This blog post builds upon the previous post that covered an introduction to ArcGIS Maps for Power BI – which can be re-accessed through the following link: https://bit.ly/2PnIgR3
We will start by looking at the editing features that are available within ArcGIS Maps for Power BI.
Jack Dangermond said, “the application of GIS is limited only by the imagination of those who use it.” So, let’s try to get a glimpse of the range of features, with the hope of being inspired and applying them in an imaginative way
Editing an ArcGIS map in PowerBI
Clicking on the Edit of the visualization map would allow you to change various map contents through different tabs and tools
Following is some information on each of the tools:
- Basemap – This lets you choose from four different basic basemaps (unless you are a Plus subscriber or have an ArcGIS account which then gives you access to more options of basemaps). Through such changes, one can ensure that the focus will remain on the data importance rather than caused visual distractions within the map. If different basemaps use different projections, there might be the need to save, close and re-open (or change between report pages) for the map features to reflect the new projection. In such cases, the Attention icon will probably show stating that the basemap will be updated next time visual loads.
- Location Type – This will allow the locations to either show as points or boundaries as can be seen below. It can be specified whether the data pertains to one country or many countries. For the boundaries the location type could be changed from a dropdown list including countries, districts, postcode areas, region etc. (available list changes depending on the country selected). The method to be used in matching locations could vary between:
- Closest match—This can be used when the exact spelling cannot be guaranteed (with errors like Frence), if there might be a mismatch with the spelling in Esri’s data services, or if an exact match search will not add all your data to the map (even if this might not be the best solution to data accuracy). This might obviously lead to inaccuracy and wrong assumptions.
- Exact match—This is the most accurate method and is to be used when boundaries are defined by codes or abbreviations, or when one is sure that the dataset spelling is correct and matches Esri’s data services.
The Location Type might be a good place to start with if a “Failed to locate [x] features error” is encountered
This might be a result of incorrect settings in the location type, like adding a list of states in Brazil without setting the Locations are in one country setting, choosing Brazil and changing Location Type to States. The error might also be a result of an improper match between a value in the dataset and Esri’s data services (or a spelling mistake).
- Map themes – This allows a change in the style for the map and once can choose from location only, heatmaps or clustering (the last two are only available for point layers, that is when you select Points in the Location Type). Through the clustering option, one could group individual location points into larger circular clusters that fall within a cluster radius – giving a high level view and then the ability to drill down into each region. If heatmaps are chosen any values in the Size or Color will be ignored and the tooltips will not be available.
If numerical data exists in the Size and/ or Color field wells, there will be a further 3 map themes available – being Size (showing bubbles with different sizes based on the measure’s value), Color and Size & Color
- Symbol style – This provides the option to do changes in appearance that are immediately reflected in the map including symbol shape and size, colours (and defining the colour ramp), transparency level and classification types. The available options in the Symbol Style screen depends on the map theme selected and the nature of the data being analyzed. For example for heat maps formatting options like Transparency and Area of Influence will be available.Classification types will provide different options to classify the data and defines the way ArcGIS is going to create the clusters from your data, namely:
- Natural Breaks (sometimes referred to as Jenks) – The fluctuated data values are clustered in naturally occurring data categories. Class breaks take place where a gap between clusters exist. This method is suitable for unevenly distributed data. For instance, streets are clustered based on their length (short vs long), or cities based on their size (small vs large).
- Equal Intervals – Value ranges are set equally in size in every category. The entire range of data values (max – min) is divided equally into the number of categories that have been chosen. One will specify the number of classes, and ArcGIS Maps for Power BI will automatically determine how to divide the data.
- Quantile – Classification of data is done within certain number of categories having equal number of units in every category.This might be suitable for evenly distributed data.
- Standard Deviation – Shows how much a feature’s attribute value varies from the mean – whether above or below mean. This might be a good way of pointing out the extremes.
- Manual Breaks – Enables one to define own classes, class breaks and ranges.
As one can see, such features and functions not only offer different options to the creator to give context to the geographic data visualization but will also help the end-user in understanding and adapting to the data better and enhance comparison. I hope the information in this post was useful in some way or another. In the next blog, we will continue building further on this. Stay mapped!