Yearly Traffic Safety Analysis

258 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Mills County, traffic crashes remained relatively stable year-over-year, with 258 incidents recorded in 2019 compared to 261 in 2018, representing a 1.1% decrease. The most significant change was a sharp decline in crash fatalities, which fell by 66.7% from 6 deaths in 2018 to 2 in 2019. Concurrently, crashes involving driving under the influence (DUI) decreased from 19 to 9 over the same period.

258

▼ -1.1%was 261

Total Crash Events

2

▼ -66.7%was 6

Persons Killed

115

▲ 8.5%was 106

Persons Injured

2

▼ -60.0%was 5

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

The overall trend in Mills County shows a slight decrease in total crashes, falling from 261 in 2018 to 258 in 2019. While total incidents were stable, outcomes shifted, with a significant 66.7% reduction in fatalities (from 6 to 2). However, the total number of injuries reported increased by 8.5%, from 106 in 2018 to 115 in 2019.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 6↓-66.7%

115

Motorists Injured

Prior: 103↑11.7%

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 Mills County shifted between 2018 and 2019. The peak day for crashes moved from Sunday (42 incidents) in 2018 to Tuesday (42 incidents) in 2019. The evening commute remained the most frequent time for crashes; in 2018, the 4 p.m. and 5 p.m. hours were tied with 22 crashes each, while in 2019, the 5 p.m. hour was the standalone peak with 19 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 improved from 2018 to 2019, with fatal crashes decreasing from 5 to 2, and the fatal crash rate dropping from 1.92 to 0.78 per 100 crashes. While the total number of injury-involved crashes fell from 87 to 80, the count of serious injury crashes increased from 9 in 2018 to 13 in 2019. Consequently, the share of crashes resulting in serious injury rose from 3.4% to 5.0% of all incidents.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.8%
↓-60.0%prior 5
Serious Injury13serious injury crashes5%
↑44.4%prior 9
Minor Injury32minor injury crashes12.4%
↓-3.0%prior 33
Possible Injury35possible injury crashes13.6%
↓-22.2%prior 45
No Injury176no injury crashes68.2%
↑4.1%prior 169

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 primary contributing factors for crashes shifted between 2018 and 2019. Collisions involving an animal became the top-ranked factor in 2019, with the count of such incidents increasing from 29 to 43. In contrast, factors that led in 2018 saw significant reductions; crashes attributed to 'Ran off road - straight' and 'Lost Control' fell from 30 incidents each in 2018 to 15 and 19, respectively, in 2019. Crashes related to 'Driving too fast for conditions' increased from 18 to 21 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal43 (16.7%)↑48.3%prior 29
Other (explain in narrative): Other23 (8.9%)→0.0%prior 23
Driving too fast for conditions21 (8.1%)↑16.7%prior 18
Lost Control19 (7.4%)↓-36.7%prior 30
Ran off road - straight15 (5.8%)↓-50.0%prior 30
FTYROW: From stop sign12 (4.7%)↓-33.3%prior 18
Ran off road - left11 (4.3%)↓-21.4%prior 14
Driver Distraction: Other interior distraction9 (3.5%)
Followed too close9 (3.5%)↓-10.0%prior 10
Operating vehicle in an reckless, erratic, careless, negligent manner8 (3.1%)↓-11.1%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While the majority of crashes in both years occurred during daylight hours on dry roads, there was a notable decrease in crashes under adverse conditions. In 2019, 32 crashes were reported in conditions such as rain or snow, down from 48 such incidents in 2018. This trend was mirrored in road surface data, where crashes on adverse surfaces like wet, snow, or ice-covered roads declined from 75 in 2018 to 59 in 2019.

Weather

Clear153 (65.4%)
↑1.3%prior 151
Cloudy49 (20.9%)
↑6.5%prior 46
Rain13 (5.6%)
↑18.2%prior 11
Snow11 (4.7%)
↓-45.0%prior 20
Blowing Snow5 (2.1%)
↓-50.0%prior 10
Freezing rain/drizzle3 (1.3%)

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

Lighting

Daylight145 (62.0%)
→0.0%prior 145
Dark - roadway not lighted58 (24.8%)
↓-1.7%prior 59
Dark - roadway lighted14 (6.0%)
↓-26.3%prior 19
Dawn12 (5.1%)
↑50.0%prior 8
Dusk5 (2.1%)
↓-50.0%prior 10

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

Road Surface

Dry162 (69.2%)
↓-1.8%prior 165
Wet29 (12.4%)
↑31.8%prior 22
Snow22 (9.4%)
↓-12.0%prior 25
Gravel11 (4.7%)
Ice/frost7 (3.0%)
↓-70.8%prior 24
Slush1 (0.4%)
Sand1 (0.4%)
Other (explain in narrative)1 (0.4%)

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

Vehicles & Demographics

Chevrolet and Ford-branded vehicles were the most common makes involved in crashes in both periods. The number of Chevrolet vehicles involved decreased from 80 in 2018 to 72 in 2019, while Ford vehicles saw a drop from 75 to 58. Regarding the age of persons involved, the 16-20 age group saw a decrease from 80 individuals in 2018 to 60 in 2019. Conversely, involvement increased for several other age demographics, including the 35-44 group (from 48 to 94 persons) and the 55-64 group (from 46 to 88 persons).

Top Vehicle Makes (393 vehicles)

1
FORD58 (14.8%)
↓-22.7%prior 75
2
CHEVROLET39 (9.9%)
→0.0%prior 39
3
CHEV33 (8.4%)
↓-19.5%prior 41
4
DODG18 (4.6%)
↑157.1%prior 7
5
KIA14 (3.6%)
↓-12.5%prior 16
6
GMC12 (3.1%)
↑50.0%prior 8
7
NISS12 (3.1%)
↑20.0%prior 10
8
TOYOTA12 (3.1%)
9
DODGE12 (3.1%)
↓-7.7%prior 13
10
NR10 (2.5%)
↓-33.3%prior 15

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

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

Sex Distribution (349 persons with recorded sex)

Male216 (61.9%)
↑29.3%prior 167
Female133 (38.1%)
↑18.8%prior 112

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: 258
  • Total persons involved: 569
  • Total vehicles involved: 393

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

ThatCarHitMe.com · An Injuria.ai Company