Iteration 3 Visualization Requirements

Iteration 3 - Exploring Reporting

Purpose / Preamble

This document captures a set of high level requirements for visualization / reporting from the Hub. We will use these requirements as we explore available modules and tools that could be used to enhance the Hub component. Enhancement is not in scope for this iteration, but the environmental scan is in scope, thus the need for some initial working requirements.

Types of Visualizations

There are many options for visualizations and ways to categorize the approaches. For SCOOP, it will be important to provide a core set of visualizations that are easy to use, as not everyone will be a researcher who uses the system. For more specific and complex visualizations, an export tool should be available. Instead, SCOOP will focus on doing a core set of visualizations that will support the majority of users an an intuitive manner.

For SCOOP we will consider the following visualizations:

• Trending over time of single and multiple variables
• Scatter plot with trend lines of two items (e.g. # of diabetics in a practice and average A1C per practice)
• Scatter plots with 3rd variable represented by size / colour / shape of indicator.
• Comparison of different categories -- bar charts
• Visualization of data quality in an answer
• Venn Diagrams?

Visualization Features

Feature: xx-xx TITLE In order to visualize relative quantities The question manager should be able to Plot multiple results of a question set as a bar graph.

Feature: xx-xx TITLE In order to visualize temporal trends in data The question manager should be able to plot results of a question over time, either from a single query set or from repetitions of a query across the network.

Export Features

Feature: xx-xx TITLE In order to share findings in a report or publication The question manager should be able to Export a visualization as a PDF

Out of Scope Visualization

For the current focus, we will assume that qualitative data visualizations are out of scope. Flowcharts, gantt charts, and other workflow / dependency charts are not in scope. Also, animated trends over time, while they can be powerful, are not needed at this time for SCOOP.

Data fundamentals for constructing visualizations

The following topics will be addressed in the sections that follow:

• Common relationships in quantitative data
• Visual options for encoding quantitative data
• Best and worst practices for representing select data relationships
• An overall process for constructing (or not constructing) appropriate data visualizations

Following the discussion of visualization strategies and tactics, an overview of open source visualization technologies will be provided, complete with a sampling of representative visualizations.

Common relationships in quantitative data

Quantitative data consists of numbers. Quantitative information consists of numbers that have meaningful descriptions. These categorical descriptions come in three flavors:

• Nominal - Categories of data that aren't really related, but instead just described by name (e.g. Doctor A, Doctor B, Doctor C)
• Ordinal - Categories of data that are related in that they follow an implied order, although they aren't really quantified (e.g. underweight, normal weight, overweight)
• Interval - Categories of data are related, ordered and represent quantitative values themselves (e.g. BMI < 15, BMI >= 15 and <25, BMI >=25)

When visualizing data, it is helpful to understand what type of categorical descriptions will be used to illustrate patterns or relationships in the underlying dataset.