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Big data research directs ICDS co-hire’s discoveries

UNIVERSITY PARK, Pa. — Xi Gong, associate professor of biobehavioral health in the College of Health and Human Development, uses various methodologies to uncover hidden patterns that could impact communities, including environmental health concerns and other complex human and social dynamics. 

Gong, who is also an ICDS co-hire, plans to use ICDS resources to support large-scale geospatial modeling, real-time computation, and streamlining and harvesting of large datasets. Gong’s research interests specifically include geographic information science (GIS), environment and health, big data science and analytics, machine learning and AI, spatio-temporal data mining, and GIS-based modeling. 

ICDS aims to support Gong and other researchers in need of data storage, mathematical and statistical calculations, developing and deploying models, and analyzing large datasets. 

“I try to use the increasing volume of big data and spatio-temporal data to direct my discoveries,” Gong said. “My research involves a lot of different datasets and the models we plan to build are very complicated. Some large-scale projects take a lot of computational power to model; while other projects need to collect and analyze real-time data generated every second. We can combine these innovative methodologies with geographic principles to investigate environmental health-related complexities and human and social dynamics.” 

Gong is currently working on a variety of research projects.  

He is working to address public concerns regarding various environmental risk factors that could affect human health outcomes. This includes air pollution, water contamination, and nuclear radiation. 

One of his projects spotlights wildfire-related smoke exposure and how that is affecting respiratory diseases and cancer outcomes in the southwest United States. 

“Right now, wildfires are very common in the southwest,” Gong said. “During the wildfires, extensive areas were affected by smoke. I use geospatial modeling and integrate location-based terrain and meteorological data into the model to understand how the air pollution dispersed during the wildfires and how to isolate the portion of air pollution contributed by the wildfires.” 

The data generated from this research is used to then gain insights into health outcomes, more specifically, asthma and lung cancer development or mortality. 

In this study, Gong and his team use a neural network – a machine learning model that contains layers of artificial neurons that work similarly to the human brain – to simulate the complex and nonlinear process from air pollution emissions to individual exposure. 

“It involves large volumes of emission, terrain, and meteorological data, and proximity measurements at high spatial and temporal resolutions,” Gong said. “Those datasets are very large in size, and we need to store and process them efficiently within our model, which ICDS can help with.” 

Gong also uses social sensing techniques, spatial-temporal data mining algorithms, and visual analytics methods to discover hidden patterns in social or human behaviors. 

He has used social media big data to investigate how people are aware of and react to certain controversial social events. He also utilized trajectory data to study human migration patterns, crime patterns and sports strategies.  

Gong is currently investigating public perceptions towards child maltreatment based on geotagged social media posts with keywords related to child maltreatment.  

His team will not only analyze the text content of the social media posts, but also examine the location information and community context in order to provide a comprehensive view of public perceptions and the factors that shape them. 

“This is an ongoing project where I am using social media big data and computational approaches to study social dynamics on a large scale,” Gong said. “Various public perceptions of child maltreatment could connect with social demographic, economic, cultural factors, and individual characteristics, and we could find potential reasonings for this.” 

Gong is also an affiliate of the Child Maltreatment Solutions Network (CMSN) and Population Research Institute (PRI) in Social Science Research Institute (SSRI) at Penn State. 

As a co-hire and faculty member at Penn State, Gong is focused on contributing to and engaging more with the broader Penn State research communities within ICDS and beyond. 

“My research spans different disciplines,” Gong said. “ICDS has a really good environment for collaboration. The institute allows people to understand and get to know other people’s work, and to create collaborations. My research in geospatial data science has always been interdisciplinary. I have collaborated and will continue to collaborate with specialists in fields including computer science, statistics, public health, pharmacy, nursing, sports management, public relations and more.” 

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