Yearly Traffic Safety Analysis

452 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Iowa County recorded 452 total traffic crashes, a 1.8% increase from the 444 crashes documented in 2018. While the overall number of incidents remained relatively stable, the severity of outcomes worsened. The most notable year-over-year shift was a 44.1% increase in total injuries, which rose from 93 in 2018 to 134 in 2019.

452

▲ 1.8%was 444

Total Crash Events

4

▲ 100.0%was 2

Persons Killed

134

▲ 44.1%was 93

Persons Injured

3

▲ 50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 trends in Iowa County showed a slight increase in total volume from 444 incidents in 2018 to 452 in 2019. However, this small rise in total crashes was accompanied by a more significant increase in severity. Total fatalities doubled from 2 to 4, and the number of people injured grew by 44.1% from 93 to 134, indicating a trend toward more harmful collisions.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 2↑50.0%

0

Pedestrians Injured

Prior: 2↓-100.0%

1

Cyclists Injured

Prior: 0→%

133

Motorists Injured

Prior: 89↑49.4%

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 Monday with 79 incidents, a change from 2018 when Friday was the peak day with 74 crashes. The peak hour also shifted earlier, from the 5 p.m. hour in 2018 (38 crashes) to the 3 p.m. hour in 2019 (32 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 increased from 2018 to 2019. The number of fatal crashes rose from 2 to 3, and the fatal crash rate increased from 0.45 to 0.66 per 100 crashes. The proportion of crashes resulting in any injury grew from 18.7% in 2018 to 23.7% in 2019. Notably, the count of crashes involving a serious injury doubled, increasing from 5 in the prior period to 10 in the current period.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.7%
↑50.0%prior 2
Serious Injury10serious injury crashes2.2%
↑100.0%prior 5
Minor Injury42minor injury crashes9.3%
↑23.5%prior 34
Possible Injury55possible injury crashes12.2%
↑25.0%prior 44
No Injury342no injury crashes75.7%
↓-4.7%prior 359

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 years, though the count decreased slightly from 142 crashes in 2018 to 136 in 2019. The second-ranked factor changed from "Driving too fast for conditions" in 2018 (42 crashes) to "Ran off road - straight" in 2019 (47 crashes). Several factors saw significant count-based increases, including crashes attributed to "Followed too close," which rose by 78.9% from 19 to 34, and those from "Ran off road - straight," which increased by 38.2% from 34 to 47.

Officer-Reported Primary Contributing Cause

Animal136 (30.1%)↓-4.2%prior 142
Ran off road - straight47 (10.4%)↑38.2%prior 34
Lost Control45 (10%)↑15.4%prior 39
Driving too fast for conditions43 (9.5%)↑2.4%prior 42
Followed too close34 (7.5%)↑78.9%prior 19
Ran off road - left20 (4.4%)↓-28.6%prior 28
Other (explain in narrative): Other18 (4%)↑63.6%prior 11
Swerving/Evasive Action13 (2.9%)↑44.4%prior 9
FTYROW: From stop sign10 (2.2%)↓-47.4%prior 19
Driver Distraction: Other interior distraction9 (2%)↑12.5%prior 8

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 were more likely to occur in adverse conditions in 2019 compared to 2018. The proportion of incidents on adverse road surfaces like snow, ice, or wet pavement increased from 22.5% to 29.6% of all crashes. There was also a significant shift in lighting conditions, as the share of crashes occurring in unlit dark areas grew from 14.6% in 2018 to 24.8% in 2019.

Weather

Clear146 (44.8%)
↓-2.7%prior 150
Cloudy77 (23.6%)
↓-13.5%prior 89
Snow33 (10.1%)
↓-15.4%prior 39
Blowing Snow27 (8.3%)
Rain21 (6.4%)
↑61.5%prior 13
Freezing rain/drizzle11 (3.4%)
↑10.0%prior 10
Fog, smoke, smog5 (1.5%)
Other (explain in narrative)3 (0.9%)
Severe Winds2 (0.6%)
Sleet, hail1 (0.3%)

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

Lighting

Daylight186 (57.2%)
↓-12.3%prior 212
Dark - roadway not lighted112 (34.5%)
↑72.3%prior 65
Dark - roadway lighted11 (3.4%)
↓-38.9%prior 18
Dawn8 (2.5%)
↑14.3%prior 7
Dusk8 (2.5%)
→0.0%prior 8

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

Road Surface

Dry183 (56.1%)
↓-1.6%prior 186
Ice/frost60 (18.4%)
↑252.9%prior 17
Snow44 (13.5%)
↓-12.0%prior 50
Wet30 (9.2%)
↓-9.1%prior 33
Gravel8 (2.5%)
↓-52.9%prior 17
Slush1 (0.3%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, Ford and Chevrolet, remained the same across both periods, though the counts for both decreased. The 26-34 age group consistently represented the largest number of people involved in crashes. The count of individuals in this demographic increased from 128 in 2018 to 167 in 2019, raising their share of total persons involved from 16.1% to 18.5%.

Top Vehicle Makes (610 vehicles)

1
FORD101 (16.6%)
↓-9.0%prior 111
2
CHEV79 (13%)
↓-19.4%prior 98
3
CHEVROLET38 (6.2%)
→0.0%prior 38
4
GMC25 (4.1%)
↑92.3%prior 13
5
DODG22 (3.6%)
↓-21.4%prior 28
6
TOYT21 (3.4%)
↑133.3%prior 9
7
HOND21 (3.4%)
↑61.5%prior 13
8
JEEP18 (3%)
↓-18.2%prior 22
9
FREIGHTLINER17 (2.8%)
↑13.3%prior 15
10
TOYOTA16 (2.6%)
↑6.7%prior 15

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

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

Sex Distribution (569 persons with recorded sex)

Male339 (59.6%)
↓-0.6%prior 341
Female230 (40.4%)
↑40.2%prior 164

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: 452
  • Total persons involved: 901
  • Total vehicles involved: 610

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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