Projects and research

Featured projects

Example cluster chart
Cluster analysis of persons experiencing homelessness in Maricopa County

Homeless Management Information System 

Michael Simeone, ASU Library

ASU students working with this project learned how to interview stakeholders and refine analytics solutions over multiple iterations. This collaboration with the Knowledge Exchange for Resilience (KER) worked with local partners, Crisis Network and Valley of the Sun United Way to better understand homelessness in Maricopa County. Using data collected by the Homeless Management Information System (HMIS), our team has been working to understand patterns and variations within various groups of persons experiencing stressed and homeless circumstances, as well as prototyping predictive models that can help interventions and targeted assistance. Rapid prototyping and revision for multiple stakeholders was a key skill developed by ASU student participants.


Geo browser for transaction data pertaining to 19th century art markets

Fake News Shelf Life

Shawn Walker, Assistant Professor of Critical Data Studies in the School of Social and Behavioral Sciences

Michael Simeone, ASU Library

Working on research about fake news helps develop skills in data acquisition, cleaning, and sampling. It also teaches responsible methodologies for understanding cultural and social trends at scale. Working with undergraduate and graduate ASU student researchers, this project aims to monitor the lifespan of hyperpartisan content that circulates on Twitter in the period leading up to national elections and democratic consultations. In past research, user-generated, hyperpartisan news content has a remarkably short shelf life, which is a marker of the perishable nature of digital content at the center of political debates in liberal democracies. Key to monitoring efforts is the storage of social media data as it is collected, such as relying on public Twitter Streaming API to track the content tweeted by users associated with key global electoral events (US 2020 Election, Brexit Referendum, etc.). The storage resources for the tweets are parsed for real-time archiving of embedded content including images and URLs embedded to the message, hence identifying and archiving the content of webpages tweeted in the context of electoral politics. From the archived data, conducting an analysis workflow will rely on topic models to contrast extant and extinct URL content tweeted in the period leading up to the vote, and during the analysis, an estimation of the size of the retweet cascade that vanished and probe the relationship between content (i.e., hyperpartisan pieces or outright fake news) and content shelf-life.

The goal is to establish metrics for the lifespan of fake news and user-generated, hyperpartisan news articles. The expectation is that the availability of this content will be short-lived and therefore hypothesize that the news cycle of hyperpartisan news deviates from the regular news cycle, including the dynamics of retweet cascades triggered by legitimate news pieces featured on Twitter.


A time series display as part of a predictive model for estimating component failures in building climate control systems
A time series display as part of a predictive model for estimating component failures in building climate control systems.

Facilities analysis

Michael Simeone, ASU Library
Alex Kohnen, Jennifer Gorney, Clint Lord, ASU Facilities and Maintenance

Teaming with ASU Facilities and Maintenance, ASU students and Unit for Data Science faculty and staff conducted beneficiary discovery, basic research, and analyzed thousands of facilities and maintenance requests submitted at Arizona State University over 5 years. These reports provided a unique view into the operations of the ASU campus, as well as the reliability and performance of green infrastructure at ASU. The final product is a prototype prediction system for breakdowns and recurrent problems, to be site tested with ASU facilities using live data. This project was funded by the Office for Naval Research and emphasizes student expertise, development of science and technology leadership skills, and student veteran engagement.