Maps in Dossier

A map is a spatial detail which is composed of one or many layers. A layer is the way in which ESRI Maps for MicroStrategy visually represents geographic datasets.  A layer is similar to a legend item on a paper map. For example, on a road map, roads, national parks, political boundaries, and rivers might be considered different layers. When you add MicroStrategy business data to a map, Esri Maps for MicroStrategy creates a layer and displays it on the Contents pane. Once the layer is created, you can configure how it is styled, set its transparency, enable clustering or heat maps, turn on pop-up windows, and so on. 
 
You can add a Map visualization to your dossier, to display your data as map markers on an interactive map. You can use the visualization to:

• Filter the map markers shown in the visualization by selecting map markers for which to display data.
• Display map markers as static images in the visualization, or display the map markers as dynamic bubbles. If you choose to display the map markers as dynamic bubbles, the size of each dynamic bubble is automatically determined based on the value of the metric used to display the map markers, with the largest bubbles being displayed for the largest metric values.

In the image below, you can view different layers used to compare different attributes and metrics on the map.


In the image below, you can view clusters on various data points on the map.


Requirements:

• Attributes: 1 or more
• Metrics: 1 or more
• Geo Attribute: Latitude & Longitude
• Angle: 1 or more metric
• Color By:  1 or more metric

 
Minimum MicroStrategy version: 10.9
Publisher: MicroStrategy 

Installation Instructions:

• To view this dossier with the visualization, download the .mstr file below and deploy it in MicroStrategy Desktop or MicroStrategy Web.
• You can open a working demo by uploading the .mstr file below and doing the following:
• To open the demo visualization in MicroStrategy Desktop, double click the .mstr file.
• To open the demo visualization in MicroStrategy Web, choose Create -> Upload MicroStrategy File -> View Dossier.

Reference: MSTR Community

Dynamic Custom Group in Dashboard/Dossier

You can dynamically cluster attribute elements by using a table with range classification. This table can be imported from Excel file or can be a table in relational database

Let's assume you have a table that clusters your Employees by the Cost brackets. Based on those brackets a Rating is defined (like A, B, C...). This table can be imported from Excel file or from a table in relational database.
Please notice there is a Flag column that contains value '1' for each row. If you don't have this column in your data don't worry! Just use Upper column instead in the formula (see formula of 'Rating Metric' below).


Currently, your Dashboard/Dossier displays Cost per Employee, but it is missing Rating information.


As a first step import your Rating table. Import Rating as an attribute.


Create a metric 'Rating Metric' with the following formula:
Max(IF(((Cost>Lower)And(Cost<Upper)),Flag,0)){~+}


Create a grid that contains Employee, Rating (attribute) and Cost. The data comes from two unrelated data sets so you will get a cross join of data.


Add "Rating Metric" to Filter Pane and set a filter 'Greater than or Equal to" 1. You will see a proper Rating assignment and you don't even need to display 'Rating Metric" on the grid.


This assignment is dynamic, meaning that if you change the Rating brackets in your data set the new Rating rules will be applied automatically (assuming, you are using a live connection to a database).
You may need to change Level of your metrics if there are more attributes in your initial data set.

Dynamic Custom Group in Dashboard/Dossier

You can dynamically cluster attribute elements by using a table with range classification. This table can be imported from Excel file or can be a table in relational database

Let's assume you have a table that clusters your Employees by the Cost brackets. Based on those brackets a Rating is defined (like A, B, C...). This table can be imported from Excel file or from a table in relational database.
Please notice there is a Flag column that contains value '1' for each row. If you don't have this column in your data don't worry! Just use Upper column instead in the formula (see formula of 'Rating Metric' below).


Currently, your Dashboard/Dossier displays Cost per Employee, but it is missing Rating information.


As a first step import your Rating table. Import Rating as an attribute.


Create a metric 'Rating Metric' with the following formula:
Max(IF(((Cost>Lower)And(Cost<Upper)),Flag,0)){~+}


Create a grid that contains Employee, Rating (attribute) and Cost. The data comes from two unrelated data sets so you will get a cross join of data.


Add "Rating Metric" to Filter Pane and set a filter 'Greater than or Equal to" 1. You will see a proper Rating assignment and you don't even need to display 'Rating Metric" on the grid.


This assignment is dynamic, meaning that if you change the Rating brackets in your data set the new Rating rules will be applied automatically (assuming, you are using a live connection to a database).
You may need to change Level of your metrics if there are more attributes in your initial data set.

Change the Tooltip displayed on a Graph

Tooltips are only visible in Web. However, both MicroStrategy Developer and Web allow you to modify the tooltips displayed in a graph by using macros. These macros are dynamic text that is replaced with graph information when the tooltip is displayed in Graph View.

While the default macro used is {&TOOLTIP}, you can use different macros to customize what information appears in a given graph. You can use multiple macros in the same tooltip or combine static text and macros.

