  A bibliometric methodology for scanning for emerging science and technology
areas is described, where topics in the science, technology and innovation
enterprise are discovered using Latent Dirichlet Allocation, their growth rates
are modeled using first-order rate kinetics, and research specialization of
various entities in these topics is measured using the location quotient.
Multiple interactive visualization interfaces that integrate these results
together to assist human analysts are developed. This methodology is
demonstrated by analyzing the last five years of publications, patents and
grants (~ 14 million documents) showing, for example, that deep learning for
machine vision is the fastest growing area, and that China has a stronger focus
than the U.S. in this area.
