Yearly Traffic Safety Analysis

135 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Ida County recorded 135 total crashes, a 10.7% increase from the 122 crashes reported in 2018. While the number of fatal crashes decreased from two to one, the total number of fatalities rose from two to three. The most significant year-over-year change was a 41.5% increase in total injuries, which climbed from 41 in 2018 to 58 in 2019.

135

▲ 10.7%was 122

Total Crash Events

3

▲ 50.0%was 2

Persons Killed

58

▲ 41.5%was 41

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (3) 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 crash trends in Ida County showed an increase between 2018 and 2019. Total crashes rose by 10.7%, from 122 to 135 incidents. This was accompanied by a rise in crash consequences, with total injuries increasing by 41.5% and fatalities increasing from two to three.

Vulnerable Road User Casualties

3

Motorists Killed

Prior: 2↑50.0%

58

Motorists Injured

Prior: 40↑45.0%

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 temporal patterns of crashes showed some shifts between 2018 and 2019. The most common day for crashes moved from Friday (25 incidents) in 2018 to Thursday (27 incidents) in 2019. Similarly, the peak hour for collisions shifted from 6 PM in the prior year to 5 PM in the current year, which saw 13 crashes. While November was a high-volume month in both periods, March 2019 stood out with 18 crashes and all three of the year's fatalities.

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

In 2019, Ida County experienced 1 fatal crash, a decrease from 2 fatal crashes in 2018, lowering the fatal crash share from 1.6% to 0.7% of all incidents. However, the overall proportion of crashes involving any level of injury increased, rising from 23.0% in 2018 to 29.6% in 2019. This was driven by a notable increase in 'Possible Injury' crashes, which more than doubled from 12 to 25, and a rise in 'Serious Injury' crashes from 5 to 7.

Severity is per crash event (most severe injury). 1 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
↓-50.0%prior 2
Serious Injury7serious injury crashes5.2%
↑40.0%prior 5
Minor Injury8minor injury crashes5.9%
↓-27.3%prior 11
Possible Injury25possible injury crashes18.5%
↑108.3%prior 12
No Injury94no injury crashes69.6%
↑2.2%prior 92

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 leading contributing factor in both periods, with counts of 43 in 2018 and 44 in 2019. The second most common factor shifted year-over-year; 'Driving too fast for conditions' dropped from 11 incidents in 2018 to 6 in 2019, while 'Failure to yield from a stop sign' increased from 8 to 10 incidents, becoming the second-leading cause in 2019. 'Lost Control' was a consistent factor, cited in 7 crashes in both years.

Officer-Reported Primary Contributing Cause

Animal44 (32.6%)↑2.3%prior 43
FTYROW: From stop sign10 (7.4%)↑25.0%prior 8
Lost Control7 (5.2%)→0.0%prior 7
Improper Backing6 (4.4%)
Driving too fast for conditions6 (4.4%)↓-45.5%prior 11
FTYROW: From yield sign6 (4.4%)
Other (explain in narrative): Other6 (4.4%)
Ran off road - left6 (4.4%)
Ran off road - straight6 (4.4%)
FTYROW: Making left turn4 (3%)↓-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 in daylight remained consistent, accounting for 50.4% of incidents in 2019 compared to 50.8% in 2018. Crashes during clear weather decreased as a share of the total, from 50.0% in 2018 to 44.4% in 2019. Conversely, the number of crashes reported during adverse weather like rain or snow increased from 14 to 24. The percentage of collisions on dry road surfaces was nearly unchanged at approximately 44% in both periods.

Weather

Clear60 (61.9%)
↓-1.6%prior 61
Cloudy13 (13.4%)
↑18.2%prior 11
Rain10 (10.3%)
Blowing Snow5 (5.2%)
Snow4 (4.1%)
↓-60.0%prior 10
Fog, smoke, smog2 (2.1%)
Blowing sand, soil, dirt2 (2.1%)
Freezing rain/drizzle1 (1.0%)

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

Lighting

Daylight68 (69.4%)
↑9.7%prior 62
Dark - roadway not lighted16 (16.3%)
↑45.5%prior 11
Dark - roadway lighted7 (7.1%)
Dawn4 (4.1%)
Dusk2 (2.0%)
↓-71.4%prior 7
Dark - unknown roadway lighting1 (1.0%)

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

Road Surface

Dry60 (61.2%)
↑11.1%prior 54
Wet14 (14.3%)
↑133.3%prior 6
Snow13 (13.3%)
↓-23.5%prior 17
Ice/frost7 (7.1%)
Gravel2 (2.0%)
Slush2 (2.0%)

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 remained consistent, with Ford and Chevrolet being the most common in both 2018 and 2019. Ford vehicles were involved in 45 crashes in 2019, down from 47, while Chevrolet vehicles were involved in a combined 43 incidents, down from 46. A notable demographic shift occurred in the age of persons involved; the 16-20 age group saw its count nearly double from 28 to 52, increasing its share of total persons from 12.2% to 16.8%.

Top Vehicle Makes (200 vehicles)

1
FORD45 (22.5%)
↓-4.3%prior 47
2
CHEV32 (16%)
↑3.2%prior 31
3
CHEVROLET11 (5.5%)
↓-26.7%prior 15
4
DODG10 (5%)
↑25.0%prior 8
5
GMC10 (5%)
↑11.1%prior 9
6
BUIC8 (4%)
↑33.3%prior 6
7
PONT7 (3.5%)
8
PETERBILT7 (3.5%)
9
TOYT5 (2.5%)
10
BUICK4 (2%)

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

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

Sex Distribution (184 persons with recorded sex)

Male110 (59.8%)
↑35.8%prior 81
Female74 (40.2%)
↑60.9%prior 46

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: 135
  • Total persons involved: 310
  • Total vehicles involved: 200

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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