ℹ️Objective: Use Geo Maps in SAP Analytics Cloud to interpret geo-enriched models and enhance your dashboard’s analytic capabilities. Develop an understanding of the various features available in geo visualizations to customize your insights.
Estimated Time: 20 mins
Exercise Description: You need to do a deeper analysis on BestRun’s regions and understand what drives geographical differences in the company’s performance. You need to leverage geo-spatial analytic capabilities of SAP Analytics Cloud to create new geo maps for your analysis.
Key Features:
- Create your own geo-enriched models with acquired data
- Employ heat maps to gather insights on geo data density
- Understand the use of distance filters and the lasso tool in analyzing your hypotheses
- Use tooltips, labels, and overlapping points to enhance the insights in your geo map
- Change your basemap layer to view your geo enriched data in different contexts
- Style your geo map for clear and informative visualizations
- Click Files
🚩If you are not in the TechEd 2021 folder, follow the path: Public/TechEd 2021
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Select Section 9.0 - Geo Visualizations(Start)
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Click Copy To
4. Click MyFiles
5. Save the file in MyFiles under the name "ANA260_Section 9_Your Initials"
6. Click OK
7. Click MyFiles
8. Open ANA260_Section 9_Your Initials file
9. Click Edit
10. Click Shipping Analysis Page
ℹ️Excercise Check! Does your dashboard look like this screenshot?
🚩In our Shipping Analysis page, we prepared a variety of visualizations that will be used conjunctly with our geo map in analysis. The first chart is a bar graph of sales revenue per region. This chart is linked to our geo map and will filter regions on the geo map based on chart selection.
🚩The second visualization is a table displaying the No of Delivered Orders and Avg Delivery Times for stores in our shipping model. This chart has linked analysis filtering applied from our geo map and will filter entries based on selections made on our geo map.
11. Scroll to the bottom
12. Select the Geo Map already created for you
13. Click Designer to open Builder Panel (in case not already open)
🚩We want to choose a layer that will help us visualize the distribution of our measure, Average Delivery Time, in our geo visualization. The Heat Map Layer is a good way to visualize the data density of a measure on our map. Let us create this layer in our geo map
14. Click Add Layer
15. Rename the layer to Order Delivery Time Density
Let us change the model for this layer to the shipping model which contains our desired measures. You can add layers sourcing measures and dimensions from multiple models on a single geo visualization, enhancing your ability to compare data.
16. Click Select Model
17. Click ANA260_SHIPPING_INFO
18. Click OK
ℹ️SAP Analytics Cloud offers a variety of layers for geographical visualizations. The most common used ones are
Bubble Layer, which shows each location as a bubble. Using color and size of the bubble allows us to see correlation between different measures
Choropleth Layer shows the shape of a geo location and allows drilling from aggregated levels such as country into smaller areas such as regions
Heat Map Layer, allows to show the concentration of geographical locations
For your next step you would like to view your data in a heat map.
19. Click Choose Layer Type
20. Click Heat Map Layer
ℹ️Let us add our geo enriched dimension to this geo map.
21. Click + Add Location Dimension
22. Click Store Location
🚩We want to look at Avg Delivery Time, a calculated measure in our model, to look at the density of delivery times in our heat map layer.
23. Click Add Measure
24. Scroll and click Avg Delivery Time
25. Click OK
🚩Our Heat Map layer looks great, but the scale may be too large for our desired purpose. We can change the gradient properties of our heatmap to improve our visualization and make it easier to discover clusters of outlier delivery times.
26. Click Edit Layer
🚩Let us reduce the blur radius so it is clearerwhere our high and low average delivery times are present in Spain. The blur radius determines the size of each data point and how it overlaps with other values. We can improve the definition between pointsby reducing the blur radius.
27. Click Expand on Avg Delivery Time
28. Change the Blue Radius from 40% to 20%
29. Click OK
🚩Let us move our geo map back to look at delivery times in Spain
30. Click into our Geo Map, hold and drag to be looking at Spain
31. Zoom into the cluster in Spain by scrolling your mouse wheel up.
🚩You can change the appearance of the heat map layer as well by zooming in and out of the geo map and focusing on areas of interest. When we zoom into Spain, we notice there are some distinct clusters of higher average delivery times. Let us investigate this further.
