With Mapline’s radial heat mapping, you’ll find many settings you can configure to view a customized radius circle on a map. This page will explain each of these settings to you.
When you create a radius map, you can “apply the heat” in two different ways: Areas Around Pins or Overlapping Radius Areas. This is set in the “APPLY HEAT TO” drop down option.
By applying heat to the “AREAS AROUND PINS,” you can quickly see high density pin clusters. If more pins are clustered together, then the areas surrounding these pins will be colored red, and areas that don’t have many pins surrounding them will be colored blue. Just set the “SIZE” for your radius, and this will set the distance around each pin that you would like to mask with the heatmap. This image helps show what a simple heatmap looks like when the heat is applied to “AREAS AROUND PINS.”
Below the “Apply Heat To” setting, you’ll find an additional “HEATMAP BASED ON” option. When you set this option to “LOCATION DENSITY,” then you can quickly identify the clusters for your pins (as mentioned above). Or you can change this setting to “DATASET VALUES,” to see high value pins. For example, if you have pins representing customers and each pin has the customer’s annual sales, then you could select the “Annual Sales” column, and the heat areas would show the customers with high annual sales.
You should understand that this type of heatmap shows both magnitude and proximity. For example, if you have one location with high sales, the heat on the radius map would appear red. Or if you could have a lot of lower sales locations which are very close to each other, then the heat would also appear red. It can be a bit tricky, but the heat colors are a combined visualization of both proximity and magnitude. This is the reason that the legend won’t show you a numeric value for “red” or “green.” It is because a green area could mean “a low value pin is in the middle of this area,” or it could mean “the area is farther away from a high value pin.” So, the legend will only show “High” and “Low” meaning “High Value or High Proximity” or “Low Value or Low Proximity.”
By applying heat to the “OVERLAPPING RADIUS AREAS,” you can quickly see high density areas. With this setting, a radius circle on a map will be drawn around each pin. And areas with a lot of overlapping circles are colored red, and areas with fewer overlapping circles are colored blue. This image helps show what a simple heatmap looks like when the heat is applied to “OVERLAPPING RADIUS AREAS.”
The heat spots show the values within a distance of the color pixel. So, unlike the Density Type heatmap, any color on the Coverage Type heatmap only has a single meaning: the value for a spot on a map. Therefore, a legend can be shown that has numeric values.
When you set the “HEATMAP BASED ON” option to “LOCATION DENSITY,” then you can quickly identify the areas on a map which are within your specified distance (“SIZE”) of your pins. If you have a map with customer pins, and you set the size to 5 miles, then areas with the highest number of customers within 5 miles will be red, and areas with only one customer within 5 miles will be blue.
If you set the “HEATMAP BASED ON” option to “DATASET VALUES,” then this will show you the areas on a map which are have the highest total (or average) value within the specified distance (“SIZE”). For example, if your “SIZE” is set to 5 miles, and you select “SUM” and “SALES,” under the “DATASET VALUES,” then the red areas with the highest total sales within 5 miles would be red, and the areas with the lowest total sales within 5 miles would be blue.
The “ADVANCED OPTIONS” enable you to customize your radial heat mapping even more. These options include the ability to set the “OPACITY” of your heatmap as well as define whether you would like the outer edges of the heatmap to “FADE” to transparent or not. You can also set the “BLUR” option to blue the colors together more, and finally you can determine whether you would like the “LEGEND” to appear.
I’m glad you asked! These heatmap options tell you very different things, and here are some basic use-case scenarios: