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Small Multiples for Qualitative Data

A small multiple is a series of miniature, similar graphs displayed together for easy comparison. The term was popularized by Edward Tufte in reference to the display of quantitative information.

However, small multiples can also be used to visualize qualitative data, allowing users to quickly perceive differences in objects or changes over time. I’ll explore two popular forms of visualization in qualitative research: the word cloud and the social network diagram, demonstrating how these methods can benefit from a small multiple approach.

Word Clouds / Word Storms

Word cloud creators, like Wordle and Tagxedo, generate visualizations from text, giving frequently used words greater size and prominence in the swarm. While word clouds are tools for generating high-level summaries of single documents, word storms* are useful for creating comparisons between multiple documents. Presented together, word storms compose a powerful visualization. This display might be used to demonstrate differences or similarities in how disparate groups discuss a given topic or how different news outlets present the same news story.

Social Network Diagram

Social network diagrams are often presented in a pre/post format to show how an intervention strengthened and expanded collaborative ties. The concept of small multiples takes the display to the next level, allowing users to discern differences over time. Here, I use MIT’s Immersion tool** to visualize how people are connected through emails. The small multiples below use metadata from 9 years of my personal emails to visualize my social network as I graduate college, join the workforce, enter a career-field, plow through graduate school, and move across the country.*** Displaying the social network diagrams together in a small multiple formation allows viewers to consider each year individually, while making comparisons.

In conclusion, small multiples are effective for presenting qualitative data as well as quantitative.

* Faculty with the University of Edinburgh are publishing exciting work on word clouds. To read more about their methods, visit: http://groups.inf.ed.ac.uk/cup/wordstorm/wordstorm.html

** Check out a video on MIT’s Immersion program here: http://vimeo.com/69464265

*** Each node represents a person in my “email life” and each tie denotes a piece of communication. The larger the node, the greater the number of emails.

#qualitative data #social network diagram #data visualization #word storms #small multiples

The Kantar Information is Beautiful Awards celebrate excellence and beauty in data visualizations, infographics and information art.

#data visualization #data art

datarep

Public perceptions of US death rates for selected causes: Comparison of public opinion survey estimates versus empirical data shows how people overestimate of rare deaths

superniemie

A simple diagonal line add clarity to this scatter plot.

Data Scraping with IFTTT

IFTTT sounds like “gift” but without the “g”. It stands for “if this, then that.” In logical representation this would be:

A -> B or A ⊃ B

IFTTT allows users to connect a variety of online media through what IFTTT calls “recipes”. For instance one recipe might be:

IF you’re tagged in a photo on facebook (A), IFTTT will THEN download that photo into your dropbox account (B).

The program is a simple, drag-and-drop interface that users can use meaningfully within minutes of signing up (an excellent overview of how the program works can be found here).

IFTTT is useful for data science, because it can be used to automate data collection from online sources. For instance, I’m interested in gauging media coverage of agricultural health and safety issues, and I’m interested in having a list of all the articles on agricultural health and safety issues that appear on, say, NPR. Searching their website each day would be labor intensive and error-prone.

However, IFTTT allows me to connect NPR’s Health & Science Real Simple Syndication (RSS) feed with a Google Drive Spreadsheet. So, every time the search words “agriculture” appears in the feed, a row would be appended to my google spreadsheet with the story’s title, a description, and a link. The spreadsheet automatically updates continually, without me having to lift a finger. I can then sort by date, and trend the data to gauge media coverage of agricultural health and safety issues.

IFTTT makes cumbersome online data collection little bit easier (and mindless). The best part is, it's absolutely free.

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#data scraping #IFTTT #Data collection #data tools #open source

Davis Rumsey presents a map from Francis Amasa Walker's 1874 Statistical Atlas of the United States. The population of the southern states is majority female-- reverberations from the civil war a decade earlier. The westward expansion is also clearly visible, with its "excessively male" populace.

I can't imagine the patience required to produce a visualization like this by hand. A craft both beautiful and rigorous.

#cartography #historical data visualization #map

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Each Friday, I'll feature posts related to graphic design and the visual display of information.

