Yearly Traffic Safety Analysis

172 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Monona County recorded 172 total crashes, a 5.5% increase from the 163 crashes documented in 2018. While total fatalities and injuries decreased year-over-year, the most significant change was a substantial increase in crashes involving a driver under the influence (DUI), which rose from 4 incidents in 2018 to 13 in 2019.

172

▲ 5.5%was 163

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

57

▼ -10.9%was 64

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 traffic crash volume in Monona County saw a slight increase in 2019, rising to 172 incidents from 163 in the prior year. Despite the increase in total crashes, key severity metrics showed a downward trend. Fatalities decreased from 4 to 2, and total injuries fell from 64 to 57 year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 4↓-50.0%

1

Pedestrians Injured

Prior: 1→0.0%

56

Motorists Injured

Prior: 63↓-11.1%

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 shifted between 2018 and 2019. The day with the highest number of crashes moved from Sunday (29 crashes) in 2018 to Friday (31 crashes) in 2019. Similarly, the peak hour for crashes occurred later in the day, shifting from 4 p.m. in 2018 (13 crashes) to 6 p.m. in 2019 (15 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 showed a mixed picture in 2019 compared to 2018. The fatal crash rate decreased from 1.84 per 100 crashes to 1.16. However, the number of crashes classified as resulting in a 'Serious Injury' increased from 6 in 2018 to 11 in 2019. Crashes resulting in 'No Injury' made up a larger share of the total in 2019 (73.8%) compared to 2018 (69.3%).

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.2%
↓-33.3%prior 3
Serious Injury11serious injury crashes6.4%
↑83.3%prior 6
Minor Injury17minor injury crashes9.9%
↑21.4%prior 14
Possible Injury15possible injury crashes8.7%
↓-44.4%prior 27
No Injury127no injury crashes73.8%
↑12.4%prior 113

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 in Monona County remained consistent year-over-year, with 'Animal' being the primary factor in both 2019 (52 crashes) and 2018 (50 crashes). 'Lost Control' was the second-most cited factor in both periods, increasing from 18 to 20 incidents. A notable change was observed in crashes where the driver 'Ran off road - straight,' which increased from 8 incidents in 2018 to 15 in 2019.

Officer-Reported Primary Contributing Cause

Animal52 (30.2%)↑4.0%prior 50
Lost Control20 (11.6%)↑11.1%prior 18
Ran off road - straight15 (8.7%)↑87.5%prior 8
Driving too fast for conditions15 (8.7%)↑15.4%prior 13
Other (explain in narrative): Other9 (5.2%)↑50.0%prior 6
Operating vehicle in an reckless, erratic, careless, negligent manner9 (5.2%)
Driver Distraction: Other interior distraction8 (4.7%)↑33.3%prior 6
Followed too close6 (3.5%)↑20.0%prior 5
Ran off road - left4 (2.3%)
Other (explain in narrative): No improper action4 (2.3%)

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 environmental conditions during crashes were largely consistent between 2018 and 2019. In both years, the majority of incidents occurred during daylight hours (84 in 2019, 81 in 2018) and in clear weather conditions (94 in 2019, 86 in 2018). Crashes on dry road surfaces were also the most common scenario in both periods, accounting for 93 crashes in 2019 and 89 in 2018, with no significant year-over-year shift in the proportion of crashes occurring in adverse conditions.

Weather

Clear94 (68.1%)
↑9.3%prior 86
Cloudy28 (20.3%)
↓-9.7%prior 31
Snow7 (5.1%)
↑40.0%prior 5
Blowing Snow3 (2.2%)
Fog, smoke, smog3 (2.2%)
Freezing rain/drizzle3 (2.2%)

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

Lighting

Daylight84 (60.4%)
↑3.7%prior 81
Dark - roadway not lighted41 (29.5%)
↑10.8%prior 37
Dawn8 (5.8%)
Dark - roadway lighted5 (3.6%)
↓-37.5%prior 8
Dusk1 (0.7%)
↓-85.7%prior 7

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

Road Surface

Dry93 (67.4%)
↑4.5%prior 89
Ice/frost12 (8.7%)
↑9.1%prior 11
Gravel11 (8.0%)
↑83.3%prior 6
Wet9 (6.5%)
↓-50.0%prior 18
Snow9 (6.5%)
→0.0%prior 9
Slush3 (2.2%)
Other (explain in narrative)1 (0.7%)

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

Vehicles & Demographics

Vehicle and person demographics show some shifts between 2018 and 2019. Combining variations in naming, Chevrolet and Ford remained the top two vehicle makes involved in crashes in both periods, with counts staying relatively stable. Analysis of persons involved in crashes reveals a significant increase in the 55-64 age group, which grew from 22 individuals in 2018 to 50 in 2019. The 21-25 age group also more than doubled, from 20 to 45 persons involved.

Top Vehicle Makes (223 vehicles)

1
FORD38 (17%)
↑2.7%prior 37
2
CHEV38 (17%)
↓-5.0%prior 40
3
CHEVROLET26 (11.7%)
↑30.0%prior 20
4
GMC9 (4%)
→0.0%prior 9
5
DODG7 (3.1%)
↓-22.2%prior 9
6
DODGE5 (2.2%)
↓-44.4%prior 9
7
PETERBILT5 (2.2%)
8
HARLEY DAVID4 (1.8%)
9
BUICK4 (1.8%)
10
HONDA4 (1.8%)

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

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

Sex Distribution (208 persons with recorded sex)

Male128 (61.5%)
↑16.4%prior 110
Female80 (38.5%)
↑42.9%prior 56

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: 172
  • Total persons involved: 323
  • Total vehicles involved: 223

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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