Yearly Traffic Safety Analysis

394 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Cedar County recorded 394 total crashes, a 1.3% decrease from the 399 crashes reported in 2018. The most significant year-over-year change was a reduction in traffic fatalities, which fell from 4 in 2018 to 1 in 2019, accompanied by a 13.7% decrease in total injuries.

394

▼ -1.3%was 399

Total Crash Events

1

▼ -75.0%was 4

Persons Killed

88

▼ -13.7%was 102

Persons Injured

1

▼ -75.0%was 4

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 Cedar County remained relatively stable, with a slight decrease of 1.3% from 399 incidents in 2018 to 394 in 2019. However, the severity of these crashes decreased more substantially, as total injuries fell by 13.7% from 102 to 88, and fatalities dropped from 4 to 1 year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 3↓-66.7%

1

Pedestrians Injured

Prior: 3↓-66.7%

1

Cyclists Injured

Prior: 3↓-66.7%

86

Motorists Injured

Prior: 95↓-9.5%

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 timing of crashes shifted between the two periods. In 2019, the peak day for crashes was Thursday with 75 incidents, a change from Monday in 2018 which saw 80 crashes. The peak hour also moved from the afternoon to the morning commute, with 7 a.m. having the highest volume (34 crashes) in 2019, compared to 2 p.m. (27 crashes) in the prior year.

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 decreased from 2018 to 2019. The number of fatal crashes fell from 4 (1.0% of all crashes) to 1 (0.3% of all crashes). While crashes resulting in serious injuries increased slightly from 8 to 9, the number of crashes with possible injuries declined from 30 to 24. Consequently, the share of non-injury crashes rose from 79.4% in 2018 to 81.5% in 2019.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
↓-75.0%prior 4
Serious Injury9serious injury crashes2.3%
↑12.5%prior 8
Minor Injury39minor injury crashes9.9%
↓-2.5%prior 40
Possible Injury24possible injury crashes6.1%
↓-20.0%prior 30
No Injury321no injury crashes81.5%
↑1.3%prior 317

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 were consistent across both years, though their counts shifted. Collisions involving an 'Animal' remained the top factor but decreased in count from 108 in 2018 to 94 in 2019. The second-ranked factor, 'Ran off road - straight,' saw its count increase from 47 to 53 incidents. Crashes attributed to 'Lost Control' decreased from 44 to 36, while incidents involving 'Driving too fast for conditions' increased from 43 to 47.

Officer-Reported Primary Contributing Cause

Animal94 (23.9%)↓-13.0%prior 108
Ran off road - straight53 (13.5%)↑12.8%prior 47
Driving too fast for conditions47 (11.9%)↑9.3%prior 43
Lost Control36 (9.1%)↓-18.2%prior 44
Ran off road - left19 (4.8%)↑26.7%prior 15
Followed too close18 (4.6%)↓-5.3%prior 19
Other (explain in narrative): Other15 (3.8%)↓-6.3%prior 16
FTYROW: From stop sign9 (2.3%)↓-25.0%prior 12
Exceeded authorized speed8 (2%)↑60.0%prior 5
Operating vehicle in an reckless, erratic, careless, negligent manner8 (2%)↓-20.0%prior 10

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 in clear weather conditions increased from 137 in 2018 to 170 in 2019, and collisions on dry roads rose from 160 to 176. Conversely, crashes on snow-covered roads decreased from 51 to 43. The number of crashes occurring in daylight remained stable at 200 in 2019 compared to 197 in the prior year, while crashes in dark, unlighted conditions increased slightly from 72 to 79.

Weather

Clear170 (55.4%)
↑24.1%prior 137
Snow54 (17.6%)
↑8.0%prior 50
Cloudy43 (14.0%)
↓-29.5%prior 61
Rain21 (6.8%)
↑23.5%prior 17
Blowing Snow7 (2.3%)
→0.0%prior 7
Fog, smoke, smog7 (2.3%)
Freezing rain/drizzle4 (1.3%)
↓-80.0%prior 20
Severe Winds1 (0.3%)

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

Lighting

Daylight200 (65.1%)
↑1.5%prior 197
Dark - roadway not lighted79 (25.7%)
↑9.7%prior 72
Dark - roadway lighted12 (3.9%)
↓-7.7%prior 13
Dusk8 (2.6%)
↑14.3%prior 7
Dawn6 (2.0%)
↓-14.3%prior 7
Dark - unknown roadway lighting2 (0.7%)

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

Road Surface

Dry176 (57.3%)
↑10.0%prior 160
Snow43 (14.0%)
↓-15.7%prior 51
Wet41 (13.4%)
↑20.6%prior 34
Ice/frost27 (8.8%)
↓-12.9%prior 31
Gravel17 (5.5%)
↑70.0%prior 10
Slush3 (1.0%)
↓-75.0%prior 12

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford and Chevrolet vehicles being the most frequent in both periods. In 2019, a combined 104 Chevrolet vehicles and 95 Ford vehicles were involved in collisions, compared to 92 Chevrolet and 102 Ford vehicles in 2018. The age distribution of persons involved in crashes showed minimal changes, with the 26-34 and 35-44 age brackets representing the largest shares in both years.

Top Vehicle Makes (568 vehicles)

1
FORD95 (16.7%)
↓-6.9%prior 102
2
CHEV61 (10.7%)
↑17.3%prior 52
3
CHEVROLET43 (7.6%)
↑7.5%prior 40
4
FREIGHTLINER28 (4.9%)
↑40.0%prior 20
5
DODG21 (3.7%)
↑40.0%prior 15
6
GMC19 (3.3%)
↑35.7%prior 14
7
JEEP15 (2.6%)
↑25.0%prior 12
8
NISSAN14 (2.5%)
↑27.3%prior 11
9
HONDA14 (2.5%)
→0.0%prior 14
10
NR13 (2.3%)
↑18.2%prior 11

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

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

Sex Distribution (530 persons with recorded sex)

Male348 (65.7%)
↑18.8%prior 293
Female182 (34.3%)
↑24.7%prior 146

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: 394
  • Total persons involved: 764
  • Total vehicles involved: 568

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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