Some tooltip macros may not be available for use in specific types of graphs. For example, {&XVALUE} can only be used in Bubble charts or Scatter plots. If a macro is unavailable for the given graph type, no tooltip is displayed in the graph when viewed in Web.

The following table describes the macros you can use to customize graph tooltips in both MicroStrategy Developer and MicroStrategy Web:

Character sort order in MSTR

The sort order of characters in MicroStrategy is different from ASCII standards. The MicroStrategy sort order of characters affects:

•

The order of files and folders in MicroStrategy Developer and MicroStrategy Web

•

The order of ascending and descending sorts of alphanumeric values in MicroStrategy reports

The sort order described below is for English locales only. The sort order may be different in different locales.

The table below lists the non-printable characters which are first in the sort order. All these characters are equal to each other, so the final sorting order is determined by which character is retrieved from the data warehouse first.

ASCII characters that are non-printable characters are listed in the tables below with their character representation. For example, a backspace is represented as BS.

NULL	SOH	STX	ETX	EOT	ENQ	ACK
BEL	BS	SO	SI	DLE	DCL	DC2
DC3	DC4	NAK	SYN	ETB	CANC	EM
SUB	ESC	FS	GS	RS	US	DEL

The table below lists the sort order of characters in MicroStrategy. The order goes from left to right and top to bottom. For example, the top left table cell (TAB) is the beginning of the sort order, the cell to the right of that (LF) is next in order, and the bottom right cell (Z) is the end of the sort order.

TAB	LF	VT	FF	CR		‘	^	_	-
,	;	:	!	?	.	‘	“	(	)
[	]	{	}	@	*	/	\	&	#
%	+	<	=	>	|	~	$	0	1
2	3	4	5	6	7	8	9	a	A
b	B	c	C	d	D	e	E	f	F
g	G	h	H	i	I	j	J	k	K
l	L	m	M	n	N	o	O	p	P
q	Q	r	R	s	S	t	T	u	U
v	V	w	W	x	x	y	Y	z	Z

Cluster analysis in MSTR

Cluster analysis offers a method of grouping data values based on similarities within the data. This technique segments different items into groups depending on the degree of association between items. The degree of association between two objects is maximal if they belong to the same group and minimal otherwise. A specified or determined number of groups, or clusters, is formed, allowing each data value to then be mathematically categorized into the appropriate cluster.

Cluster analysis is seen as an unguided learning technique since there is no target or dependent variable. There are usually underlying features that determine why certain things appear related and others unrelated. Analyzing clusters of related elements can yield meaningful insight into how various elements of a dataset report relate to each other.

MicroStrategy employs the k-Means algorithm for determining clusters. Using this technique, clusters are defined by a center in multidimensional space. The dimensions of this space are determined by the independent variables that characterize each item. Continuous variables are normalized to the range of zero to one (so that no variable dominates). Categorical variables are replaced with a binary indicator variable for each category (1 = an item is in that category, 0 = an item is not). In this fashion, each variable spans a similar range and represents a dimension in this space.

The user specifies the number of clusters, k, and the algorithm determines the coordinates of the center of each cluster.

In MicroStrategy, the number of clusters to be generated from a set of data can be specified by the user or determined by the software. If determined by the software, the number of clusters is based on multiple iterations to reveal the optimal grouping. A maximum is set by the user to limit the number of clusters determined by the software.

The table below shows the result of a model that determines which of five clusters a customer belongs to.

The center of each cluster is represented in the five cluster columns. With this information, it is possible to learn what the model says about each cluster. For example, since the center of Cluster 1 is where “Marital Status = Single” is one and all other marital statuses are zero, single people tend to be in this cluster. On the other hand, there is not a lot of variability across the clusters for the Age Range dimensions, so age is not a significant factor for segmentation.

Creating a cluster model is the first step, because while the algorithm will always generate the proper clusters, it is up to the user to understand how each cluster differs from the others.

Logistic regression in MSTR

Logistic regression is a classification technique used to determine the most likely outcome from a set of finite values. The term logistic comes from the logit function, shown below:

Notice how this function tends to push values to zero or one; the more negative the x-axis value becomes, the logit function approaches a value of zero; the more positive the x-axis value becomes, it approaches a value of one. This is how regression, a technique originally used to calculate results across a wide range of values, can be used to calculate a finite number of possible outcomes.

The technique determines the probability that each outcome will occur. A regression equation is created for each outcome and comparisons are made to select a prediction based on the outcome with the highest likelihood.

Evaluating logistic regression using a confusion matrix

As with other categorical predictors, the success of a logistic regression model can be evaluated using a confusion matrix. The confusion matrix highlights the instances of true positives, true negatives, false positives, and false negatives. For example, a model is used to predict the risk of heart disease as Low, Medium, or High. The confusion matrix would be generated as follows:

Proper analysis of the matrix depends on the predictive situation. In the scenario concerning heart disease risk, outcomes with higher false positives for Medium and High Risk are favored over increased false negatives of High Risk. A false positive in this case encourages preventative measures, whereas a false negative implies good health when, in fact, significant concern exists.