Yearly Traffic Safety Analysis

1,348 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Dallas County recorded 1,348 total vehicle crashes, a 1.9% decrease from the 1,374 crashes reported in 2018. While overall crashes and fatalities declined, the number of crashes resulting in serious injuries increased significantly. Fatalities dropped from 6 in 2018 to 3 in 2019, but serious injury crashes rose from 16 to 28 during the same period.

1,348

▼ -1.9%was 1,374

Total Crash Events

3

▼ -50.0%was 6

Persons Killed

426

▼ -0.7%was 429

Persons Injured

3

▼ -50.0%was 6

Fatal Crash Events

Note: "Persons Killed" (3) 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

Overall, traffic crashes in Dallas County saw a slight year-over-year decline. The total number of crashes decreased by 1.9%, from 1,374 in 2018 to 1,348 in 2019. Similarly, total injuries fell slightly from 429 to 426, while total fatalities were halved, decreasing from 6 to 3.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 5↓-40.0%

3

Pedestrians Injured

Prior: 0→%

4

Cyclists Injured

Prior: 6↓-33.3%

419

Motorists Injured

Prior: 423↓-0.9%

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 in Dallas County showed shifts between the two periods. The peak day for crashes moved from Friday in 2018 (261 crashes) to Monday in 2019 (234 crashes). The busiest hour for collisions also shifted slightly later in the day, from the 4 p.m. hour in 2018 (125 crashes) to the 5 p.m. hour in 2019 (128 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

Crash severity outcomes changed notably year-over-year. The fatal crash rate was halved, decreasing from 0.44% in 2018 to 0.22% in 2019, with fatal crashes dropping from 6 to 3. Conversely, serious injury crashes increased by 75%, rising from 16 incidents in 2018 to 28 in 2019. The share of no-injury crashes remained stable, accounting for just over 75% of all incidents in both years.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.2%
↓-50.0%prior 6
Serious Injury28serious injury crashes2.1%
↑75.0%prior 16
Minor Injury122minor injury crashes9.1%
↑1.7%prior 120
Possible Injury181possible injury crashes13.4%
↓-8.6%prior 198
No Injury1,014no injury crashes75.2%
↓-1.9%prior 1,034

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 leading contributing factors for crashes saw a shift in ranking between 2018 and 2019. 'Followed too close' became the most cited factor in 2019 with 197 crashes, despite its count decreasing from 202 in the prior year. 'Animal' involvement, the top factor in 2018 with 203 crashes, dropped to the second position in 2019 with a count of 179. Notably, crashes attributed to 'Driving too fast for conditions' increased by 15.6%, from 96 incidents in 2018 to 111 in 2019.

Officer-Reported Primary Contributing Cause

Followed too close197 (14.6%)↓-2.5%prior 202
Animal179 (13.3%)↓-11.8%prior 203
Driving too fast for conditions111 (8.2%)↑15.6%prior 96
Other (explain in narrative): Other90 (6.7%)↓-19.6%prior 112
FTYROW: From stop sign74 (5.5%)↑8.8%prior 68
FTYROW: Making left turn66 (4.9%)↑6.5%prior 62
Lost Control63 (4.7%)↓-20.3%prior 79
Ran off road - left54 (4%)↓-10.0%prior 60
Ran off road - straight51 (3.8%)↓-13.6%prior 59
Driver Distraction: Other interior distraction51 (3.8%)↓-7.3%prior 55

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 distribution of environmental conditions during crashes remained largely consistent year-over-year. In both 2019 and 2018, the majority of crashes occurred during daylight hours (63.4% and 62.7%, respectively) and on dry road surfaces (59.3% and 60.2%, respectively). Crashes in clear weather also made up the majority in both periods, accounting for 55.1% of crashes in 2019 and 53.8% in 2018, indicating no significant shift in the prevalence of adverse-condition crashes.

Weather

Clear743 (62.1%)
↑0.5%prior 739
Cloudy251 (21.0%)
↓-0.8%prior 253
Snow69 (5.8%)
↓-8.0%prior 75
Rain66 (5.5%)
↓-5.7%prior 70
Blowing Snow30 (2.5%)
↑200.0%prior 10
Freezing rain/drizzle24 (2.0%)
↓-48.9%prior 47
Fog, smoke, smog8 (0.7%)
↑60.0%prior 5
Severe Winds3 (0.3%)
Sleet, hail1 (0.1%)
Other (explain in narrative)1 (0.1%)

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

Lighting

Daylight855 (71.3%)
↓-0.8%prior 862
Dark - roadway lighted150 (12.5%)
↑0.7%prior 149
Dark - roadway not lighted119 (9.9%)
↓-7.0%prior 128
Dawn36 (3.0%)
↑16.1%prior 31
Dusk33 (2.8%)
↑3.1%prior 32
Dark - unknown roadway lighting6 (0.5%)

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

Road Surface

Dry800 (66.8%)
↓-3.3%prior 827
Wet167 (14.0%)
↑14.4%prior 146
Ice/frost111 (9.3%)
↑60.9%prior 69
Snow85 (7.1%)
↓-22.7%prior 110
Gravel17 (1.4%)
↓-37.0%prior 27
Slush14 (1.2%)
↓-30.0%prior 20
Other (explain in narrative)2 (0.2%)
Mud, dirt1 (0.1%)

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

Vehicles & Demographics

The composition of vehicles involved in crashes showed consistency in make, but some shifts in driver demographics. Ford and Chevrolet were the top two makes in both periods; Ford-made vehicles increased from 369 to 377, while Chevrolet-branded vehicles decreased from a combined 450 to 422. When examining the age of persons involved, there was a notable increase in the 65+ age group, which grew from 231 individuals in 2018 to 286 in 2019.

Top Vehicle Makes (2,371 vehicles)

1
FORD377 (15.9%)
↑2.2%prior 369
2
CHEV299 (12.6%)
↓-9.4%prior 330
3
TOYT156 (6.6%)
↓-3.1%prior 161
4
CHEVROLET123 (5.2%)
↑2.5%prior 120
5
JEEP112 (4.7%)
↑25.8%prior 89
6
HOND101 (4.3%)
↓-14.4%prior 118
7
DODG95 (4%)
↓-1.0%prior 96
8
NISS90 (3.8%)
↓-4.3%prior 94
9
GMC64 (2.7%)
↑18.5%prior 54
10
HYUN59 (2.5%)
↑55.3%prior 38

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

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

Sex Distribution (2,258 persons with recorded sex)

Male1,233 (54.6%)
↑12.7%prior 1,094
Female1,025 (45.4%)
↑10.6%prior 927

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: 1,348
  • Total persons involved: 3,021
  • Total vehicles involved: 2,371

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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