Social media is valuable in propagating information during disasters for its timely and available characteristics nowadays, and assists in making decisions when tagged with locations. Considering the ambiguity and inaccuracy in some social data, additional authoritative data are needed for important verification. However, current works often fail to leverage both social and authoritative data and, on most occasions, the data are used in disaster analysis after the fact. Moreover, current works organize the data from the perspective of the spatial location, but not from the perspective of the disaster, making it difficult to dynamically analyze the disaster. All of the disaster-related data around the affected locations need to be retrieved. To solve these limitations, this study develops a geo-event-based geospatial information service (GEGIS) framework and proceeded as follows: (1) a geo-event-related ontology was constructed to provide a uniform semantic basis for the system; (2) geo-events and attributes were extracted from the web using a natural language process (NLP) and used in the semantic similarity match of the geospatial resources; and (3) a geospatial information service prototype system was designed and implemented for automatically retrieving and organizing geo-event-related geospatial resources. A case study of a typhoon hazard is analyzed here within the GEGIS and shows that the system would be effective when typhoons occur.
Striation in the ocean is a research frontier in physical oceanography. Interestingly, it has some “sisters and brothers” in Mother Nature, such as the Jovian belts, subtropical jet streams in the atmosphere, and zonal flows in plasma. This meso-scale oceanic phenomenon is, however, concomitant with but covered up by the macro-scale ocean currents or circulations. In order to unveil such zonal jet-like structures, a spatial filtering must be applied to the commonly available time-average data. Previous studies mostly focused on prominent features of striations, such as banded structures, and the generation mechanism; however, the differences revealed by applying different types of filtering methods have not received enough attention. In this paper we present a comprehensive study on the effectiveness of the different detection approaches to unveiling the striations. Three one-dimensional filtering methods: Gaussian smoothing, Hanning and Chebyshev high-pass filtering, are used to analyze SODA data and LICOM model outputs. The first two methods have been used in many previous studies; on the other hand, the Chebyshev filter is a newcomer for this purpose. Our results show that all three methods can reveal ocean banded structures, but the Chebyshev filtering is the best choice. The Gaussian smoothing is not a high pass filter, and it can merely bring regional striations, such as those in the Eastern Pacific, to light. The Hanning high pass filter can introduce a northward shifting of stripes, so it is not as good as the Chebyshev filter. In addition, a cutoff frequency is often needed in applying the high-pass filter, and this frequency depends on the spectrum analysis of the original data. In this paper, we discuss the filtering output and its spatial power spectra of three normalized cutoff-frequencies, 0.1, 0.3 and 0.7. When the cutoff-frequency is too low, the filtering is insufficient; on the other hand, if the cut-off frequency is too high, excessive filtering can happen. Our study shows that for analyzing the global ocean striations, the best normalized cutoff frequency domain is between 0.1 and 0.4. In addition, the bandwidth of striation for using the Chebyshev high pass filter to analyze the SODA data in a depth of 300 m is 150-300 km. In the general case, we propose to use the Chebyshev filter in lieu of Hanning or other methods for investigating ocean striations.
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