STEM

Crime rates comparison: US Cities

Recently, I came across two articles, one titled “15 Cheapest, Safest Places To Live in the US” and another titled “12 Cities With Really High Salaries and Really Low Costs of Living”. You can read more about their methodologies on their respective sites if you’re interested. However, these artciles prompted me to ask questions on their overall crime rates (one article had some crime rates information, while the other didn’t), and how they compare to some of the larger cities that were not listed in either of the articles such as: NYC, Seattle, LA, and a few others which I’ll touch on below.

My Methodology: Find I found crimes per 1,000 residents for each of 27 selected cities that were rated as either cheapest and safest, or low cost of living with high salaries. Then I found the national average for that metric, after which I calculated percentage difference from the national average for each city. I then found the population for each. Combining these, I was able to answer the question: How likely is it for a person to fall a victim in each city (such as 1 in every X people is likely to be a victim). With the above metrics, I then calculated the probability in percentage of a person becoming a victim of a crime in a year in each city. Finally I compared that metric also with the national likelihood and probability metrics.

These crimes are overall crimes and includes all types of crimes, scaled to per 1,000 residents.
All numbers are rounded to nearest whole number (except probability metrics). My data sources: GOBankingRates, NeighborhoodScout, Sperling’s Best Places, Bureau of Labor Statistics, fbi.gov, usafacts.org, US Census Bureau, crimegrade.org
Data range: 2021 to 2023

I arranged the data I collected into a table, which looks something like this (a few rows shown as a sample here):

Let’s start with the overall picture across all 27 cities mentioned in the mentioned articles. (TIP: You can use ctrl-mousewheel to zoom in/out when viewing on the browser.)

About the chart: Population (bars) is on a different scale for best fit and is shown above each location’s bar. Red lines show crimes per 1,000 residents by location. Three highest crime rates along with locations called out in chart (along red line). The dotted purple line is the national average of crimes per 1,000 residents. Data is sorted by population in descending order—that means city on the left-most position has the highest population, with decreasing population as we go to the next and so on. That is, the city on the on the right-most position has the lowest population of the group. All numbers are rounded to nearest whole number.

Takeaway: Generally, higher the population, the higher the crime rate. However, in this group of cities, we see relatively low populated cities such as:  Aurora, Olathe, Midland, Carrollton, Pearland, Rochester, League City, Broken Arrow having some of the highest crime rates.

Let’s now look at how these rates compare to national average and how they relate to each other.

About the chart: National Average crimes per 1,000 residents shown as the purple inner circle.
Red lines show crimes per 1,000 residents by location. The farther the red line from the center, the higher the crime rate, and vice versa. All numbers are rounded to nearest whole number.

The previous chart showed the crime rates in relation to the national average and to each other as actual numbers. It’s useful to also look the crimes in terms of percentage differences from the national average to get another angle.

About the chart: National Average crimes per 1,000 residents is the X-axis horizontal line at 0%. Any city bar above the X-axis has that much higher crime rate than the national average (expressed in percentage).  e.g. Raleigh, NC has 22% more crimes per 1,000 residents than that of the national average. Any city bar below the X-axis has that much lower crime rate than the national average (expressed in percentage).  e.g. Rexburg, ID has 83% less crimes per 1,000 residents than that of the national average. Data is sorted by population in descending order—that means city on the left-most position has the highest population, with decreasing population as we go to the next and so on. That is, the city on the on the right-most position has the lowest population of the group. All numbers are rounded to nearest whole number.

Next, let’s look at the likelihood of being a victim. We want to express the crime rate in a way to be able to say: “1 in every x people in this city is likely to be a victim in a year.” Using the table shared above, we can visualize this clearly as in the next chart.

About the chart: The chart above shows the likelihood of a single person being a victim of a crime in each city in a year. This is expressed as 1 in x.  In this chart, the taller the bar or icon stack, the better (because it means less chance of being a victim). The purple checkered area is the national average for this metrics, which is 1 in 43.

Takeaway: We can see that 1 in 35 people is likely to be a victim of a crime in Raleigh, NC in a year; whereas 1 in 294 people is likely to be a victim in Arlington, MA. The safest in this group is Lake in the Hills, IL where likelihood falls to 1 in 323 people. The worst being Midland, TX where 1 in every 17 people are likely to be a victim of crime.

Based on the data gathered and computations so far, let’s take a look at probability. This is a slightly different way of calculating and expressing the probability of a person becoming a victim of a crime.

About the chart: National Average probability of being a victim of a crime in the year is 2% and is shown as purple dotted line. Any probability for a city (bars) above that line yields higher probability than the national average, and vice versa.

Takeaway: Three highest probabilities along with the locations are called out in chart.
e.g. Probability of a person being a victim of a crime in the year in Midland, TX is 5.7%.
Remember, these are selected cities picked for being some of the safest, and offering some of the highest salary and/or lowest cost of living.

So, it’d be interesting to compare these 3 poor-performing cities as per as crimes go with the some of the more populous, even notorious cities of the USA! Next, we’ll do exactly that!

Now we have the selected 3 cities out of 27, and include 6 more much larger cities and see how they compare as far as safety is concerned. That chart is below:

About the chart: National Average crimes per 1,000 residents shown as the purple inner circle.
Red lines show crimes per 1,000 residents by location. The farther the red line from the center, the higher the crime rate, and vice versa. All numbers are rounded to nearest whole number.

Takeaway: Of this selected group of cities, Seattle, WA fares the worst with 115 crimes per 1000 residents. Large portion of that are property crimes. Other high-crime cities are Los Angeles followed by Miami.
The breakdown of overall crimes by violent vs non-violent crimes by city is another factor that may be useful. I will not delve into much deeper here as the numbers vary greatly and seem inconsistent.
However, some recent data suggests regarding violent crimes per 1000 residents show that Miami is at highest (10.6), followed by Seattle (8.1), followed by Los Angeles (6.9).

The probabilities chart with these 9 cities looks like this:

About the chart: National Average probability of being a victim of a crime in the year is 2% and is shown as purple dotted line.
Any probability for a city (bars) above that line yields higher probability than the national average, and vice versa.

Takeaway: Three highest probabilities along with the locations are called out in chart (LA, Seattle, Miami). e.g. Probability of a person being a victim of a crime in the year in Seattle, WA is 11.5% which is now slightly higher than Los Angeles’ 10.7%.

I hope this was informative and easy to understand with the visuals. Thank you for reading!

Disclaimer:
These are statistical measures and do not predict the outcome for any specific individual.
It’s also important to note that we base our calculations on the assumption that the likelihood is the same for all residents, which may not be the case in reality as we know that factors such as age, occupation, lifestyle, etc., can influence an individual’s likelihood of being a victim.
Regarding crimes rates, and violent vs non-violent crimes, the numbers fluctuate quite a bit based on the source and the specific crime classification by different cities and sources.
In conclusion, do not take the numbers in blind faith, rather use them as a reference and focus on the relative metrics to one another and over time. Politics directly impacts local policies and therefore can change the landscape quickly.

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