| 1. |
- Huang, Kun, et al.
(författare)
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Enhanced peak growth of global vegetation and its key mechanisms
- 2018
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Ingår i: Nature Ecology and Evolution. - : Springer Science and Business Media LLC. - 2397-334X. ; 2:12, s. 1897-1905
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Tidskriftsartikel (refereegranskat)abstract
- The annual peak growth of vegetation is critical in characterizing the capacity of terrestrial ecosystem productivity and shaping the seasonality of atmospheric CO2 concentrations. The recent greening of global lands suggests an increasing trend of terrestrial vegetation growth, but whether or not the peak growth has been globally enhanced still remains unclear. Here, we use two global datasets of gross primary productivity (GPP) and a satellite-derived Normalized Difference Vegetation Index (NDVI) to characterize recent changes in annual peak vegetation growth (that is, GPPmax and NDVImax). We demonstrate that the peak in the growth of global vegetation has been linearly increasing during the past three decades. About 65% of the NDVImax variation is evenly explained by expanding croplands (21%), rising CO2 (22%) and intensifying nitrogen deposition (22%). The contribution of expanding croplands to the peak growth trend is substantiated by measurements from eddy-flux towers, sun-induced chlorophyll fluorescence and a global database of plant traits, all of which demonstrate that croplands have a higher photosynthetic capacity than other vegetation types. The large contribution of CO2 is also supported by a meta-analysis of 466 manipulative experiments and 15 terrestrial biosphere models. Furthermore, we show that the contribution of GPPmax to the change in annual GPP is less in the tropics than in other regions. These multiple lines of evidence reveal an increasing trend in the peak growth of global vegetation. The findings highlight the important roles of agricultural intensification and atmospheric changes in reshaping the seasonality of global vegetation growth.
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| 2. |
- Lv, Qiujian, et al.
(författare)
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Measuring Geospatial Properties : Relating Online Content Browsing Behaviors to Users’ Points of Interest
- 2018
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Ingår i: Wireless personal communications. - : Springer Science and Business Media LLC. - 0929-6212 .- 1572-834X. ; 101:3, s. 1469-1498
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Tidskriftsartikel (refereegranskat)abstract
- With the growth of the Mobile Internet, people have become active in both the online and offline worlds. Investigating the relationships between users’ online and offline behaviors is critical for personalization and content caching, as well as improving urban planning. Although some studies have measured the spatial properties of online social relationships, there have been few in-depth investigations of the relationships between users’ online content browsing behaviors and their real-life locations. This paper provides the first insight into the geospatial properties of online content browsing behaviors from the perspectives of both geographical regions and individual users. We first analyze the online browsing patterns across geographical regions. Then, a multilayer-network-based model is presented to discover how inter-user distances affect the distributions of users with similar online browsing interests. Drawing upon results from a comprehensive study of users of three popular online content services in a metropolitan city in China, we achieve a broad understanding of the general and specific geospatial properties of users’ various preferences. Specifically, users with similar online browsing interests exhibit, to a large extent, strong geographic correlations, and different services exhibit distinct geospatial properties in terms of their usage patterns. The results of this work can potentially be exploited to improve a vast number of applications.
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| 3. |
- Ma, Zhanyu, et al.
(författare)
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Variational Bayesian Matrix Factorization for Bounded Support Data
- 2015
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 0162-8828 .- 1939-3539. ; 37:4, s. 876-889
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Tidskriftsartikel (refereegranskat)abstract
- A novel Bayesian matrix factorization method for bounded support data is presented. Each entry in the observation matrix is assumed to be beta distributed. As the beta distribution has two parameters, two parameter matrices can be obtained, which matrices contain only nonnegative values. In order to provide low-rank matrix factorization, the nonnegative matrix factorization (NMF) technique is applied. Furthermore, each entry in the factorized matrices, i.e., the basis and excitation matrices, is assigned with gamma prior. Therefore, we name this method as beta-gamma NMF (BG-NMF). Due to the integral expression of the gamma function, estimation of the posterior distribution in the BG-NMF model can not be presented by an analytically tractable solution. With the variational inference framework and the relative convexity property of the log-inverse-beta function, we propose a new lower-bound to approximate the objective function. With this new lower-bound, we derive an analytically tractable solution to approximately calculate the posterior distributions. Each of the approximated posterior distributions is also gamma distributed, which retains the conjugacy of the Bayesian estimation. In addition, a sparse BG-NMF can be obtained by including a sparseness constraint to the gamma prior. Evaluations with synthetic data and real life data demonstrate the good performance of the proposed method.
