Thura Naing, Swasti Dadhich, Mahin Master
Service-Learning Course Project as part of DS 4200 F20: Information Visualization, taught by Prof. Cody Dunne, Data Visualization @ Khoury, Northeastern University.
Professor Hilary Robinson and her team are conducting an interdisciplinary study of the issues arising from the arrival of the algorithmic workplace in the United States. This area is particularly interesting due to “the emergence of digital platforms in the economy that is rapidly transforming economic activity”. The research will develop new evidence about the workers, organizations, and government institutions involved in the ”algorithmic workplace”. We aim to create visualizations for the research team to explain the data to the general public. The data was collected from UberPeople.net which is an online public forum for Uber drivers. By looking at the posts on UberPeople.net the research team hopes to gain insight into the issues that algorithmic workplace employees are facing.
We created a bubble graph through D3. We used a size encoding based on the size of each circle corresponding to the frequency of how many times that word is used in the posts.
We also created a network graph to show the relationships of topics and keywords of all the posts. We hope that this graph will help show how some issues may be connected and how some issues may be disconnected. In solving an issue for one problem, it is important to see the bigger picture and how that problem can affect other areas. There is a positional encoding for the lines that connect the data together. The thickness of the links show how often the links are found between two words. Keywords are represented by green and related words are shown in purple.
We chose to use a line graph to display the frequency of posts on specific keywords over time. The tilt can help explain the slope of how much of an increase or decrease this specific keyword is mentioned over time. This visualization represents time on the x axis and the y axis represents the count which represents frequency of the words in posts. The line graph makes it easier to observe the trend, i.e. to see whether or not the number of those posts increase or decrease. The line graph will be empty and deactivated until the user selects a word in the bubble graph.
Selection is done through the brushing feature that available on the bubble graph, where the user can brush or click over the keyword to select them. By selecting the keywords, the user will link the selection to the nodes on the network graph. The brush links node on the network graph to show the connections of the selected keywords and highlights the node circle in red on the network graph.
Mouse-over features are available on the network and the line graph. Mouse-over on the links show the common links between two nodes. Mouse-over each line will show the keyword that each line represents. This graph is scalable to a larger dataset through minimal adjustments in sizing.
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