An integrated space–time framework for linkage discovery of big survey data

In the realm of survey research, establishing connections within large datasets remains a challenge. This study aims to unveil underlying connections within extensive survey data, emphasizing the need for a more integrated approach to decipher intricate relationships among survey elements. Utilizing computational semantics, machine learning, and advanced spatiotemporal models, we developed an all-encompassing database. This novel database is adept at extracting and characterizing features from a multitude of survey studies, spotlighting relationships among metadata elements such as terms, variables, and topics. The derived relationships are systematically stored as connectivity matrices. These matrices not only quantify the degree of interconnectedness among features but also provide insights into their complex interplay. As a result, our system functions akin to a digital geographical data librarian. Beyond merely serving as a storage tool, this system facilitates interdisciplinary research. It equips researchers with the capability to discern connections between survey elements, enabling them to identify the most influential paths among features based on diverse criteria. Such a tool fosters cross-disciplinary integration and unveils potential ties between seemingly unrelated survey attributes, paving the way for breakthroughs in understanding and application.