Abstract. The long-time historical evolution and recent rapid development of Beijing, China, present before us a unique urban structure. A 10-metre spatial resolution SPOT panchromatic image of Beijing has been studied to capture the spatial patterns of the city. Supervised image classifications were performed using statistical and structural texture features produced from the image. Textural features, including eight texture features from the Grey-Level Cooccurrence Matrix (GLCM) method; a computationally efficient texture feature, the Number of Different Grey-levels (NDG); and a structural texture feature, Edge Density (ED), were evaluated. It was found that generally single texture features performed poorly. Classification accuracy increased with increasing number of texture features until three or four texture features were combined. The more texture features in the combination, the smaller difference between different combinations. The results also show that a lower number of texture features were needed for more homogeneous areas. NDG and ED combined with GLCM texture features produced similar results as the same number of GLCM texture features. Two classification schemes were adopted, stratified classification and non-stratified classification. The best stratified classification result was better than the best non-stratified classification result.

Recently there has been increasing interest in the distribution, abundance, and ecology of forage fish populations because of the crucial role they play in nearshore and pelagic ecosystems. However, relatively little is known about the ecology of forage fishes in Alaska. From 1995 to 1997 extensive ecosystem studies were conducted in response to the Exxon Valdez oil spill in 1989. Monthly broadscale aerial surveys were included in those studies for the purpose of determining distribution and abundance of juvenile Pacific herring (Clupea pallasi) and other surface-schooling forage fishes (Brown and Norcross 1997). Many other types of physical and biological data were also available for the same area and dates that could be used for ecological analyses of forage fishes. In order to proceed with hypothesis-driven science, we recognized that basic descriptive life history parameters and the spatial overlap with other ecological parameters needed to be documented first. Therefore, a general research goal

First principles calculations of electronic and optical properties of multiferroic oxide BiFeO3 are used in combination with a plasmonic device model of optical switch to show that a BiFeO3 based device can have much better performance than devices based on existing materials. This arises from the combination of octahedral tilts, ferroelectricity and G‐type antiferromagnetism in BiFeO3 leading to a strong dependence of the optical refractive indices on the orientation with respect to the polarization. A prototype of a plasmonic resonator with an R‐BFO thin film layer is used as an example and shows excellent switch and modulation responses. The proposed approach provides potential opportunities to develop high performance nanophotonic devices for optical communication.

The widespread use of spatio-temporal databases and applications have fuelled an urgent need to discover interesting time and space patterns in such databases. While much work has been done in discovering time/sequence patterns or spatial patterns, discovering of patterns involving both time and space dimensions is still in its infancy.In this paper, we introduce the concept of flow patterns. Flow patterns are intended to describe the change of events over space and time. These flow patterns are useful to the understanding of many real-life applications. We present a disk-based algorithm, FlowMiner, which utilizes temporal relationships and spatial relationships amid events to generate flow patterns. Our performance study shows that FlowMiner is both scalable and efficient. Experiments on real-life datasets also reveal interesting flow patterns.