🚩Let us create a new layer that looks at our delivery time measures at a store level. First, let us hide our heat map layer.
32. Click Hide on Order Delivery Time Density
33. Click + Add Layer
🚩We are going to create a layer that shows our delivery measures on a store by store basis to further our analysis
34. Rename the layer to Store Location Analysis
35. Select ANA260_SHIPPING_INFO if not already selected
🚩We want the ability to drill up and down in our layer from a store level to an aggregated country level (i.e.Spain). Let us choose to create a choropleth layer.
36. Click Choose Layer Type
37. Click Chloropleth/ Drill Layer
🚩We can choose to use bubble instead of choropleth in the style of our drill down layer. This will represent our hierarchy members in the form of bubbles on the geo map. Since we want to look at individual stores, this is preferred!
38. Click Choose Style
39. Click Bubble
40. Click + Add Location Dimension
41. Click StoreLocation
42. Click Add Measure/Dimension
🚩Let us add additional measures into our analysis. We can choose to color code our bubbles with the Average # of Delivered Orders to see if this measure is related to our delivery times.
43. Click Average # of Delivered Orders
44. Click + Add Measure
🚩We are choosing average delivery time as our bubble size, so it is easy to pinpoint which stores are outliers in our modeled data.
45. Scroll and Click Avg Delivery Time
🚩Based on our current geo map, the sizing of our bubbles does not suit our analysis of delivery times. We can customize how our measure is translated into bubble size to improve our visualization
46. Click Expand on Avg Delivery Time
🚩Changing the range for our bubbles will help us identify outliers in our geo map based on our measure, Avg Delivery Time. Let us changes these ranges for the hierarchy levels we are interested in: Country, Region, and StoreLocation.
47. Expand Range for Country
48. Change upper range from 100% to 500
49. Click outside the pop-up.
50. Expand Range for Region
51. Change upper range from 100% to 500%
52. Click outside the pop-up.
53. Expand Range for StoreLocation
54. Change upper range from 100% to 500%
55. Click outside the pop-up.
56. Click OK
57. Click Spain
58. Click Drill Down
🚩We can see that the La Rioja region in Spain seems to have high average delivery times (with its large bubble) and high numbers of delivered orders. On the other hand, Castilla Leon has very low average delivery times. This is very informative, and we should look at this layer on an individual store level.
🚩Before we drill down again, let us check to see how our hierarchy is defined.
59. Click Navigate up/down the hierarchy
🚩Let us choose the hierarchy for the current map layer we are looking at.
60. Click Store Location
🚩As we can see, there are 2 additional hierarchy layers, Sub-Region 1 and Sub-Region 2, before we reach our desired StoreLocation level. Let us simplify our hierarchy to two levels, Country and StoreLocation, for efficiency purposes
🚩We can customize which hierarchy levels are drillable in our choropleth layer. This is a performance best practice tip! Rendering additional hierarchy levels that we are not interested in is both time and resource intensive.
61. Click the Hierarchy option for Store Location Analysis
62. Uncheck Show Region
63. Uncheck Show Sub-Region 1
64. Uncheck Show Sub-Region 2
65. Click Save
🚩Let us try drilling into Spain and see if we reach our desired hierarchy level.
66. Click on Spain
67. Click Drill Down
🚩Let us double check to see if our geo map only displays these two hierarchy levels
68. Click Navigate up/down the hierarchy
69. Click StoreLocation
🚩As we can see our hierarchy levels for this layer are properly defined. Let us look at the insights on our StoreLocation hierarchy. Definite Gains Gym seems to be an outlier in high delivery times from the La Rioja region and trainingapparel4sale.com is an outlier in low delivery times from the Castilla Leon region.
70. Right Click on the Geo Map to open Context Menu
71. Click Show/Hide
72. Hover over Store Location Analysis
73. Click Average Delivery Time (Size)
74. Click outside to collapse the context menu
ℹ️We can also display measure values in interactions with the geo map.
75. Click Edit Layer for Store Location Analysis
🚩Let us add a tooltip for our Average # of Delivered Orders measure.
