The process of urbanization is formed by regular movements of human beings. It yields different functional zones in a city, such as residential zone and commercial zone. Consequently, there exists a close connection b...The process of urbanization is formed by regular movements of human beings. It yields different functional zones in a city, such as residential zone and commercial zone. Consequently, there exists a close connection between the human mobility pattern and the city＇s zones. However, it is not easy to collect large-scale society-wide data that can precisely capture the underlying relations between the individual＇s movement and the regional functions. Hence, our knowledge for understanding the basic patterns of human mobility is still limited. In order to discover the functions of different regions in a city, we propose an affinity based method in this paper. The affinity is a recently introduced metric for measuring the correlation of two connecting node in a complex network. The proposed model groups different functional zones by measuring user＇s arrival/departure distribution via relative entropy. In addition to this, we also identify the intensity of each functional zone by taking kernel density estimation （KDE） method. In the end, some experiments are conducted to evaluate our method with a large-scale real-life dataset, which consists of 3 million cellphone users＇ records from a period of one month. Our findings on the interaction between the mobility pattern and the regional functions can capture the city dynamics efficiently and provide a valuable reference for urban planners.展开更多
近邻传播(AP)聚类算法面临的一个问题是不适用于多重尺度及任意空间形状的数据聚类处理。该文从数据分布特性的表征出发,提出了一种改进的近邻传播聚类算法AP-VSM(Affinity Propagation based on Variable-Similarity Measure)。首先,...近邻传播(AP)聚类算法面临的一个问题是不适用于多重尺度及任意空间形状的数据聚类处理。该文从数据分布特性的表征出发,提出了一种改进的近邻传播聚类算法AP-VSM(Affinity Propagation based on Variable-Similarity Measure)。首先,综合数据的全局与局部分布特性,设计了一种数据可变相似性度量计算方法,该度量可以有效地反映数据实际聚类的分布特性;然后在传统AP算法框架基础上,构造出基于可变相似性度量的近邻传播聚类算法,从而拓展了传统AP算法的数据处理能力。仿真实验验证了新方法性能优于传统AP算法。展开更多
基金supported by the National Nature Science Foundation of China(615111300816147104861273217)
文摘The process of urbanization is formed by regular movements of human beings. It yields different functional zones in a city, such as residential zone and commercial zone. Consequently, there exists a close connection between the human mobility pattern and the city＇s zones. However, it is not easy to collect large-scale society-wide data that can precisely capture the underlying relations between the individual＇s movement and the regional functions. Hence, our knowledge for understanding the basic patterns of human mobility is still limited. In order to discover the functions of different regions in a city, we propose an affinity based method in this paper. The affinity is a recently introduced metric for measuring the correlation of two connecting node in a complex network. The proposed model groups different functional zones by measuring user＇s arrival/departure distribution via relative entropy. In addition to this, we also identify the intensity of each functional zone by taking kernel density estimation （KDE） method. In the end, some experiments are conducted to evaluate our method with a large-scale real-life dataset, which consists of 3 million cellphone users＇ records from a period of one month. Our findings on the interaction between the mobility pattern and the regional functions can capture the city dynamics efficiently and provide a valuable reference for urban planners.
文摘近邻传播(AP)聚类算法面临的一个问题是不适用于多重尺度及任意空间形状的数据聚类处理。该文从数据分布特性的表征出发,提出了一种改进的近邻传播聚类算法AP-VSM(Affinity Propagation based on Variable-Similarity Measure)。首先,综合数据的全局与局部分布特性,设计了一种数据可变相似性度量计算方法,该度量可以有效地反映数据实际聚类的分布特性;然后在传统AP算法框架基础上,构造出基于可变相似性度量的近邻传播聚类算法,从而拓展了传统AP算法的数据处理能力。仿真实验验证了新方法性能优于传统AP算法。
