3 research outputs found
GenSyn: A Multi-stage Framework for Generating Synthetic Microdata using Macro Data Sources
Individual-level data (microdata) that characterizes a population, is
essential for studying many real-world problems. However, acquiring such data
is not straightforward due to cost and privacy constraints, and access is often
limited to aggregated data (macro data) sources. In this study, we examine
synthetic data generation as a tool to extrapolate difficult-to-obtain
high-resolution data by combining information from multiple easier-to-obtain
lower-resolution data sources. In particular, we introduce a framework that
uses a combination of univariate and multivariate frequency tables from a given
target geographical location in combination with frequency tables from other
auxiliary locations to generate synthetic microdata for individuals in the
target location. Our method combines the estimation of a dependency graph and
conditional probabilities from the target location with the use of a Gaussian
copula to leverage the available information from the auxiliary locations. We
perform extensive testing on two real-world datasets and demonstrate that our
approach outperforms prior approaches in preserving the overall dependency
structure of the data while also satisfying the constraints defined on the
different variables.Comment: 10 pages, 6 figures, Accepted for the 2022 IEEE International
Conference on Big Dat
Business Analysis of The New Hampshire Grand Hotels
Over the course of the semester Dr. Roxana Wright\u27s Strategic Management class from the Business Department here at PSU has been working on a project that investigates the business side of The New Hampshire Grand Hotels. Much of the research focuses on future outlooks of the industry and covers a variety of different topics such as core competencies, recommendations on business practices, consumer demographics, and even industry characteristics. Formal business reports have been constructed as well as artifacts highlighting key statistics and information. All of this work will be up for display in the Museum of The White Mountains here in Plymouth upon completion of the project. This project is the culmination of 4 years of education across various business majors collaborating on one final project
Exploring county-level spatio-temporal patterns in opioid overdose related emergency department visits.
Opioid overdoses within the United States continue to rise and have been negatively impacting the social and economic status of the country. In order to effectively allocate resources and identify policy solutions to reduce the number of overdoses, it is important to understand the geographical differences in opioid overdose rates and their causes. In this study, we utilized data on emergency department opioid overdose (EDOOD) visits to explore the county-level spatio-temporal distribution of opioid overdose rates within the state of Virginia and their association with aggregate socio-ecological factors. The analyses were performed using a combination of techniques including Moran's I and multilevel modeling. Using data from 2016-2021, we found that Virginia counties had notable differences in their EDOOD visit rates with significant neighborhood-level associations: many counties in the southwestern region were consistently identified as the hotspots (areas with a higher concentration of EDOOD visits) whereas many counties in the northern region were consistently identified as the coldspots (areas with a lower concentration of EDOOD visits). In most Virginia counties, EDOOD visit rates declined from 2017 to 2018. In more recent years (since 2019), the visit rates showed an increasing trend. The multilevel modeling revealed that the change in clinical care factors (i.e., access to care and quality of care) and socio-economic factors (i.e., levels of education, employment, income, family and social support, and community safety) were significantly associated with the change in the EDOOD visit rates. The findings from this study have the potential to assist policymakers in proper resource planning thereby improving health outcomes