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Understanding individuals’ travel patterns has great impact on practical issues such as traffic control and urban planning. Here, we analyze a 4G dataset of 1000 randomly selected individuals in Shijiazhuang city, China during half month which contains the position information of these cell phone users in each second and GPS logs of 182 volunteers in a period of over five years. We find that the dynamics of locations’ visitations is characterized by bursts, namely the distributions of the relative variation of visitation traffic per day, week display a long tail. On that basis we propose the Exploration and Rank-shift Return model combined the classic Exploration and Preferential Return model with a rank-shift mechanism where every location may move up to a higher ranking position with probability. The model qualitatively recovers the statistical properties observed in the empirical data and reproduces the existence of bursty visitation of locations.

This paper seeks to gain a clearer understanding of Hong Kong's state-led urban renewal, its justification and value effects, with a concentrate on the redevelopment of occupied dwellings into new residences. This study revisits the establishment of the Urban Renewal Authority (URA) and analyzes key differences in how this agency differs from its predecessor. The theoretical analysis shows that the problem of multi-ownership has played a key role in fostering the pattern of urban renewal in Hong Kong. Given this we propose that the political power of resumption (i.e., taking or eminent domain) critically shapes the URA's low-cost advantage in land assembly and forms the basis that carves a niche for state-led redevelopment. This proposition is tested by estimating externalities of transforming Tong Lau—Hong Kong's unique type of historical strata titled housing—to residences fitting to the modern building structure using difference-in-differences techniques. Findings reveal significant and prolonged effects of urban renewal projects on nearby housing prices. This paper implores researchers and policy-makers to pay attention to residential renewal and questions about why a redevelopment project should be led by the public sector.

Video is a rich media widely used in many of our daily life applications like education, entertainment, surveillance, etc. In order to retrieve rapidly, it is necessary to establish digital archive for storing these videos. However, it is not realistic to store vast amounts of video data into digital archive artificially. This paper proposes a new method for the task of video digital archive management by employing scene recognition technology based on extreme learning machine (ELM). This paper only focuses on scene recognition technology which is the key step of digital video archive management. Dense scale invariant feature transform (dense SIFT) features are used as features in this proposed method. The 15-Scenes dataset with more than 4000 images is used. Experimental results have shown that this proposed method achieves not only high recognition accuracy but also extremely low computational cost.

Detecting vehicles in video is a challenging problem owing to the motion of vehicles, the camera and the background and to variations of speed. This paper proposes a classifier based supervised method to detect moving vehicles from a moving camera. Dense scale invariant feature transform (dense SIFT) descriptors are used as features to describe the pattern of the object. And Extreme Learning Machine provides excellent generalization performance at fast speed. Our sample images taken by a camera in helicopter include 2000 images. Experiment results shown that this proposed method has not only good overall performance but also low computational cost.