On the outskirts of the metropolitan areas in Japan, the rapid development of urban areas and the improvement in transportation networks have brought various land use problems in their wake, including urban diffusion ...On the outskirts of the metropolitan areas in Japan, the rapid development of urban areas and the improvement in transportation networks have brought various land use problems in their wake, including urban diffusion and the phenomenon of urban sprawl. There is a strong need for accurate predictions of land-use change and future urbanization, as well as investigation of the appropriateness of present land use controls and the land use controls that will be required in the future. This study took as its object the outskirts of the Keihanshin (Kyoto-Osaka-Kobe) Metropolitan Area, the second largest conurbation in Japan after the Tokyo Metropolitan Area, and used the digital maps and spatial analysis offered by GIS. It aimed to: 1) describe the characteristics of land use controls, land use and urbanization;2) develop an urbanization prediction model that considers the neighboring relationship of neighboring areas on a 100 m mesh unit;3) apply this model to the study area and verify its validity regarding the conditions of present land use;4) compare urbanization prediction results by this model with the present land use controls;and 5) make predictions for future urbanization and propose remedial measures for future land use controls.展开更多
Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique f...Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.展开更多
文章从邻里尺度出发,检索3个数据库(Web of Science、Pubmed、Scopus)10年来邻里绿地与心理健康的研究性文献,对达到筛选要求的51篇文献的研究方法、研究结果进行统计分析,在梳理城市邻里空间范围、邻里绿地特征、社会背景与心理健康相...文章从邻里尺度出发,检索3个数据库(Web of Science、Pubmed、Scopus)10年来邻里绿地与心理健康的研究性文献,对达到筛选要求的51篇文献的研究方法、研究结果进行统计分析,在梳理城市邻里空间范围、邻里绿地特征、社会背景与心理健康相关性关系的基础上,进一步总结出以居民心理健康为导向的邻里绿地空间规划启示为:对于邻里绿地空间规划,需要综合邻里100~500 m、500~800 m、800~1600 m范围内绿地特征,关注绿道等线性绿地、大型休闲绿地的建设,关注劣势群体聚集区域的绿地规划;对于存量提升的绿地规划,需要优化邻里整体空间的植物结构、丰富植被种类,关注绿地内部品质,尤其重视口袋公园、街道空间的质量提升。未来需要增加纵向研究、绿地量化特征比较研究,以及绿地解释机制关系研究。展开更多
In recent times genetic network analysis has been found to be useful in the study of gene-gene interactions, and the study of gene-gene correlations is a special analysis of the network. There are many methods for thi...In recent times genetic network analysis has been found to be useful in the study of gene-gene interactions, and the study of gene-gene correlations is a special analysis of the network. There are many methods for this goal. Most of the existing methods model the relationship between each gene and the set of genes under study. These methods work well in applications, but there are often issues such as non-uniqueness of solution and/or computational difficulties, and interpretation of results. Here we study this problem from a different point of view: given a measure of pair wise gene-gene relationship, we use the technique of pattern image restoration to infer the optimal network pair wise relationships. In this method, the solution always exists and is unique, and the results are easy to interpret in the global sense and are computationally simple. The regulatory relationships among the genes are inferred according to the principle that neighboring genes tend to share some common features. The network is updated iteratively until convergence, each iteration monotonously reduces entropy and variance of the network, so the limit network represents the clearest picture of the regulatory relationships among the genes provided by the data and recoverable by the model. The method is illustrated with a simulated data and applied to real data sets.展开更多
文摘On the outskirts of the metropolitan areas in Japan, the rapid development of urban areas and the improvement in transportation networks have brought various land use problems in their wake, including urban diffusion and the phenomenon of urban sprawl. There is a strong need for accurate predictions of land-use change and future urbanization, as well as investigation of the appropriateness of present land use controls and the land use controls that will be required in the future. This study took as its object the outskirts of the Keihanshin (Kyoto-Osaka-Kobe) Metropolitan Area, the second largest conurbation in Japan after the Tokyo Metropolitan Area, and used the digital maps and spatial analysis offered by GIS. It aimed to: 1) describe the characteristics of land use controls, land use and urbanization;2) develop an urbanization prediction model that considers the neighboring relationship of neighboring areas on a 100 m mesh unit;3) apply this model to the study area and verify its validity regarding the conditions of present land use;4) compare urbanization prediction results by this model with the present land use controls;and 5) make predictions for future urbanization and propose remedial measures for future land use controls.
基金supported in part by the National Natural Science Foundation of China(61379049,61772120)
文摘Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.
文摘In recent times genetic network analysis has been found to be useful in the study of gene-gene interactions, and the study of gene-gene correlations is a special analysis of the network. There are many methods for this goal. Most of the existing methods model the relationship between each gene and the set of genes under study. These methods work well in applications, but there are often issues such as non-uniqueness of solution and/or computational difficulties, and interpretation of results. Here we study this problem from a different point of view: given a measure of pair wise gene-gene relationship, we use the technique of pattern image restoration to infer the optimal network pair wise relationships. In this method, the solution always exists and is unique, and the results are easy to interpret in the global sense and are computationally simple. The regulatory relationships among the genes are inferred according to the principle that neighboring genes tend to share some common features. The network is updated iteratively until convergence, each iteration monotonously reduces entropy and variance of the network, so the limit network represents the clearest picture of the regulatory relationships among the genes provided by the data and recoverable by the model. The method is illustrated with a simulated data and applied to real data sets.
