Yearly Traffic Safety Analysis

91 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Howard County recorded 91 total crashes, a 10.8% decrease from the 102 crashes reported in 2018. While overall crashes and injuries declined, the county experienced one fatal crash in 2019, which resulted in one fatality, compared to zero fatal crashes or fatalities in the prior year.

91

▼ -10.8%was 102

Total Crash Events

1

Persons Killed

23

▼ -39.5%was 38

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) 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

Crash trends in Howard County showed a year-over-year decrease. Total crashes fell by 10.8%, from 102 in 2018 to 91 in 2019. The number of people injured in these incidents also decreased from 38 to 23, a 39.5% reduction. However, the county registered one fatality in 2019, an increase from zero in the previous year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 0→%

0

Other Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 0→%

21

Motorists Injured

Prior: 37↓-43.2%

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 highest number of crashes occurred on Thursdays and Saturdays (16 each), a change from 2018's peak days of Wednesday and Friday (18 each). The peak hour for crashes remained 6 a.m. in both years, with the count of crashes during that hour increasing from 9 to 11. The afternoon crash distribution also changed, with a notable peak at 2 p.m. in 2019 that was not present in the prior year.

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 saw a mixed change year-over-year. Howard County recorded one fatal crash in 2019, representing 1.1% of all crashes, whereas no fatal crashes occurred in 2018. Conversely, the number of crashes resulting in serious injuries dropped significantly from 5 in 2018 to just 1 in 2019. The proportion of crashes with no reported injuries increased from 74.5% in 2018 to 78.0% in 2019.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.1%
Serious Injury1serious injury crashes1.1%
↓-80.0%prior 5
Minor Injury12minor injury crashes13.2%
→0.0%prior 12
Possible Injury6possible injury crashes6.6%
↓-33.3%prior 9
No Injury71no injury crashes78%
↓-6.6%prior 76

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 leading contributing factor in both periods, though the count of such incidents decreased by 35.6% from 45 in 2018 to 29 in 2019. The second most common factor in 2019 was 'Lost Control,' which saw its count increase by 80% from 5 to 9 incidents year-over-year, moving it from the fifth-ranked factor to the second. Crashes attributed to 'Ran off road - straight' declined from 11 to 7.

Officer-Reported Primary Contributing Cause

Animal29 (31.9%)↓-35.6%prior 45
Lost Control9 (9.9%)↑80.0%prior 5
Ran off road - straight7 (7.7%)↓-36.4%prior 11
Ran off road - left5 (5.5%)
Other (explain in narrative): Other5 (5.5%)
Driving too fast for conditions5 (5.5%)→0.0%prior 5
Followed too close4 (4.4%)
Ran Stop Sign3 (3.3%)
FTYROW: From driveway3 (3.3%)
FTYROW: Other (explain in narrative)2 (2.2%)

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 data indicates a shift towards more crashes occurring in adverse conditions. In 2019, 22.0% of crashes happened during weather events like snow or freezing rain, up from a 12.7% share in 2018. Similarly, crashes on adverse road surfaces such as snow, ice, or slush increased from 21 incidents in 2018 to 33 in 2019. Despite this, a larger number of crashes in 2019 occurred during daylight (43) compared to 2018 (32).

Weather

Clear30 (43.5%)
↓-18.9%prior 37
Cloudy16 (23.2%)
↑45.5%prior 11
Snow8 (11.6%)
↓-11.1%prior 9
Freezing rain/drizzle7 (10.1%)
Severe Winds3 (4.3%)
Rain3 (4.3%)
Blowing Snow1 (1.4%)
Blowing sand, soil, dirt1 (1.4%)

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

Lighting

Daylight43 (61.4%)
↑34.4%prior 32
Dark - roadway not lighted18 (25.7%)
↓-18.2%prior 22
Dawn3 (4.3%)
↓-40.0%prior 5
Dark - roadway lighted3 (4.3%)
Dusk2 (2.9%)
Dark - unknown roadway lighting1 (1.4%)

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

Road Surface

Dry30 (43.5%)
↓-6.3%prior 32
Snow11 (15.9%)
→0.0%prior 11
Ice/frost9 (13.0%)
↑28.6%prior 7
Slush7 (10.1%)
Gravel6 (8.7%)
↓-40.0%prior 10
Wet4 (5.8%)
Mud, dirt2 (2.9%)

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

Vehicles & Demographics

Chevrolet and Ford vehicles were the most common makes involved in crashes during both periods. While the number of Fords remained constant at 24, the count of Chevrolets (combining 'CHEV' and 'CHEVROLET') decreased from 44 in 2018 to 26 in 2019. An analysis of persons involved shows a demographic shift, with the 55-64 age group's involvement increasing from 19 individuals in 2018 to 30 in 2019. In contrast, the number of persons in the 26-34 age group involved in crashes fell from 32 to 23.

Top Vehicle Makes (124 vehicles)

1
FORD24 (19.4%)
→0.0%prior 24
2
CHEV18 (14.5%)
↓-40.0%prior 30
3
CHEVROLET8 (6.5%)
↓-42.9%prior 14
4
DODG7 (5.6%)
→0.0%prior 7
5
JEEP6 (4.8%)
6
KIA5 (4%)
7
DODGE5 (4%)
8
BUICK5 (4%)
9
BUIC5 (4%)
10
GMC4 (3.2%)
↓-33.3%prior 6

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

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

Sex Distribution (119 persons with recorded sex)

Male82 (68.9%)
↑41.4%prior 58
Female37 (31.1%)
↑42.3%prior 26

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: 91
  • Total persons involved: 176
  • Total vehicles involved: 124

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

ThatCarHitMe.com · An Injuria.ai Company