Assignment #7

Visual Distribution Analysis


S&P 500 Distribution of Monthly Returns

Given the current state of the coronavirus and the stock markets, I decided to look at the monthly performance of the SPY since January 1993.

The data was obtained from Yahoo Finance, analyzed in RStudio and then modified in Adobe Illustrator.

The code, pdf, and related files can be obtained from GitHub @ git-me .

Assignment #6

Visual Differences and Deviation Analysis


Florida Counties Relative Health Importance

This is a visualization from the Community Health Status Indicators (CHSI) data set. This particular indicator domain has been filtered for Florida counties and still contains 1474 data points covering 22 unique variables for 66 counties.

With so many data points, the heat map provides a quick way to spot differences and compare across multiple variables. One thing that isn’t apparent by perusing the data set but that the visualization brings out, is the clustering of missing or non-reported data.

The .csv file was obtained from healthdata.gov, analyzed in RStudio and then modified in Adobe Illustrator.

The code, pdf, and related files can be obtained from GitHub @ git-me

Assignment #5

Part to Whole and Ranking Analysis using Plotly


Leading Causes of Death for Florida from 1999 - 2017

The provided dashboard consists of three charts that respectively show causes as a yearly percentage, causes by ratio-linear, and causes by ratio-logarithmic. The logarithmic scale is provided as a means to zoom for two reasons: (1) bunching of data at the lower linear end and (2) more easily observe comparative changes over time.

The interactive dashboard can be found here: Plotly dashboard.

An image of the three dashboard charts is provided below.

Note:

Data retrieved from the Centers for Disease Control and Prevention at https://data.cdc.gov/NCHS/NCHS-Leading-Causes-of-Death-United-States/bi63-dtpu

Assignment #4

Display data using bar graph


Population Size and Density

This visualization shows the relationship between a state's counties' population size and its density. Bar length shows population size and color shows its density. Density is defined as persons per square mile.

For quite a few states, the data shows that size and density are not always related. A shown below, Pinellas County has less than half of Miami-Dade County's population but it is more than twice Miami-Dade's density.

If this visualization was published as a Tableau Dashboard or Story, the user could select other states and view its information.

Assignment #3

Edit previous assignment using Adobe Illustrator


Medicare Hospital Overall Rating

This assignment was an exercise in simplicity or subtlety. Tableau had already done much of the work around the map’s edges i.e. text for the title and legend.

Adobe Illustrator was used to focus the viewer’s attention. In keeping with the traditional color of hospitals signs, dark blue was used for this. I added border, hospital symbol, and dividing lines in the legend.

Assignment # 2

Create a geographic map using Tableau


Medicare Hospital Overall Rating

With over 5000 data points on the map there's a clear issue with over plotting so the circles without fill color seemed most appropriate. The color choice of orange/gold along with varying circle sizes also seemed to best capture the hospital rating scale. The intend is to use the pre-attentive attributes of size and color intensity together. Using other combinations of colors, shapes, and size just seemed to result in a loss of the visual data interpretation.

If this visualization was published as a Tableau Dashboard or Story, the user could zoom in/out for the entire country and get some additional information via the Tooltip (details on demand) feature.

Assignment # 1

An eye-catching visualization from around the Web


This is a Life Expectancy visualization from the Institute for Health Metrics and Evaluation (IHME) at http://www.healthdata.org/.

The visualization landing page is at https://vizhub.healthdata.org/le/

Landing Page

From the landing page’s menu, I selected:

• ‘Life expectancy decomposition’
• sexes as ‘Both’
• sorted by ‘Change in life expectancy’

which then produces the below interactivity.

Life Expectancy Sorted in Ascending Order

I think it’s quite a successful and intuitive visualization – there is no real help or legend necessary to explain it. Granted, I think that most visualizations use something like their own ‘lingo’ and our brain may need a few minutes to adjust and adapt to the presentation. But overall, I think it’s self-explanatory.

I’ll now briefly describe this visualization using some of the verbiage from Keim et. al [1].

Hovering the mouse and clicking on either the bars or the ailments pane reveals insight about which health topics need attention and which ones seem to work well.

Detail for South Africa

The user is free to zoom, filter, and analyze an innumerable amount of combinations and options in order to answers questions and unlock knowledge about certain global health patterns and trends.

This is clearly an information visualization as opposed to a scientific one. The dimensions and variables involved here are numerous and not strictly limited to some specific biological measurement like a platelet count. The data or input involved to provide a global health snapshot must be enormous and its data management must have been carefully thought through.

The model is really hidden from the user, but we perhaps see its interface when we change options and make different selections. I understand that from a broad public health perspective, the triad of linear regression, logistic regression, and survival analysis are heavily used and I’m sure that the IHME uses these under the hood.


Notes
[1] visual-analytics book. https://www.visual-analytics.eu/book/aboutbook/