Collective Background Extraction for Station Market Area by Using Location Based Social Network

Half centuries of follow-up survey has enabled the architects and urban planners to design rationally by the aid of planning Nonetheless, limitation has occurred at planning because city has been changing its utility in accordance with its users＇ demand. In this paper, the authors proposed a method to analyze trait of users in market areas near stations by analyzing location based social network. After the datum collection from geotagged tweets, these GPS （global positioning system） datum were plotted to map attained from yahoo open location platform. Then the morphological analysis and terminology extraction system extracted the keywords and their scores. After calculating the distance from stations and users＇ GPS coordination, the authors extracted the array of keywords and corresponding scores in some station market area. Lastly, ratios of all users＇ scores and city＇s scores were calculated to examine the locality. Full combination of data collection, natural language processing and visualization enabled the authors to envisage distribution of collective background in city.

Recommending Personalized POIs from Location Based Social Network

Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user＇s check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user＇s check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem.

Analysis of the Key Issues about the Regularized Construction of Hainan National Breeding and Multiplication Base

Based on winter nursery and shuttle breeding, Hainan National Breeding and Multiplication （HNBM）, with off-season breeding and seed production as the essence, has become a national strategic resource. The way of HNBM rising to the national strategy was analyzed through the aspects of technology features, historical characteristics, irreplaceability, function and value hierarchy, and the key problems. As the major achievement of the practice of scientific research and historical selec- tion, HNBM played an irreplaceable role in the climatic conditions, scientific breed- ing, and seed production in disaster relief, protecting the safety of the seed industry and Iocational advantages. Based on the analysis on the functions, values and ex- isted problems of HNBM, the function and value hierarchical structure of HNBM was established through introducing concepts of value engineering, industrial clusters and regional economy. But to fully achieve the industrial clustering development and functional values of HNBM, it needed to solve the key problems existed in top-level planning, experimental bases, system design, hardware construction and ecological security. Some suggestions were put forward in this paper, including setting up key scientific research protection zones, improving the regulations and policies safeguard mechanism and management system of HNBM, drawing up and carrying out the base development planning of HNBM, and striving for financial support from national policies.