76. Click Add Tooltip
77. Click Tooltip Information
78. Click + Add Measures/Dimensions under Tooltip Information
79. Click Average # of Delivered Orders
80. Click OK
Let us see what the tooltip looks like when we hover over a bubble.
81. Hover over Definite Gains Gym
🚩We want to add a new layer for analysis which will require some data preparation
ℹ️In this next part of the Geo Visualization deep dive we will look at how to create a geo-enriched model. We want to add a geo-enriched model for Shipping Port locations that will be used as the next layer in our geo map analysis.
Please save your story by pressing Ctrl + S on your keyboard.
82. Click Menu
83. Click Modeler to create a new model
🚩We want to start our model based on the Shipping Ports Excel file, which is located in GitHub.
To access the Shipping Ports file, please navigate to our ANA260 GitHubrepository. You can access this at https://github.com/SAP-samples/teched2021-ANA260
84. Click on the exercises folder
85. Open the Resources folder
86. Click Shipping Ports.xlsx
🚩Please download the Excel file onto your computer so you can import it into SAP Analytics Cloud as an acquired model.
- Click Download
🚩
Navigate back to SAP Analytics Cloud
- Click Import From a CSV or Excel File
- Click Select Source File
ℹ️Once the Shipping Ports.xlsx is downloaded, open the Downloads folder on your machineto select the file.
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Select the Shipping Ports.xlsx file
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Click Open
- Click Import
🚩Welcome to Data Modeling in SAP Analytics Cloud!
In this screen, users can assign dimensions and measures, build hierarchies in their data, apply transformations on columns of data, check for and replace wrong data entries, and geo enrich their data. If you are importing a large data set, this screen will show a subset of your data, so it is easier to work with. SAP Analytics Cloud will apply all your requested changes to your entire data set when creating the model.
🚩Let us geo enrich our model so it can be used in our geo visualization. Model creators can choose to geo enrich their data by coordinates (latitude and longitude) or by location name (country and region name). Country data can be imported by ISO3 and ISO2 codes or by Englishnames.
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Click Geo Enrichment
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Click Coordinates
🚩Let us choose an Identifier for our geo enriched Location dimension. This will be the label used for each node in a geo map layer. Let us use the Shipping Port names as the description.
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Click Location Description
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Click Port
- Click Create
🚩Let us now create our model with the geo enriched dimension. You may notice that there area few invalid data points in our geo enriched dimension due to incorrect longitude and latitude data. SAP Analytics Cloud warns model creators about potential errors in the data. The model can still be created and the rows containing faulty data will be excluded from the model. You can choose to replace these invalid data cells directly in the model creation screen.
- Click Create Model
- Click Create
🚩Let us name our model ShippingPorts and save it in our MyFiles directory on the tenant.
- Click OK
Note that some of the rows were rejected due to incorrect locations.
ℹ️After this stage, we can navigate back to our "ANA260_Section 9_Your Initials" story and continue our analysis with our new ShippingPorts model!
- Click Home
ℹ️Since we just used the Geo story before, we can open it via the recently used stories tiles on the homepage.
- Select the last story you worked on (ANA260_Section 9_Your Initials)
- Click Edit
- Click Shipping Analysis
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Select the Geo Map
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Click Designer
🚩Let us add a new layer with our new geo enriched model.
- Click + Add Layer
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Rename Layer to Shipping Port Location
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Click Change Model
🚩Since our model is new to this story, we will have to select it from our files first.
- Click Select other model...
🚩Navigate to where you saved the ShippingPorts model (My Files directory).
- Click ShippingPorts
ℹ️Let us add the geo enriched dimension we created with coordinates
- Click + Add Location Dimension
- Click Location
🚩Let us change the shape of our shipping port bubbles so they are easily distinguished fromour stores.
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Click Expand
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Click Shapes
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Choose the Star shape
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Click OK
🚩We can now choose to add a filter between our two location dimensions, Store Location and Shipping Port Location, to generate additional analysis in our geo visualization
- Click + Add Filter
ℹ️We want to test a hypothesis if distance to shipping ports affect delivery times. We will create a distance filter based on the distance from a shipping port. If a store is further from a shipping port than our defined distance, it will be excluded from our geo map.
🚩Our Show parameter is the dimension that we would like to exclude members based on our distance filter.
