Finding All Co-occurrences from Spatiotemporal Data in an Efficient and Scalable Manner
Description
Undergraduate honors thesis / Open Access
Abstract
People are constantly moving around, coming and going from one place to another.
People interact with friends, coworkers, and strangers on a daily basis. This data - that is a
person’s location at a given time - has the potential to tell much about the person’s social
connections. This has broad-ranging applications: it has applications in fields such as
epidemiology, criminology, and marketing. Previous work on computing co-occurrences from
spatiotemporal data to infer social ties has been mostly mathematical and has not focused on
achieving computationally efficient results. In addition, many of the approaches have
relinquished finding every co-occurrence in order to compute things in an efficient and scalable
manner. This work sets out to discuss a scalable and computationally efficient approach to
compute all co-occurrences from a spatiotemporal dataset for the purpose of inferring social ties.
Permanent Link(s)
https://hdl.handle.net/20.500.12202/8248Citation
Polonetsky, L. (2022, April 28). Finding All Co-occurrences from Spatiotemporal Data in an Efficient and Scalable Manner. Undergraduate honors thesis, Yeshiva University.
*This is constructed from limited available data and may be imprecise.
Collections
Item Preview
The following license files are associated with this item: