华东师范大学地理科学学院邀请康蕾(Emily Lei Kang)教授作了一场题为“Statistical Models for Large Spatial and Spatio-Temporal Datasets(大空间和时空数据集的统计模型)”的讲座。地理科学学院是我国最早具有地理学一级学科博士点授予权的单位之一，是我国首批博士后流动站建站单位之一，也是我国最早2个具有自然地理学重点学科的单位之一。讲座的主要内容是：

　　随着现代技术，如地理信息系统(GIS)和全球定位系统(GPS)常规识别在当今各种学科的地理坐标，科学家和研究人员的发展能够获得地理编码数据以前所未有的，而这样的数据越来越高维在观察位置的数量方面(以及随着时间的推移)。对于非常大的和大规模数据集的空间数据是具有挑战性的，因为数据集的大小导致计算最佳空间预测，如克里格问题。此外，当将数据集收集在大的空间域，感兴趣的关联的空间过程通常表现非平稳行为超过该域，和非平稳空间相关结构的柔性家族优选在统计模型。我先介绍一下统计挑战及其在分析大型或巨型空间和时空数据的发展，然后谈谈一些我已经在这个领域做了近期工作。具体来说，我将讨论(1)预测和降尺度统计方法; (2)进行数据融合的统计方法。这些方法的应用也将被讨论。

　　原文：With the development of modern technologies such as Geographical Information Systems (GIS) and Global Positioning Systems (GPS) routinely identifying geographical coordinates, scientists and researchers in a variety of disciplines today have access to geocoded data as never before, and such data become increasingly high-dimensional in terms of the number of observed locations (and over time). Spatial statistics for very large and massive datasets is challenging, since the size of the dataset causes problems in computing optimal spatial predictors, such as kriging. In addition, when a dataset is collected on a large spatial domain, the associated spatial process of interest typically exhibits nonstationary behavior over that domain, and a flexible family of nonstationary spatial dependence structure is preferred in statistical models. I will first introduce the statistical challenges and their developments in analyzing large or massive spatial and spatio-temporal data, then talk about some recent work I have done in this field. Specifically, I will discuss (1) statistical methods for prediction and downscaling; (2) statistical methods for data fusion. Applications of these methods will also be discussed.