Today almost every data set in practice has got geographical attributes.

In this post, I would like to discuss a way to analyse geography-based data set by quartile partitioning. That is, we partition the data set into 4 equal-size groups on contiguous area.

The example data set will be the US Mass Shootings from 2013-2015. Note that in the analysis, we only focus on the 48 continental states of USA.

1.The Quartile Approach

This is inspired by the widely used boxplot technique. Like boxplot, we will cut a data set into 4 groups of equal size, thus the quartile approach. Boxplot works along a single dimension. We will work with a 2-dimension twist here: longitude and latitude. In 2 dimensions, there are multiple ways to cut the data set into contiguous quartiles, as will explain below.

US mass shootings have been a society problem for quite some years. Gun violence is a problem that is particular in the US where we have the best of technology and thinkers, but we don't know what to do with the guns.

Here is an effort to understand the distribution of mass shooting incidents. Mass shooting is defined as an incident where 4 or more people got shot, whether killed or wounded. The study will lead us to gain certain insights into the pattern of the problem.
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I thought that dimension filtering and data blending are logically permutable. So that's how I drew the last diagram for the order of operations.

Zen Master Jonathan Drummey pointed out that the actual queries for dimension filtering take place before data blending. So here is a new version incorporating Jonathan's contribution, showing the precedence of dimension filters over data blending.

Within each filter categories, there could be subcategories of filters.

We have worked to understand the order of operations in Tableau at a high level.

Actually there are also filtering operations at a lower level. Here we are going to have a look into the order of operations within either of dimension filter and set filter.

A good understanding of the filtering options will let us take advantage of the versatile functionalities of the dimension and set filters. Especially, the filters within are not affected by dimensions in view.
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