This week, I want to share an inspirational piece of typography art from the Experiential Company. This work for Time Magazine is composed over 12,680 letters from headlines during Obama's first term in office.

As evaluators, we've been subjected to word clouds ad nauseam. Why not take this form of "data art" to the next level of expression?

Check out more work from The Experiential Company on their website: http://theexperiential.com/

#graphic design friday #typography #data art #display of qualitative information

How brilliant is the Place Pulse team for turning a giant data gathering project into a crowdsourced game? I'm really inspired by how they leverage the web to define something so abstract as our perceptions of places. What else could this methodology be applied to? Our perceptions of people, of food products' nutrient value...

Visit the website to contribute to their research or to download their pilot dataset for your own analysis. Here's more on Place Pulse:

Place Pulse aims to quantitatively recognize which areas of a city are perceived as wealthy, modern, safe, lively, active, unique, central, adaptable or family friendly. With enough user participation, Place Pulse can identify which neighborhoods in Bangkok are perceived better than neighborhoods in New York City or to examine how the distribution of a certain perception in Mexico City compares with that same perception in Tokyo. For curious researchers, the Place Pulse dataset can even be used to study the association between urban perception and other datasets, such as violent crime, creativity or economic growth.

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Check out my blog on AEA 365.

Release the Data: Data Scraping Tools for the Computer Programming-Challenged and the Programming Novice

Hi! I’m Mallory Johnson, and I work as an Evaluation Specialist with the UC Davis Schools of Health Evaluation Unit. Have you ever been frustrated by webpages that allow you to view data in a browser, but neglect to offer export or download functions? Government websites in particular have a habit of posting useful evaluation information in useless formats. After all, that table is no help to your evaluation efforts stuck on a computer screen. Data scraping (or screen scraping) allows you to extract data from constrictive webpages, kind of like scratching delicious frosting off a stale cake. This post will introduce time-saving techniques for scraping data off the internet into Microsoft Excel or CSV files.

Rad Resource #1: Google Spreadsheets is a free resource that allows you to import HTML tables into a spreadsheet format.

STEP ONE: Open a Google Spreadsheet:

STEP TWO: locate the URL from the webpage you would like to scrape:

STEP THREE: enter the following formula into the first cell of the Google Spreadsheet.

Formula: =importHTML(“websiteURL”,“table”,“# of table on the webpage”)

Example: =importHTML("http://en.wikipedia.org/wiki/California_counties", "table", 2)

Hot Tip: It may take some trial and error to locate the correct table number (the last portion of the formula). You can review the HTML source code for difficult cases. For Internet Explorer users, navigate to the page, select “view” from the toolbar, and click “source”. Then, count the number of times you see the word “table” in the source-code. This will give you the number of tables within the webpage.

STEP FOUR: Press enter, and the data will magically populate the spreadsheet. Data can be exported as Microsoft Excel, CSV, or text files.

Rad Resource #2: Import.io (http://import.io/) is a new product that allows users to create data scrapers, spiders, and bots with an easy to use “drag and drop” interface. The software is still in beta, but it promises to be a coding-free solution for the programming-challenged.

Rad Resource #3: Beautiful Soup (http://www.crummy.com/software/BeautifulSoup/) is a Python library (http://www.python.org/) designed to make screen scraping projects faster than writing code from scratch. For those with rudimentary programming knowledge, the Beautiful Soup is a scraping, parsing, url-finding wonder. Both the Beautiful Soup and Python are open-source. Tutorials are available from multiple free venues, such as Coursera (www.coursera.org) and Code Academy (www.codeacademy.com).

Lessons Learned: Just because data is “scrape-able” doesn’t mean it’s legal to possess it. Traditional copyright and privacy protection rules apply. Also, these techniques should be avoided on password protected websites-- even when users utilize avatars or usernames to protect their identities (programs exist with the capability to decode them, making personal information frighteningly transparent). Most producers of web-based media monitor their websites for scraping activity. If you wouldn’t want site-owners or users to know that you scraped their data, chances are that you shouldn’t.

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#data scraping #screen scraping #inaccessible data

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