5 Powerful Map Layers for Location Analytics

5 Powerful Map Layers for Location Analytics

Location analytics helps you gain insights from geographic components in your business data. By adding maps to your stories you’ll be able to share and consume your data in a whole new way.

In SAP Analytics Cloud, location analytics display in layers on a map. You can use multiple layers on a single map and can interact with these layers in your story.

Start mapping your data

Your model must contain at least one location dimension in order for you to add geo maps to your story. Location dimensions are the combined latitude and longitude columns in your data set. You must define a location dimension to add maps to your story.

Choose from 5 types of map layers:

  • Bubble
  • Heatmap
  • Choropleth
  • Point of Interest
  • Flow

Let’s see how the different layers give us location-based insights of our data. In the example maps below, we are using sales data from a fictional beverage company.

Bubble layer

Bubble layers are useful when you want to take a look at exact locations. Bubbles can communicate information using both their size and color. Identifying trends and anomalies in data is faster with visualization.

On the map below, the bubble color indicates store revenue while bubble size indicates gross margin. The bubble layer shows us that revenue and gross margin is low in Nevada stores and that the best performing store is in California.

Heatmap layer

A heatmap layer shows concentration by combining individual data points to show trends. Typically, the more of a measure there is in the area, the warmer the color.

The bright red on the map below indicates a high quantity of products sold in and around Los Angeles.

Choropleth layer

Choropleth layers display aggregated information as opposed to a localized information. Navigating hierarchies within the choropleth layer gives you more details on a region.

In this example, the color of the region relates to the total gross margin of all regional stores. You can see that the average gross margin in all US stores is good. Go down a level, you can see that California is outperforming both Washington and Nevada. And at the most granular level, you see which regions in California have the highest gross margin.

Point of interest

A point of interest (POI) layer does not depend on measures or dimensions. Import a POI layer to pin relevant location points to your map. For example, a bike rental chain may want to pin the location of local parks. Or a healthcare chain may want to see clinics and hospitals.

You can use a distance filter based on your POI layer to display locations depending on how far they are from a POI.

In the example below, the POI layer is comprised of local universities. An added distance filter means we are only showing stores that are within 10 miles of a university.

Flow layer

Use a flow layer to show connected start and end locations. A flow layer can communicate up to two measures that are dependent on a route. One measure via line color and another measure via line thickness. You need at least two location dimensions in your data set to use a flow layer.

In the map below, the color of the line represents delivery time and the line thickness represents mileage.