The environment has become increasingly important in recent years as pressures from human impacts and interactions are starting to be realized. This is not an isolated or a local issue. Most indicators suggest that global warming is already showing impacts throughout the world and extreme weather events have become commonplace. Furthermore, the impact of air pollution, such as smog and high PM2.5 measurements, on health and economy is once again a serious problem in developed and developing countries worldwide. In addition, by many accounts we are now in the midst of the sixth great species extinction event in the history of the planet. The processes underlying these challenges are complex, showing spatio-temporal structure, non-Gaussian error distributions, and multivariate interactions. Furthermore, understanding these challenges is both facilitated by and, paradoxically, limited by the increasing volumes of data that are being collected on the environment through remote sensing platforms, complex sensor networks, GPS movement and accelerometer data, and social network information. Indeed, these are ¡§big data!¡¨ Many of the existing statistical methods to address problems with complex data structures require retooling in the presence of such massive data streams.
The goal of the proposed theme topic of
"Big Data in Environmental Studies" is to encourage high-quality research on the theory and methods for analyzing big data related to the environment, and to put those papers in one place to facilitate further research. Both methodological and application papers are welcome.
All submissions will
go through the rigorous review process of the journal in a timely
manner, and interested authors should submit their manuscripts on the
web of the journal. Please mark clearly that the manuscripts are for
the theme topic of "Big Data in Environmental Studies."
The deadline of the
submission is December 31, 2017 and the
targeted publication is January 2019.
Statistica Sinica is
delighted to have the following guest co-editors for this theme topic:
(a) Professor Song-Xi Chen, Iowa State
University and Peking University
(b) Professor Ruey S. Tsay, University
(c) Professor Christopher Wikle,
University of Missouri.