[OC] Top 10 Safest & Most Dangerous Cities in the United States (2023) Visualization

January 29, 2026
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By Alex Cartwright
[OC] Top 10 Safest & Most Dangerous Cities in the United States (2023) Visualization
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Data Analysis

What This Visualization Shows

This data visualization displays "[OC] Top 10 Safest & Most Dangerous Cities in the United States (2023)" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on The top 10 safest and most dangerous cities in the United States, based on 2023 data.

The original data set used was based on cities with a ***population of 65,000 and above***. There are 640 cities and suburbs that meet this criteria according to the US Census Bureau, however only 518 of these cities and suburbs had crime data available. The "US Average" violent crime rate of 470 is the average of these 518 cites and suburbs.

Some interesting facts:

* Memphis, Tennessee (the most dangerous city) has a violent crime rate **30 times higher** than Irvine, California (the safest city) * 4 states had multiple cities on these lists: California, Wisconsin, Texas, and Utah * Texas and Utah are the only states with two cities on the top 10 safest list * Texas (McAllen is #6 safest and College Station is #8 safest) * Utah (Orem is #7 safest and Provo is #10 safest) * California and Wisconsin are the only states with cities on both lists * California (Irvine is #1 safest, Thousand Oaks is #3 safest, and Oakland is #6 most dangerous) * Wisconsin (Eau Claire is #5 safest and Milwaukee is #8 most dangerous)

What was considered a "city" for this list? I broke this down into three categories: core cities, principal cities, and suburbs. The core city is usually the largest and most important city in the metro area, a [principal city](https://www.census.gov/programs-surveys/metro-micro/about.html#:~:text=The%20largest%20city%20in%20each,cases%20consist%20of%20county%20names) can be other cities in the metro area that are prominent in their own right, and suburbs are other remaining cities. For example, in the Dallas metropolitan area, I broke it down: Dallas (Core City), Fort Worth (Principal City), and Arlington and the rest (Suburb). For these lists, I only counted core and principal cities, *not* suburbs.

These "city types" are all subjective, so some cities (like Irvine, Thousand Oaks, and Orem) may be categorized different than you would categorize them. Here are the 5 next safest core and principal cities, to round out the top 15:

* Ames, Iowa (156.83) * Rochester, Minnesota (162.44) * St. George, Utah (163.87) * Lawrence, Kansas (164.95) * Auburn, Alabama (172.48)

Here are the 5 next most dangerous core and principal cities, to round out the top 15:

* Birmingham, Alabama (1,429.06) * St. Louis, Missouri (1,409.04) * Flint, Michigan (1,376.07) * Lansing, Michigan (1,374.79) * Beaumont, Texas (1,373.90)

These are the top 5 cities with the lowest violent crime rate if you include suburbs (over 65,000 population).

* Castle Rock, Colorado (23.69) * Sammamish, Washington (24.30) * Yorba Linda, California (43.23) * Apex, North Carolina (46.44) * Johns Creek, Georgia (48.51)

Data Sources: This data was sourced from two main locations. First, was the city's police department, if they had data available. If they didn't, I went to the FBI NIBRS report. The data for Anchorage, Detroit, Little Rock, Portland, Oakland, Milwaukee, Irvine, Thousand Oaks, Virginia Beach, and McAllen were from their city's police department. While the data for Memphis, Cleveland, Baltimore, Kansas City, Nashua, Eau Claire, Orem, College Station, Bellevue, and Provo were from the [FBI NIBRS](https://cde.ucr.cjis.gov/LATEST/webapp/#/pages/downloads) report.

Tools Used: [Community Scout](https://community-scout.com/) for research, Excel to clean data, Figma to build infographic, US Map SVG from Wikipedia, which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

This data visualization represents a sophisticated analysis of complex information patterns that provide valuable insights into underlying trends and relationships. Data visualization serves as a bridge between raw numerical data and human understanding, transforming abstract statistics into comprehensible visual narratives.

The power of data visualization lies in its ability to reveal patterns, outliers, and correlations that might not be apparent in traditional tabular formats. Through careful selection of chart types, color schemes, and interactive elements, effective visualizations can communicate complex information quickly and accurately to diverse audiences.

Modern data visualization combines statistical analysis with design principles to create compelling visual stories. This interdisciplinary approach requires understanding both the underlying data and the cognitive processes involved in visual perception. The result is more effective communication of quantitative insights that can inform decision-making and drive positive change.

Data Analysis and Insights

The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.

Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.

The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.

Significance and Applications

This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.

For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.

The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.

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About the Author

Alex Cartwright

Alex Cartwright

Senior Data Visualization Expert

Alex Cartwright is a renowned data visualization specialist and infographic designer with over 15 years of experience in...

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Visualization Details

Published1/29/2026
CategoryData Analysis
TypeVisualization
Views22