Yearly Traffic Safety Analysis

337 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

Total crashes in Harrison County increased by 5.6% from 319 in 2018 to 337 in 2019. While total fatalities decreased from 5 to 2, the number of crashes attributed to 'Driving too fast for conditions' increased by 94%, rising from 18 incidents in the prior year to 35 in the current year.

337

▲ 5.6%was 319

Total Crash Events

2

▼ -60.0%was 5

Persons Killed

112

▲ 19.1%was 94

Persons Injured

2

▼ -33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (2) 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

Overall crash volume in Harrison County showed an upward trend, increasing by 5.6% from 319 incidents in 2018 to 337 in 2019. This was accompanied by a 19.1% rise in total injuries, from 94 to 112. In contrast, fatalities saw a significant decrease, dropping 60% from 5 in 2018 to 2 in 2019.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 5↓-60.0%

2

Pedestrians Injured

Prior: 1↑100.0%

1

Cyclists Injured

Prior: 1→0.0%

109

Motorists Injured

Prior: 92↑18.5%

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 remained largely consistent year-over-year. Wednesday was the peak day for crashes in both 2019 (58 crashes) and 2018 (56 crashes). However, the peak hour for crashes shifted earlier, moving from 9 p.m. in 2018 (25 crashes) to 6 p.m. in 2019 (27 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

While the number of fatal crashes decreased from 3 to 2 year-over-year, the severity distribution of other crashes shifted. The count of crashes resulting in a possible injury increased from 27 to 42, and their share of all crashes rose from 8.5% to 12.5%. Consequently, the proportion of non-injury crashes declined from 78.1% in 2018 to 73.6% in 2019.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.6%
↓-33.3%prior 3
Serious Injury13serious injury crashes3.9%
↑8.3%prior 12
Minor Injury32minor injury crashes9.5%
↑14.3%prior 28
Possible Injury42possible injury crashes12.5%
↑55.6%prior 27
No Injury248no injury crashes73.6%
↓-0.4%prior 249

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

The top two contributing factors remained unchanged, with 'Animal' (76 crashes in 2019 vs. 75 in 2018) and 'Lost Control' (40 crashes in both years) leading the list. A significant change was the 94% increase in the count of crashes attributed to 'Driving too fast for conditions,' which rose from 18 incidents in 2018 to 35 in 2019. In contrast, crashes due to 'Followed too close' decreased from 23 to 16 over the same period.

Officer-Reported Primary Contributing Cause

Animal76 (22.6%)↑1.3%prior 75
Lost Control40 (11.9%)→0.0%prior 40
Driving too fast for conditions35 (10.4%)↑94.4%prior 18
Ran off road - straight25 (7.4%)↑47.1%prior 17
Other (explain in narrative): Other18 (5.3%)↑28.6%prior 14
Followed too close16 (4.7%)↓-30.4%prior 23
Ran off road - left12 (3.6%)↓-45.5%prior 22
FTYROW: From stop sign10 (3%)↑66.7%prior 6
Ran Stop Sign7 (2.1%)→0.0%prior 7
Operating vehicle in an reckless, erratic, careless, negligent manner7 (2.1%)↓-56.3%prior 16

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 majority of crashes in both periods occurred in clear weather and on dry roads, with proportions remaining stable. There was a notable increase in crashes under adverse winter conditions in 2019 compared to 2018. Incidents on roads with snow increased from 12 to 22, and crashes reported during freezing rain or drizzle rose from 2 to 12.

Weather

Clear175 (64.8%)
↑1.7%prior 172
Cloudy40 (14.8%)
↓-16.7%prior 48
Snow23 (8.5%)
↑64.3%prior 14
Freezing rain/drizzle12 (4.4%)
Rain8 (3.0%)
↓-63.6%prior 22
Blowing Snow6 (2.2%)
Fog, smoke, smog5 (1.9%)
Severe Winds1 (0.4%)

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

Lighting

Daylight182 (66.7%)
↑5.8%prior 172
Dark - roadway not lighted66 (24.2%)
↑8.2%prior 61
Dark - roadway lighted10 (3.7%)
↓-47.4%prior 19
Dusk10 (3.7%)
↓-23.1%prior 13
Dawn4 (1.5%)
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

Dry180 (66.9%)
↑1.7%prior 177
Wet29 (10.8%)
↓-25.6%prior 39
Ice/frost24 (8.9%)
↑33.3%prior 18
Snow22 (8.2%)
↑83.3%prior 12
Gravel10 (3.7%)
↓-9.1%prior 11
Slush4 (1.5%)
↓-55.6%prior 9

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, including Ford and Chevrolet, remained consistent in the top rankings year-over-year. A significant demographic shift occurred in the age of persons involved in collisions. The number of individuals in the 45-54 age group more than doubled, increasing from 49 in 2018 to 107 in 2019, making it the largest age group involved in crashes in the current period.

Top Vehicle Makes (458 vehicles)

1
FORD84 (18.3%)
↑16.7%prior 72
2
CHEV72 (15.7%)
↑46.9%prior 49
3
CHEVROLET35 (7.6%)
↓-2.8%prior 36
4
GMC19 (4.1%)
↑35.7%prior 14
5
DODG17 (3.7%)
↓-10.5%prior 19
6
FREIGHTLINER15 (3.3%)
↑36.4%prior 11
7
DODGE14 (3.1%)
↓-6.7%prior 15
8
TOYT11 (2.4%)
↑57.1%prior 7
9
TOYOTA11 (2.4%)
↓-35.3%prior 17
10
JEEP11 (2.4%)
↓-21.4%prior 14

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

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

Sex Distribution (415 persons with recorded sex)

Male274 (66.0%)
↑33.7%prior 205
Female141 (34.0%)
↑20.5%prior 117

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: 337
  • Total persons involved: 675
  • Total vehicles involved: 458

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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