Clustered heavy rains （CHRs） defined using hierarchical cluster analysis based on daily observations of precipitation in China during 1960-2008 are investi- gated in this paper. The geographical pattern of CHRs in C...Clustered heavy rains （CHRs） defined using hierarchical cluster analysis based on daily observations of precipitation in China during 1960-2008 are investi- gated in this paper. The geographical pattern of CHRs in China shows three high-frequency centers--South China, the Yangtze River basin, and part of North China around the Bohai Sea. CHRs occur most frequently in South China with a mean annual frequency of 6.8 （a total of 334 times during 1960-2008）. June has the highest monthly frequency （2.2 times/month with a total of 108 times dur- ing 1960-2008）, partly in association with the Meiyu phenomenon in the Yangtze River basin. Within the past 50 years, the frequency of CHRs in China has increased significantly from 13.5 to 17.3 times per year, which is approximately 28%. In the 1990s, the frequency of CHRs often reached 19.1 times per year. The geographical extent of CHR has expanded slightly by 0.5 stations, and its average daily rainfall intensity has increased by 3.7 mm d-1. The contribution of CHRs to total rainfall amount and the frequency of daily precipitation have increased by 63.1% and 22.7%, respectively, partly due to a significant decrease in light rains. In drying regions of North and Northeast China, the amounts of minimal CHRs have had no significant trend in recent years, probably due to warming in these arid regions enhancing atmospheric conveetivity at individual stations.展开更多
The rapid development of location-based social networks（LBSNs） has provided an unprecedented opportunity for better location-based services through point-of-interest（POI） recommendation. POI recommendation is pers...The rapid development of location-based social networks（LBSNs） has provided an unprecedented opportunity for better location-based services through point-of-interest（POI） recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper we propose a textual-geographical-social aware probabilistic matrix factorization method for POI recommendation. Our model is textual-geographical-social aware probabilistic matrix factorization called TGS-PMF, it exploits textual information, geographical information, social information, and incorporates these factors effectively. First, we exploit an aggregated latent Dirichlet allocation（LDA） model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, our exploit probabilistic matrix factorization model（PMF） to integrate the interest, geographical, social relevance scores for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGS-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.展开更多
基金supported by the NationalBasic Research Program of China (Grant No. 2009CB421401)the Chinese Meteorological Administration Program (Grant No.GYHY200906009)
文摘Clustered heavy rains （CHRs） defined using hierarchical cluster analysis based on daily observations of precipitation in China during 1960-2008 are investi- gated in this paper. The geographical pattern of CHRs in China shows three high-frequency centers--South China, the Yangtze River basin, and part of North China around the Bohai Sea. CHRs occur most frequently in South China with a mean annual frequency of 6.8 （a total of 334 times during 1960-2008）. June has the highest monthly frequency （2.2 times/month with a total of 108 times dur- ing 1960-2008）, partly in association with the Meiyu phenomenon in the Yangtze River basin. Within the past 50 years, the frequency of CHRs in China has increased significantly from 13.5 to 17.3 times per year, which is approximately 28%. In the 1990s, the frequency of CHRs often reached 19.1 times per year. The geographical extent of CHR has expanded slightly by 0.5 stations, and its average daily rainfall intensity has increased by 3.7 mm d-1. The contribution of CHRs to total rainfall amount and the frequency of daily precipitation have increased by 63.1% and 22.7%, respectively, partly due to a significant decrease in light rains. In drying regions of North and Northeast China, the amounts of minimal CHRs have had no significant trend in recent years, probably due to warming in these arid regions enhancing atmospheric conveetivity at individual stations.
基金supported by the National Key Project of Scientific and Technical Supporting Programs of China (2014BAH26F00)
文摘The rapid development of location-based social networks（LBSNs） has provided an unprecedented opportunity for better location-based services through point-of-interest（POI） recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper we propose a textual-geographical-social aware probabilistic matrix factorization method for POI recommendation. Our model is textual-geographical-social aware probabilistic matrix factorization called TGS-PMF, it exploits textual information, geographical information, social information, and incorporates these factors effectively. First, we exploit an aggregated latent Dirichlet allocation（LDA） model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, our exploit probabilistic matrix factorization model（PMF） to integrate the interest, geographical, social relevance scores for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGS-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.
