通过数据挖掘调查气候变化的长期趋势及其因区域和地方环境而产生的空间差异（英文）     

谢一春  张扬  兰海  毛立身  曾寔  陈宇璐 《地理学报(英文版)》2018,(6)

Climate change is a global phenomenon but is modified by regional and local environmental conditions.Moreover,climate change exhibits remarkable cyclical oscillations and disturbances,which often mask and distort the long-term trends of climate change we would like to identify.Inspired by recent advancements in data mining,we experimented with empirical mode decomposition(EMD) technique to extract long-term change trends from climate data.We applied GIS elevation model to construct 3 D EMD trend surface to visualize spatial variations of climate change over regions and biomes.We then computed various time-series similarity measures and plot them to examine spatial patterns across meteorological stations.We conducted a case study in Inner Mongolia based on daily records of precipitation and temperature at 45 meteorological stations from 1959 to 2010.The EMD curves effectively illustrated the long-term trends of climate change.The EMD 3 D surfaces revealed regional variations of climate change,while the EMD similarity plots disclosed cross-station deviations.In brief,the change trends of temperature were significantly different from those of precipitation.Noticeable regional patterns and local disturbances of the changes in both temperature and precipitation were identified.The trends of change were modified by regional and local topographies and land covers.  相似文献   

基于数据场的空间目标行为挖掘(英文)     

王树良  伍爵博  程峰  金红  曾寔 《地球空间信息科学学报》2009,12(3):202-211

The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining. Supported by the National 973 Program of China(No.2006CB701305,No.2007CB310804), the National Natural Science Fundation of China (No.60743001), the Best National Thesis Fundation (No.2005047), the National New Century Excellent Talent Fundation (No.NCET-06-0618).  相似文献