Yearly Traffic Safety Analysis

557 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Muscatine County recorded 557 total vehicle crashes, a 1.6% increase from the 548 crashes reported in 2018. While the total number of fatalities remained unchanged at 4, total injuries decreased by 5.1% from 196 to 186. A notable year-over-year change was the 60% rise in crashes involving a driver under the influence (DUI), which increased from 15 in 2018 to 24 in 2019.

557

▲ 1.6%was 548

Total Crash Events

4

Persons Killed

186

▼ -5.1%was 196

Persons Injured

4

▲ 33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 crash trend in Muscatine County was relatively stable from 2018 to 2019, with total crashes increasing by 1.6% from 548 to 557. While the number of fatalities held steady at 4 for both years, the number of individuals injured in these incidents decreased by 5.1%, from 196 in 2018 to 186 in 2019.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

2

Cyclists Killed

Prior: 0→%

2

Motorists Killed

Prior: 3↓-33.3%

4

Pedestrians Injured

Prior: 6↓-33.3%

9

Cyclists Injured

Prior: 1↑800.0%

173

Motorists Injured

Prior: 187↓-7.5%

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 showed some shifts between 2018 and 2019. The peak day for crashes moved from Tuesday (96 incidents) in 2018 to Wednesday (93 incidents) in 2019. The peak hour also shifted, moving from the 5 p.m. hour in 2018 (50 crashes) to the 2 p.m. hour in 2019 (45 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 severity of crashes saw a slight shift toward more serious outcomes in 2019. The number of fatal crashes increased from 3 in 2018 to 4 in 2019, raising their share of total crashes from 0.5% to 0.7%. Crashes resulting in serious injuries also increased in both count (from 9 to 12) and proportion (from 1.6% to 2.2%). Conversely, crashes involving minor injuries decreased from 65 to 55.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.7%
↑33.3%prior 3
Serious Injury12serious injury crashes2.2%
↑33.3%prior 9
Minor Injury55minor injury crashes9.9%
↓-15.4%prior 65
Possible Injury90possible injury crashes16.2%
↑15.4%prior 78
No Injury396no injury crashes71.1%
↑0.8%prior 393

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 an animal remained the leading contributing factor in both periods, though the count of such incidents decreased by nearly 30% from 175 in 2018 to 123 in 2019. The second-leading factor in 2018, 'Followed too close,' saw its count remain stable at 37 incidents in 2019. Conversely, crashes attributed to 'Failure to yield from a stop sign' increased by 31%, from 29 incidents in 2018 to 38 in 2019.

Officer-Reported Primary Contributing Cause

Animal123 (22.1%)↓-29.7%prior 175
Other (explain in narrative): Other44 (7.9%)↑18.9%prior 37
FTYROW: From stop sign38 (6.8%)↑31.0%prior 29
Followed too close37 (6.6%)↓-2.6%prior 38
Lost Control32 (5.7%)↑23.1%prior 26
Ran off road - left31 (5.6%)↑55.0%prior 20
Ran off road - straight25 (4.5%)↑78.6%prior 14
FTYROW: Making left turn22 (3.9%)↓-4.3%prior 23
Driving too fast for conditions14 (2.5%)↓-30.0%prior 20
FTYROW: Other (explain in narrative)12 (2.2%)↑71.4%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

In 2019, a larger proportion of crashes occurred during ideal driving conditions compared to the previous year. Crashes on dry road surfaces increased from representing 54.2% of the total in 2018 to 63.7% in 2019. Similarly, incidents in clear weather grew from representing 50.9% of all crashes to 59.4%. Crashes during daylight hours also saw a proportional increase, accounting for 56.6% of incidents in 2019 versus 46.9% in 2018.

Weather

Clear331 (73.1%)
↑18.6%prior 279
Cloudy57 (12.6%)
↓-16.2%prior 68
Rain24 (5.3%)
→0.0%prior 24
Snow24 (5.3%)
↑71.4%prior 14
Freezing rain/drizzle10 (2.2%)
↓-23.1%prior 13
Blowing Snow4 (0.9%)
Sleet, hail1 (0.2%)
Fog, smoke, smog1 (0.2%)
Blowing sand, soil, dirt1 (0.2%)

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

Lighting

Daylight315 (69.5%)
↑22.6%prior 257
Dark - roadway not lighted70 (15.5%)
↑4.5%prior 67
Dark - roadway lighted53 (11.7%)
↓-13.1%prior 61
Dawn9 (2.0%)
↑28.6%prior 7
Dusk6 (1.3%)
↓-50.0%prior 12

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

Road Surface

Dry355 (78.0%)
↑19.5%prior 297
Wet40 (8.8%)
↓-33.3%prior 60
Snow32 (7.0%)
↑68.4%prior 19
Ice/frost17 (3.7%)
↓-15.0%prior 20
Gravel7 (1.5%)
→0.0%prior 7
Slush4 (0.9%)

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

Vehicles & Demographics

The ranking of top vehicle makes involved in crashes saw some changes. Ford-branded vehicles became the most common in 2019 with 154, up from 135 in 2018, while Chevrolet-branded vehicles (listed as 'CHEV') decreased from 139 to 106. Regarding the age of persons involved in crashes, the 26-34 age group was the largest in both years, with 189 individuals in 2019. The most significant demographic shift was a 70% increase in the count of persons aged 65 and older, which rose from 93 in 2018 to 158 in 2019.

Top Vehicle Makes (896 vehicles)

1
FORD154 (17.2%)
↑14.1%prior 135
2
CHEV106 (11.8%)
↓-23.7%prior 139
3
TOYT76 (8.5%)
↑31.0%prior 58
4
CHEVROLET58 (6.5%)
↑9.4%prior 53
5
DODG50 (5.6%)
↑22.0%prior 41
6
JEEP33 (3.7%)
↑32.0%prior 25
7
GMC30 (3.3%)
↑3.4%prior 29
8
TOYOTA26 (2.9%)
↑23.8%prior 21
9
BUIC26 (2.9%)
↑73.3%prior 15
10
NISS25 (2.8%)
↑92.3%prior 13

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

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

Sex Distribution (801 persons with recorded sex)

Male461 (57.6%)
↑20.7%prior 382
Female340 (42.4%)
↑33.3%prior 255

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: 557
  • Total persons involved: 1,270
  • Total vehicles involved: 896

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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