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| 4. |
- Qiao, Yuanyuan, et al.
(författare)
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A hybrid Markov-based model for human mobility prediction
- 2018
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 278:SI, s. 99-109
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Tidskriftsartikel (refereegranskat)abstract
- Human mobility behavior is far from random, and its indicators follow non-Gaussian distributions. Predicting human mobility has the potential to enhance location-based services, intelligent transportation systems, urban computing, and so forth. In this paper, we focus on improving the prediction accuracy of non-Gaussian mobility data by constructing a hybrid Markov-based model, which takes the non-Gaussian and spatio-temporal characteristics of real human mobility data into account. More specifically, we (1) estimate the order of the Markov chain predictor by adapting it to the length of frequent individual mobility patterns, instead of using a fixed order, (2) consider the time distribution of mobility patterns occurrences when calculating the transition probability for the next location, and (3) employ the prediction results of users with similar trajectories if the recent context has not been previously seen. We have conducted extensive experiments on real human trajectories collected during 21 days from 3474 individuals in an urban Long Term Evolution (LTE) network, and the results demonstrate that the proposed model for non-Gaussian mobility data can help predicting people’s future movements with more than 56% accuracy.
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| 5. |
- Yuanyuan, Qiao, et al.
(författare)
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Understanding Online Shopping and Offline Mobility Behavior in Urban Area from the View of Multilayer Network
- 2016. - 8
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Ingår i: 2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC). - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781509012466 ; , s. 416-421
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Konferensbidrag (refereegranskat)abstract
- The interactive nature of the Internet offers many opportunities to increase the efficiency of online shopping by improving availability of product information, enabling direct multi-attribute comparisons, and reducing buyer search costs. A great body of research focuses on how consumers shop online or why and how online shopping impacts urban development, but the understanding of mutual influence between online and offline behavior of consumers remains somewhat underserved. This paper bridges that gap by quantifying the relationship between consumers' online shopping and offline mobility behavior. The results of the study give insights to further understand human behavior from both a cyber and real world point of view, which may help to place location based targeted advertisements, and plan commercial & retail centers in urban areas.
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| 6. |
- Zhao, Xiaoxing, et al.
(författare)
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Prediction of user app usage behavior from geo-spatial data
- 2016. - 7
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Ingår i: Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data. - New York, NY, USA : ACM. - 9781450343091
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Konferensbidrag (refereegranskat)abstract
- In the era of mobile Internet, a vast amount of geo-spatial data allows us to gain further insights into human activities, which is critical for Internet Services Providers (ISP) to provide better personalized services. With the pervasiveness of mobile Internet, much evidence show that human mobility has heavy impact on app usage behavior. In this paper, we propose a method based on machine learning to predict users' app usage behavior using several features of human mobility extracted from geo-spatial data in mobile Internet traces. The core idea of our method is selecting a set of mobility attributes (e.g. location, travel pattern, and mobility indicators) that have large impact on app usage behavior and inputting them into a classification model. We evaluate our method using real-world network traffic collected by our self-developed high-speed Traffic Monitoring System (TMS). Our prediction method achieves 90.3% accuracy in our experiment, which verifies the strong correlation between human mobility and app usage behavior. Our experimental results uncover a big potential of geo-spatial data extracted from mobile Internet.
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