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Expand Show Dimension
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Click StoreLocation as the Show Dimension
🚩Let us add an interactive input control slider to our page to dynamically change the distance in distance filter.
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Check Add as an input control slider to page
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Input 1 as Minimum Distance
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Input 6000 as Maximum Distance
🚩Let us choose the Shipping Port locations as our reference location to measure the distance from.
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Click Select a Reference Location
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Click Dimensions
- Choose Location dimension from ShippingPorts model.
- Click OK
🚩Let us resize our input control so it is easier to read and dynamically change.
- Expand the Input Control by dragging on the bottom right corner
🚩Let us start with a medium distance filter.
- Change the distance filter to 275
🚩Let us increase the range of our distance filter.
- Change the distance filter to 500
🚩Let's add Linked Analysis on our Geo Map
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Click More icon
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Click Linked Analysis
- Click Only Selected Widgets
- Select Filter on Data Point Selection
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Scroll and Select Store Region wise Delivery Time and # of Delivered Orders..
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Click Apply
ℹ️Let us filter directly on our two outlier stores for Avg Delivery Time.
- Mouse wheel up in the geo map to zoom in on Spain
ℹ️We want the ability to be able to select many data points on our geo map directly and easily filter on a selected area. Let us use the lasso functionality in geo maps.
- Click the Lasso Tool
- Draw a lasso around Definite Gains Gym and trainingapparel4sale.com
🚩We can now choose to apply a filter on the bubbles selected by our lasso tool.
- Click the Filter Icon
🚩We can also look at our table that is connected to our geo map by linked analysis applied on data point selection. We can see the measures in this table for our two filtered entries.
🚩Since we did not verify our hypothesis that distance to shipping ports is the cause for a higher average delivery time, let us dive into another tool we can use for analysis in our geo map.
- Select our Geo Map and click on Designer
ℹ️We can change the basemap layer in our visualization to better fit the purposes of our analysis. Let us see if there is a geographical reason forthe outliers in delivery time. For now, we want to see if there are any insights that we can gather from the street map view in our geo map
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Click Choose Basemap
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Click OpenStreetMap
🚩First, let us change the font color so it stands out on our base map.
- Click Styling Panel
ℹ️We can change formatting options for all text in the geo map or based on individual layers. We want to select the Store layer to change the color on Store name labels.
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Scroll to Font Options and click Text Selection
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Click Store Location Analysis
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Click Color
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Select Black Palette
🚩Let us now change the Basemap properties to complement our styling change
- Click Builder Panel
🚩We can change the Basemap Opacity,so our layers stand out in our geo map
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Click Expand
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Change Basemap Opacity from 100% to 50%
🚩If we lookat the geo map, a new hypothesis for delivery times can be formed. trainingapparel4sale.comis located directly on a major highway connected to Madrid whereas Definite Gains Gym is secluded and much further from major transit routes. This potentially explains our outliers for average delivery times.
🚩Let us move forward and change our Basemap back to a standard template to try out other geo visualization features.
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Click Choose Basemap
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Click Light Gray
🚩We want to look at a different area in the geo map. Let us move backto general view by removing all our applied filters.
- Click Remove Filters
🚩Let us also drill up to the country hierarchy level.
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Click on a Store Bubble
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Click Drill Up
🚩We can also choose to change our layer options in the Legends tab of the geo map. This enables us to change our geo map display in View Mode without accessing the Designer Panel.
- Click Expand Legends
🚩Let us show the original choropleth layer for sales revenue and hide the other layers we have created. We want to go back to our chart analysis of sales revenue across all regions in our geo map.
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Click Hide Layer for Shipping Port Layer.
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Click to Collapse Legends
🚩Please save your story by pressing Ctrl + S on your keyboard!
ℹ️You have now completed the Geo Visualizations Deep Dive section!
You have completed the entire Geo Visualizations section!
You are now able to:
- Create your own geo-enriched models with acquired data
- Employ heat maps to gather insights on geo data density
- Understand the use of distance filters and the lasso tool in analyzing your hypotheses
- Use tooltips, labels, and overlapping points to enhance the insights in your geo map
- Change your basemap layer to view your geo enriched data in different contexts
- Style your geo map for clear and informative visualizations