Aurora Data Visualization Developed in Java-based Processing

Aurora is a 3D visualization of the relationship between people’s level of curiousity for the aurora borealis and the actual insensity of the aurora borealis in the North Pole. The visualization demonstrates book-checkout data from the Seattle Public Library and the solarwind intensity data from NASA from 2006-2014. The raw data sets were processed through SQL and Python Jupyter Notebook and then visualized in a spherical coordinate system to allow 4 dimensions of information (month, year, dewey class, and level of interest/ intensity) to exist simutaneously. Users can scroll through 10 years of data with the GUI element and see the fluctuation between years animated. They can also turn on and off different scales such as month, year, and dewey class with key press interaction.

Oscilla

Sound can be visualized in a number of ways. Different forms of representation are typically used as analytical tools in the context of scientific inquiry. Oscilla is an audio-visual installation that allows the audience to interact with a waveform with their own voice through a microphone, and experience both the acoustic and visual results. The audience is encouraged by the visual feedback from the waveform and the audio feedback from the ring-modulation filter to produce more interesting results with their voice. With more experimenting, the audience can deduce certain patterns hidden in the algorithm of the visual pattern and gain control over them.

// In collaboration with Rodney Duplessis

// Currently Exhibiting at Museum of Sensory and Movement Experiences

Luminaria

This is a LED installation as part of IV Lightworks exhibition. LEDs installed on a bridge in Anisquoio Park, with colors and patters responding to incoming traffic (pedestrians, bikes and skateboards) detected by transducers attached underneath the bridge. Responses are made programmable by four Arduino units

// In collaboration with: David Aleman, Hsin Hsuan Chen, Chris Hoang, Intae Hwang, Xindi Kang, Lu Liu, Wen Liu, Brenda Morales, Andrew Piepenbrink, PJ Powers, Rebecca Prieto, Annika Tan, Leonardo Vargas, Muhammad Hafiz Wan Rosli, Dan Wang, Carmen Wen, Junxiang Yao

// Exhibited June 2016 - June 2017, Anisq'Oyo Park, Isla Vista, CA

GeoD

GeoD visualizes geographical information (locations and entities with geocoded information) contained in topic models. It can be used to analyze locations discussed in the whole corpus underlying a whole model or in a specific topic. The geocoded information that MetadataGeoD maps is gathered from the corpus for a topic model first through a “wikification” process (using the Illinois Wikifier; see L. Ratinov et al., 2011) that confirms the recognition of named entities by checking for correspondence to locations, organizations, etc., for which there are articles in Wikipedia, and secondly through collecting latitude/longitude information for the data. (However, not all possible named entities can be recognized and geocoded as locations in this way.)

// In collaboration with Dan Baciu and Sihwa Park

// Developed for the WE1S Project included in the Topic Model Observatory