A9. Spatial modelling using statistical approaches including modern statistics, geostatistics, machine learning methods

Spatially continuous data play a significant role in planning, risk assessment and decision making in environmental management and conservation. As geographic information systems and modeling techniques are becoming powerful tools in natural resource management and biological conservation, spatially continuous data of environmental variables become increasingly required. They are, however, usually not readily available and often difficult and expensive to acquire, especially for mountainous and deep marine regions. Therefore, spatial modelling techniques are essential for predicting the spatially continuous data of environmental properties. This is a rapidly developing area, including such as development of novel modelling methods/algorithms, new applications of the existing methods, and relevant computational issues. Spatial modellers are encouraged to submit their findings in using various modelling techniques such as modern statistics, geostatistics, machine learning methods, and statistical computing, or their novel modelling methods, with an application case in environmental sciences.