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Maps provide a method of visualizing geographic data in a way that is easy for viewers to understand. In this lesson, we’ll discuss the dataset considerations you show be aware of when creating maps, and create two types of map in our report.
- Your data must have geographic fields in order to create a map
- Latitude and longitude data are preferred, as they are unambiguous
- For other geographical data, the Bing Maps engine will make its best guess of what you are trying to plot
- The engine will identify locations correctly for some data (e.g. US state codes) but can make mistakes with other data (e.g. city names).
- Each location is represented by a bubble
- The size of each bubble is based on a specified field you want to measure
- Bubbles can also be colored using a legend or color saturation
- This is similar to a scatter plot but placed on top of a map
- Each location is represented by a shaded outline
- The outline can be colored using a legend or color saturation
- This is most useful for illustrating countries, states or provinces
Maps provide a method of interpreting geographic data in an easily understandable format.
In this lesson, we'll learn about the mapping functionality available in Power BI.
The first type of map we'll cover is the bubble map.
As you may expect, this is simply a bubble chart overlaid onto a map.
In the visualizations pane, the bubble map is simply called map.
Let's select the map icon and look at the fields we can add.
There are two methods of supplying location data when creating a map. The best method is to add latitude and longitude data.
If your data set includes these fields, you should use them to identify location-based data.
Unfortunately our data set does not include latitude and longitude fields.
In our case, we need to add a geographic field to a location well.
We can add any field to the location well, and that being a maps engine's, we'll make the best guess as to what the field represents. Note that the engine is capable of recognizing states and provinces from many different countries. So this is a good place to start if you're unsure what location field to use. The state field in our data set represents the U.S. state where a sale takes place. The Bing maps engine can recognize U.S. states, so we'll drag the state field to the location well and resize the map.
This correctly creates a map of U.S. states with a dot for each state.
Note that the engine does not work so well in every case. Let's add city to the location well by dragging it above the state field.
Our data points are now spread out all over the world. However, we know all of our sales took place in the U.S.
When given a list of city names, Bing maps makes educated guesses as to where these cities are located.
This could be an issue if two cities in different countries share the same name. As such, we can only guarantee that our maps will work correctly if we use latitude and longitude data. Let's remove city from the location well so that the map only shows states. We'll now format the bubble for each state. We'll drag revenue to the size well, and see that the bubbles resize according to the state's revenue. Next, let's get a sense of the number of users in each state, by adding the user's field to the color saturation well.
Now states with more users have darker bubbles and states with fewer users have lighter bubbles.
Let's move on and look at a field map.
We'll drag the state field onto the canvas, change the visualization tape to field map, and resize the chart.
As we can see, this shades in the outline of each state, instead of giving it a bubble. Note that this type of map does not have a size well, but does have the legend and color saturation options. Let's add the region field to the legend well. This gives us a nice visual indication of the regions that we've been using in this data set. Although our field map is not displaying any numeric data, we can use it to interact with our bubble map. To see this in action, we'll select the Midwest region from the legend.
This highlights the Midwest region in the field map, and filters the bubble map to show only the states in the Midwest. Notice that the color saturation of the bubble map has also changed so that it's now specific to the Midwest region.
As we can see, field maps can be useful filtering tools. If your upwork contains data from a wide geographic area, a field map can helps yours easily focus their analysis. Let's stop the lesson here. As you can see, maps are a powerful visualization tool if your data set has location fields that the Bing maps engine can recognize. The ability to interact with maps and quickly zoom to different areas makes the visual very easy to use and should hold the attention of any audience interested in geographic data.
In the next lesson, we'll look at a few other visualizations, including cards, gauges, and funnel charts.