Interactive Exploration of the Employment Situation Report: From Fixed Tables to Dynamic Discovery
August 11, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Peter Mancini, Benjamin Bengfort, Ben Shneiderman
arXiv ID
1608.03569
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
The monthly Bureau of Labor Statistics Employment Situation Report is widely anticipated by economists, journalists, and politicians as it is used to forecast the economic condition of the United States. The report has broad impact on public and corporate economic confidence; however, the online access to this data employs outdated techniques, using a PDF format containing solely text and fixed tabular information. Creating an interactive interface for dynamic discovery on the BLS website could elicit more dialogue between the public and government spheres, drawing more traffic to government websites and triggering greater civic engagement. Our work suggests that the implementation of interactive visual analysis techniques to enable dynamic discovery leads to rapid interpretation of data as well as provides the means to explore the data for further insights. This paper presents two inspirational prototypes: a dashboard of interactive visualizations and an interactive time series explorer, allowing for temporal and spatial analyses and enabling users to combine data sets to create their own customized visualization.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted