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Abstract

Much research has been done in the separate fields of microclimate environmental monitoring and data visualization for large datasets, but the two have not been combined. This thesis examines how large, heterogeneous monitoring datasets can be synthesized into hierarchical and actionable information using principals of human-machine interface design and machine learning. Winterthur Museum, Garden, and Library, located in Delaware, collects large amounts of microclimate monitoring data to best preserve its collections, but to date has been limited in how it can analyze and visualize that data. This thesis performs data analysis and visualization for the data collected by Winterthur, enabling better conservation at the museum by improving understanding of microclimate control and promoting prompt action to remedy problems. Bounds and Swing Analysis, Cross-Correlation Analysis, and Factor Analysis are used to analyze the data collected by Winterthur’s sensors, and visualization on floorplans is explored. The outcome of this thesis is a novel workflow for analyzing and visualizing data from Winterthur, implemented with a Python-based set of software that makes the data interpretable by a diverse audience. This thesis also provides a framework for other monitoring situations, such as structural health monitoring, to utilize for better understanding of the many factors involved in any monitoring situation.