Second Data Update

Lead

The Northwest Territories has seen the largest relative increase in both spousal relationships and women seeking shelter help because of spousal abuse.

Excel Workbook Link and Explanation

In my Excel Workbook, the first section includes a breakdown of the original data I found through the Statistics Canada and CANSIM website. From there, I sorted and moved some of the data so that I could have the "Number of women residents escaping abusive spousal relationships" (Slice1) and the "Rate per 100,000 women in spousal relationships" (Slice2) separated. I wanted to be able to make comparisons within each part of the data before comparing them to each other. According to the footnotes provided with the original data, the "data prior to 2004 may have used different populations to calculate this rate; thus, comparisons should be made with caution," so I removed the data from 2000 and 2002 to be safe.

I then used the percent change formula on both portions of the data to see which province has had the most relative change within the 2004 to 2014 period. This showed that the Northwest Territories had a 100% increase in the number of women seeking shelter and an 86% increase in the number of spousal relationships.

Original Dataset Link

Here is another link to the original dataset I found through the Statistics Canada and CANSIM website.

News Story/Study Link and Brief Summary

I found an article was published by Kate Kyle at the CBC on June 14, 2017 titled "'Women have nowhere to go': New report shows picture of intimate partner violence in N.W.T." This article includes interviews with experts and researchers involved with this report, local RCMP officers, and data that shows how serious this issue is in the Northwest Territories. According to the article, "in 2013 the rate of violence against women in the N.W.T. was nine times the national rate, according to Statistics Canada."

This article also looks at some of the issues the Northwest Territories has in terms of actually providing these services and working with those in abusive relationships. Kyle notes, "eighty percent of communities in the N.W.T. don't have access to victim services; 85 per cent don't have shelters."

First Data Update

1. What dataset will you use for your final report?

The dataset I will be using for my assignment is titled "Transition home survey, one-day snapshot of women residing in shelters because of spousal abuse, Canada, provinces and territories."

2. Describe the dataset. What kind of data does it contain?

The dataset contains data from shelters across the country and focuses specifically on women living at these shelters to escape spousal abuse. Part of the data has been broken down to show the "Number of women residents escaping abusive spousal relationships." The marriage/spousal relationship data for each province is also included and broken down into the "Rate per 100,000 women in spousal relationships." The relationship data includes "women who are married, living in common-law relationships, or are separated or divorced." Information for this dataset is collected through a survey every two years and includes data from 1998 up to 2014.

3. Is there anything about your data that you don’t understand? (i.e. what a column heading means). How will you find this out?

Overall, the data is straightforward and organized in a clear manner. The only aspect I am worried about understanding properly is the math and numbers involved. There are footnotes explaining how everything was calculated so hopefully using those as a reference and doing some more research will help.

4. What are some questions you hope to answer with your data?

Some questions I hope to answer through researching this dataset:

1. Which province has seen the biggest increase in spousal abuse?

2. Which province has seen the biggest decrease in spousal abuse?

3. Which province has seen the biggest increase in spousal relationships?

4. Which province has seen the biggest decrease in spousal relationships?

Data Visualization Analysis of "Hamilton" Lyrical Data

Joel Eastwood and Erik Hinton from The Wall Street Journal analyzed some of the rhyming patterns and lyrics from the recent Broadway hit, "Hamilton." Taking on creator Lin-Manuel Miranda's lyrics was no easy task. As Eastwood and Hinton state in their article, the musical has "some of the most densely packed, complex rhyming lyrics in the history of musicals." They created an algorithm and visualizations to help analyze some of the rhyming patterns and lyrics featured in the musical.

At first, the visualizations weren't exactly clear because I was trying to decipher what the different colour-codes meant and how they were actually analyzing and categorizing the rhymes. Reading the second article about the methodology behind creating the algorithm and the research they did helped clarify some of these things for me. The different lyric maps/visualizations and being able to play the sections of the songs being analyzed also made things clearer. Visually following how the different rhyming patterns play out in the songs was useful for interpreting and understanding the data and patterns.

Some notes on and an example from The Wall Street Journal article/data set.

Illustrated in the image above, there is a section with the different phonetic rhyming families listed with their respective colour-codes. Users can also play the section of the song being analyzed, follow the rhyming pattern(s) as it plays, and match up the rhyming families through the colour-coded squares. Underneath this section, the lyrics are also made available and the rhyming words are colour-coded according to the corresponding rhyming family.

Being that this was a music and lyric-based visualization, having the data mapped out almost like a music sheet worked and was an interesting style choice. I don't think another chart or map style would have worked as well for what Eastwood and Hinton were trying to achieve with this particular data set.

Playing the song sections, clicking on each individual square, and clicking on the lyrics are the main interactive features for this visualization. As previously mentioned, playing sections of the songs being analyzed made things clearer and was a strong visual aid. However, it feels like being able to click on the individual squares or lyrics is not completely necessary or useful. These interactive features were all easy enough to use and decipher, but having all of the different interactive options seems to over-complicate things. Just having the song playing and the squares showing the rhyming patterns could be enough.