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 4↑25.0%

2

Pedestrians Injured

Prior: 0→%

2

Cyclists Injured

Prior: 0→%

105

Motorists Injured

Prior: 79↑32.9%

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 a notable shift between the two periods. While Friday remained the peak day for crashes in both 2018 (48 crashes) and 2019 (58 crashes), the peak hour changed significantly. In 2019, the most crashes occurred at 7 a.m. with 27 incidents, a shift from the 7 p.m. peak hour observed in 2018, which saw 26 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 worsened in 2019 compared to the prior year. While the number of fatal crashes was unchanged at 4, serious injury crashes doubled from 5 to 10. Consequently, the share of crashes resulting in a serious injury rose from 1.7% to 3.2%. Overall, the proportion of crashes involving any level of injury increased from 21.2% in 2018 to 28.7% in 2019.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.3%
→0.0%prior 4
Serious Injury10serious injury crashes3.2%
↑100.0%prior 5
Minor Injury28minor injury crashes9%
↑12.0%prior 25
Possible Injury47possible injury crashes15.2%
↑67.9%prior 28
No Injury221no injury crashes71.3%
↓-4.3%prior 231

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 an 'Animal' remained the top contributing factor in both 2018 (99 incidents) and 2019 (93 incidents), despite a 6.1% decrease in its count. 'Lost Control' was the second-ranked factor in both periods, with its count dropping from 31 to 28. Notably, crashes attributed to 'Followed too close' more than tripled, increasing from 4 incidents in 2018 to 13 in 2019. Similarly, crashes involving 'Ran Stop Sign' rose from 6 to 11 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal93 (30%)↓-6.1%prior 99
Lost Control28 (9%)↓-9.7%prior 31
Driving too fast for conditions18 (5.8%)↑12.5%prior 16
Ran off road - straight15 (4.8%)↓-6.3%prior 16
Operating vehicle in an reckless, erratic, careless, negligent manner15 (4.8%)↑150.0%prior 6
Ran off road - left15 (4.8%)↓-11.8%prior 17
Other (explain in narrative): Other14 (4.5%)↓-30.0%prior 20
Followed too close13 (4.2%)
Ran Stop Sign11 (3.5%)↑83.3%prior 6
FTYROW: From stop sign11 (3.5%)↑83.3%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

Crashes in clear weather and on dry roads remained the most common scenarios in both 2018 and 2019. However, there was a notable increase in crashes occurring under adverse conditions. Incidents on roads with snow, ice, or slush increased from 40 in 2018 to 61 in 2019. Similarly, crashes in 'Dark - roadway not lighted' conditions rose from 35 to 48 incidents year-over-year.

Weather

Clear135 (60.0%)
↓-2.2%prior 138
Cloudy48 (21.3%)
↑54.8%prior 31
Snow17 (7.6%)
↑21.4%prior 14
Blowing Snow10 (4.4%)
Rain8 (3.6%)
↑14.3%prior 7
Freezing rain/drizzle5 (2.2%)
→0.0%prior 5
Fog, smoke, smog2 (0.9%)

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

Lighting

Daylight149 (65.6%)
↑14.6%prior 130
Dark - roadway not lighted48 (21.1%)
↑37.1%prior 35
Dark - roadway lighted20 (8.8%)
↑25.0%prior 16
Dawn5 (2.2%)
↓-50.0%prior 10
Dusk4 (1.8%)
↓-60.0%prior 10
Dark - unknown roadway lighting1 (0.4%)

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

Road Surface

Dry132 (58.7%)
↓-3.6%prior 137
Snow33 (14.7%)
↑57.1%prior 21
Ice/frost25 (11.1%)
↑78.6%prior 14
Wet22 (9.8%)
↑29.4%prior 17
Gravel10 (4.4%)
↑25.0%prior 8
Slush3 (1.3%)
↓-40.0%prior 5

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

Vehicles & Demographics

The top vehicle makes involved in crashes shifted between the two years; while Chevrolet was most common in 2018 (106 vehicles), Ford took the top spot in 2019 with 97 vehicles involved. The number of people involved in crashes increased across most age demographics, consistent with the overall rise in collisions. Notably, the number of individuals aged 26-34 involved in crashes grew from 69 to 100, and those aged 35-44 increased from 67 to 106.

Top Vehicle Makes (435 vehicles)

1
FORD97 (22.3%)
↑16.9%prior 83
2
CHEV62 (14.3%)
↓-20.5%prior 78
3
CHEVROLET25 (5.7%)
↓-10.7%prior 28
4
DODG24 (5.5%)
↑14.3%prior 21
5
TOYT17 (3.9%)
↑183.3%prior 6
6
GMC16 (3.7%)
↑33.3%prior 12
7
CHRY15 (3.4%)
→0.0%prior 15
8
JEEP15 (3.4%)
↑15.4%prior 13
9
BUIC14 (3.2%)
↑75.0%prior 8
10
PONT12 (2.8%)
↑20.0%prior 10

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

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

Sex Distribution (409 persons with recorded sex)

Male244 (59.7%)
↑42.7%prior 171
Female165 (40.3%)
↑51.4%prior 109

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: 310
  • Total persons involved: 658
  • Total vehicles involved: 435

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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