Normalization is the process of taking a count and dividing it by something else in order to make a number more comparable or to put it in context. When you normalize data you eliminate the units of measurement for data, enabling you to compare data from different places.
TL;DR: Normalize data for easier comparisons or to provide context and to show rates rather than raw numbers.
Here's an example:
Let's say you are comparing the unemployment numbers in different cities. If you are working in Kansas City and trying to understand how your rate of unemployment compares to St. Louis, you would take the Kansas City unemployment number and divide it by the potential working population in the city. That way, instead of showing unemployment in Kansas City as 38,000 people, you get a rate of 3.3% which can be compared to St. Louis’s 52,000 people at 3.6%.
Comparing the total number of people unemployed in each city does not provide the context needed for an accurate comparison. You can see that St. Louis appears to have higher unemployment, but because the city has a larger population, the difference in the unemployment rate is not nearly as large as you would have thought with the non-normalized numbers.
You could also normalize by area to get the density of a variable. You could use the total population of a geography, but maybe you're trying to understand the density of your city. You could divide the total population by land area and get a population density variable. This normalization allows us to show people per acre and gives more context to the population variable.
Normalization in the mySidewalk:
mySidewalk gives you normalization tools right out of the gate, as soon as you select data to accompany your map or chart you can go to Style>>Edit Data Styling>>More Options in order to find the normalization options.
There are often a few options that you’ll see everywhere, including Total Population, Total Households, and Total Area (acres), but sometimes, when the data calls for something a bit more unique, mySidewalk will give you options that we believe will benefit your data normalization process. For example, working with any type of employment numbers will give you the option to normalize by “Working Population 16 and Over” rather than the entire population.