Yearly Traffic Safety Analysis

262 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Crawford County recorded 262 total crashes, a 13.9% increase from the 230 crashes reported in 2018. While total crashes rose, the number of people injured decreased by 20.2% from 99 to 79, and fatalities increased from 3 to 5. The most notable year-over-year change was the increase in crashes involving animals, which rose by 60.7% from 28 incidents in 2018 to 45 in 2019.

262

▲ 13.9%was 230

Total Crash Events

5

▲ 66.7%was 3

Persons Killed

79

▼ -20.2%was 99

Persons Injured

3

Fatal Crash Events

Note: "Persons Killed" (5) 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

The overall trend in Crawford County shows a rise in traffic collisions year-over-year. Total crashes increased by 13.9%, from 230 in 2018 to 262 in 2019. Despite this increase in total incidents, the number of people injured in these crashes saw a 20.2% decline, while fatalities increased from 3 to 5.

Vulnerable Road User Casualties

5

Motorists Killed

Prior: 3↑66.7%

79

Motorists Injured

Prior: 97↓-18.6%

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 showed some shifts between the two years. The peak day for collisions moved from Monday (40 crashes) in 2018 to Friday (49 crashes) in 2019. The peak hour for crashes, however, remained consistent at 5 p.m. in both periods, with 20 incidents in 2018 and 18 in 2019. A significant monthly variation was observed in November, which saw 47 crashes in 2019, more than double the 21 crashes recorded in November of 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

While the number of fatal crashes remained constant at 3 in both 2018 and 2019, the overall severity of crashes decreased. The proportion of crashes resulting in no injuries grew from 62.6% of all incidents in 2018 to 77.9% in 2019. Correspondingly, the share of crashes involving possible or minor injuries dropped significantly, with minor injury crashes falling from 12.2% of all incidents to 5.3%.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.1%
→0.0%prior 3
Serious Injury7serious injury crashes2.7%
↑75.0%prior 4
Minor Injury14minor injury crashes5.3%
↓-50.0%prior 28
Possible Injury34possible injury crashes13%
↓-33.3%prior 51
No Injury204no injury crashes77.9%
↑41.7%prior 144

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 the count increasing by 60.7% from 28 crashes in 2018 to 45 in 2019. 'Driver Distraction: Other interior distraction' saw a notable rise, jumping from 7 incidents to become the second-ranked factor with 20 crashes in 2019. Conversely, crashes attributed to 'Lost Control' decreased from 21 to 15, and incidents of 'Driving too fast for conditions' fell from 12 to 7.

Officer-Reported Primary Contributing Cause

Animal45 (17.2%)↑60.7%prior 28
Driver Distraction: Other interior distraction20 (7.6%)↑185.7%prior 7
Other (explain in narrative): Other19 (7.3%)↓-20.8%prior 24
Ran off road - straight16 (6.1%)↑33.3%prior 12
Lost Control15 (5.7%)↓-28.6%prior 21
Followed too close15 (5.7%)↑15.4%prior 13
Ran off road - left13 (5%)→0.0%prior 13
Ran Stop Sign9 (3.4%)↑50.0%prior 6
FTYROW: Making left turn8 (3.1%)↓-20.0%prior 10
Driver Distraction: Exterior distraction8 (3.1%)

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 dark conditions increased, accounting for 32.4% of all incidents in 2019 compared to 27.4% in 2018. The proportion of crashes occurring on dry road surfaces rose slightly from 57.4% to 60.7%. Conversely, collisions on adverse road surfaces like ice, snow, or wet pavement decreased, making up 23.7% of crashes in 2019, down from 29.6% in the prior year. The share of crashes in clear weather remained dominant and relatively stable across both periods.

Weather

Clear164 (71.9%)
↑21.5%prior 135
Cloudy28 (12.3%)
↓-17.6%prior 34
Snow15 (6.6%)
↑200.0%prior 5
Freezing rain/drizzle10 (4.4%)
↓-33.3%prior 15
Rain6 (2.6%)
↓-33.3%prior 9
Fog, smoke, smog2 (0.9%)
Blowing Snow2 (0.9%)
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

Daylight132 (57.6%)
↓-3.6%prior 137
Dark - roadway not lighted49 (21.4%)
↑32.4%prior 37
Dark - roadway lighted36 (15.7%)
↑38.5%prior 26
Dusk6 (2.6%)
Dawn5 (2.2%)
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

Dry159 (69.4%)
↑20.5%prior 132
Ice/frost22 (9.6%)
↑4.8%prior 21
Snow19 (8.3%)
↑46.2%prior 13
Wet19 (8.3%)
↓-32.1%prior 28
Gravel7 (3.1%)
→0.0%prior 7
Slush2 (0.9%)
↓-66.7%prior 6
Mud, dirt1 (0.4%)

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

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 396 in 2018 to 421 in 2019. Chevrolet and Ford remained the top two makes involved in collisions, with Chevrolet-branded vehicles increasing from 74 to 109 and Fords increasing from 59 to 75. In contrast, the number of Dodge vehicles involved in crashes decreased from 42 in 2018 to 21 in 2019. Among persons involved in crashes, the 45-54 age group saw its representation increase from 11.8% of all individuals in 2018 to 14.4% in 2019.

Top Vehicle Makes (421 vehicles)

1
CHEV80 (19%)
↑60.0%prior 50
2
FORD75 (17.8%)
↑27.1%prior 59
3
CHEVROLET29 (6.9%)
↑20.8%prior 24
4
GMC18 (4.3%)
↓-21.7%prior 23
5
NISS15 (3.6%)
↑25.0%prior 12
6
TOYT14 (3.3%)
↑40.0%prior 10
7
JEEP14 (3.3%)
↑7.7%prior 13
8
DODG13 (3.1%)
↓-55.2%prior 29
9
DODGE8 (1.9%)
↓-38.5%prior 13
10
CHRY7 (1.7%)
↓-50.0%prior 14

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

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

Sex Distribution (382 persons with recorded sex)

Male226 (59.2%)
↑37.8%prior 164
Female156 (40.8%)
↑23.8%prior 126

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: 262
  • Total persons involved: 568
  • Total vehicles involved: 421

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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