Big Data and machine learning (ML) technologies have the potential to impact many facets
of environment and water management (EWM). Big Data are information assets …

Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …

This software note announces a new open‐source release of the Maxent software for
modeling species distributions from occurrence records and environmental data, and …

The integrated nested Laplace approximation (INLA) is a recent computational method that
can fit Bayesian models in a fraction of the time required by typical Markov chain Monte …

Human African trypanosomiasis (sleeping sickness) is a parasitic infection that almost
invariably progresses to death unless treated. Human African trypanosomiasis caused …

Modeling spatial and spatio-temporal continuous processes is an important and challenging
problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential …

Coming up with Bayesian models for spatial data is easy, but performing inference with them
can be challenging. Writing fast inference code for a complex spatial model with realistically …

With the advance of methods for estimating species distribution models has come an interest
in how to best combine datasets to improve estimates of species distributions. This has …

油蒿(Artemisia ordosica) 是我国北方农牧交错带的重要固沙植物, 研究其种群格局对理解种群
生态过程和改善流沙治理技术具有重要意义. 点格局分析法是20 世纪末发展起来的多尺度空间 …

In this overview paper, data-driven learning model-based cooperative localization and
location data processing are considered, in line with the emerging machine learning and big …