Data is everywhere and usually it’s locked up in excel or simple tables somewhere. You can analyze it with graphs and charts but what happens when you want to see the geographic patterns? Applying your excel data on a map can start to open new views of your data. One way to describe this approach is heat mapping. Let’s explore.
While most datasets can be huge there are a few ways to make sense of the noise. In it’s basic form you could make all of your data geographic in bulk. Say addresses or zipcodes or even counties. You can color and shape the data to try to make sense of an overall pattern. What if you have so much data you can’t easily do that? We recommend standardizing it. Here are some ways.
Let’s say you have 75,000 addresses of customers. Seeing patterns in the individual locations will be difficult. What if you generalize it down to the zipcode level? (Zipcodes are general areas of equal population as determined by the USPS in delivering mail most efficiently) By generalizing your data you get to see the overall pattern of customers much easier. Things to watch out for is overselling density with larger zipcodes. They could oversell the truth and depending on what you are trying to show an additional step in normalizing could be needed. You apply color to each zipcode based on it’s overall value causing a heat mapping effect in drawing your eye to the most important areas.
The simplicity of this approach is what makes heat mapping so valuable. It generalizes your data down to something that’s easy to understand. There are many other types of heat maps out there especially in webmaps. Web versions can re-sample data on the fly giving you an amoeba effect that is always changing. Any way you go it always depends on what you are trying to accomplish. Heat maps can be a good option to have in your toolbox.
Need some Heat Mapping done? Let us know.