Yearly Traffic Safety Analysis

230 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In ADAIR County, Iowa, total crashes increased by 4.07% from 221 in 2018 to 230 in 2019. Fatalities rose by 50% from 4 to 6, and injuries increased by 3.95% from 76 to 79. The most significant year-over-year change was a 300% increase in DUI crashes, rising from 3 in 2018 to 12 in 2019.

230

▲ 4.1%was 221

Total Crash Events

6

▲ 50.0%was 4

Persons Killed

79

▲ 3.9%was 76

Persons Injured

6

▲ 50.0%was 4

Fatal Crash Events

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (6) 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 metrics in ADAIR County showed an upward trend from 2018 to 2019. Total crashes increased by 4.07%, rising from 221 to 230 incidents. Fatalities saw a substantial 50% increase, going from 4 to 6, while total injuries also rose by 3.95% from 76 to 79.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

5

Motorists Killed

Prior: 4↑25.0%

1

Pedestrians Injured

Prior: 0→%

78

Motorists Injured

Prior: 75↑4.0%

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

Temporal patterns of crashes showed notable shifts year-over-year. The peak day for crashes changed from Wednesday in 2018 (35 crashes) to Sunday in 2019 (46 crashes), representing a 48.57% increase in Sunday crashes. The peak crash hour also shifted from 8 PM in 2018 (27 crashes) to 4 PM in 2019 (18 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

The overall severity of crashes increased from 2018 to 2019, with the fatal crash rate rising from 1.81% to 2.61%. Fatal crashes increased by 50% from 4 to 6, and serious injury crashes rose by 71.43% from 7 to 12. Conversely, minor injury crashes decreased by 41.67%, falling from 36 to 21 incidents.

Outcome by Severity (Crash Events)

Fatal6fatal crashes2.6%
↑50.0%prior 4
Serious Injury12serious injury crashes5.2%
↑71.4%prior 7
Minor Injury21minor injury crashes9.1%
↓-41.7%prior 36
Possible Injury33possible injury crashes14.3%
↑73.7%prior 19
No Injury158no injury crashes68.7%
↑1.9%prior 155

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 factor, 'Animal' collisions, decreased by 8.93% from 56 incidents in 2018 to 51 in 2019. 'Lost Control' crashes saw an 18.42% reduction, falling from 38 to 31, while 'Ran off road - straight' crashes slightly decreased by 2.86% from 35 to 34. Notably, 'Followed too close' crashes experienced a significant 160% increase, rising from 5 to 13 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal51 (22.2%)↓-8.9%prior 56
Ran off road - straight34 (14.8%)↓-2.9%prior 35
Lost Control31 (13.5%)↓-18.4%prior 38
Followed too close13 (5.7%)↑160.0%prior 5
Other (explain in narrative): Other12 (5.2%)↑20.0%prior 10
Ran off road - left12 (5.2%)↓-20.0%prior 15
Driving too fast for conditions10 (4.3%)↑42.9%prior 7
Operating vehicle in an reckless, erratic, careless, negligent manner9 (3.9%)
Driver Distraction: Other interior distraction7 (3%)↑40.0%prior 5
FTYROW: From stop sign6 (2.6%)↓-14.3%prior 7

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 occurring in clear weather increased by 16.49% from 97 in 2018 to 113 in 2019, while those in snowy conditions rose by 81.82% from 11 to 20. Crashes on wet road surfaces saw a significant 110% increase, climbing from 20 to 42 incidents. Conversely, crashes on icy/frosty roads decreased by 39.13% from 23 to 14.

Weather

Clear113 (57.7%)
↑16.5%prior 97
Cloudy25 (12.8%)
↓-3.8%prior 26
Snow20 (10.2%)
↑81.8%prior 11
Rain13 (6.6%)
↓-7.1%prior 14
Severe Winds9 (4.6%)
Fog, smoke, smog7 (3.6%)
Blowing Snow4 (2.0%)
Freezing rain/drizzle4 (2.0%)
↓-71.4%prior 14
Other (explain in narrative)1 (0.5%)

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

Lighting

Daylight118 (59.6%)
↑10.3%prior 107
Dark - roadway not lighted60 (30.3%)
↑17.6%prior 51
Dark - roadway lighted10 (5.1%)
↑100.0%prior 5
Dawn6 (3.0%)
↑20.0%prior 5
Dusk4 (2.0%)
↓-42.9%prior 7

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

Road Surface

Dry113 (57.4%)
↑5.6%prior 107
Wet42 (21.3%)
↑110.0%prior 20
Snow16 (8.1%)
↑14.3%prior 14
Ice/frost14 (7.1%)
↓-39.1%prior 23
Slush5 (2.5%)
→0.0%prior 5
Gravel4 (2.0%)
Mud, dirt2 (1.0%)
Other (explain in narrative)1 (0.5%)

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

Vehicles & Demographics

The top vehicle makes, FORD, CHEV, and CHEVROLET, all saw slight decreases in crash involvement year-over-year. FORD incidents decreased by 4.76% from 42 to 40, while CHEV decreased by 10.81% from 37 to 33. In terms of age distribution among persons involved in crashes, the 26-34 age group experienced a significant 87.04% increase, rising from 54 persons in 2018 to 101 in 2019, while the 16-20 age group decreased by 17.1% from 41 to 34 persons.

Top Vehicle Makes (299 vehicles)

1
FORD40 (13.4%)
↓-4.8%prior 42
2
CHEV33 (11%)
↓-10.8%prior 37
3
CHEVROLET21 (7%)
↓-8.7%prior 23
4
FREIGHTLINER16 (5.4%)
→0.0%prior 16
5
DODGE15 (5%)
↑36.4%prior 11
6
VOLVO13 (4.3%)
↑160.0%prior 5
7
KENWORTH10 (3.3%)
↑25.0%prior 8
8
TOYOTA10 (3.3%)
↑11.1%prior 9
9
GMC9 (3%)
↑28.6%prior 7
10
NISSAN9 (3%)
→0.0%prior 9

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

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

Sex Distribution (277 persons with recorded sex)

Male187 (67.5%)
↑36.5%prior 137
Female90 (32.5%)
↑11.1%prior 81

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: 230
  • Total persons involved: 427
  • Total vehicles involved: 299

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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