The article revisits spatial interaction and distance decay from the perspective of human mobility patterns and spatially-embedded networks based on an empirical data set. We extract nationwide inter-urban movements in China from a check-in data set that covers half a million individuals within 370 cities to analyze the underlying patterns of trips and spatial interactions. By fitting the gravity model, we find that the observed spatial interactions are governed by a power law distance decay effect. The obtained gravity model also closely reproduces the exponential trip displacement distribution. The movement of an individual, however, may not obey the same distance decay effect, leading to an ecological fallacy. We also construct a spatial network where the edge weights denote the interaction strengths. The communities detected from the network are spatially cohesive and roughly consistent with province boundaries. We attribute this pattern to different distance decay parameters between intra-province and inter-province trips.

Most existing human mobility literature focuses on exterior characteristics of movements but neglects activities, the driving force that underlies human movements. In this research, we combine activity-based analysis with a movement-based approach to model the intra-urban human mobility observed from about 15 million check-in records during a yearlong period in Shanghai, China. The proposed model is activity-based and includes two parts: the transition of travel demands during a specific time period and the movement between locations. For the first part, we find the transition probability between activities varies over time, and then we construct a temporal transition probability matrix to represent the transition probability of travel demands during a time interval. For the second part, we suggest that the travel demands can be divided into two classes, locationally mandatory activity (LMA) and locationally stochastic activity (LSA), according to whether the demand is associated with fixed location or not. By judging the combination of predecessor activity type and successor activity type we determine three trip patterns, each associated with a different decay parameter. To validate the model, we adopt the mechanism of an agent-based model and compare the simulated results with the observed pattern from the displacement distance distribution, the spatio-temporal distribution of activities, and the temporal distribution of travel demand transitions. The results show that the simulated patterns fit the observed data well, indicating that these findings open new directions for combining activity-based analysis with a movement-based approach using social media check-in data.

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With the rapid development of Digital Earth System, vector data has been widely used as an important data model. Visualization methods to vector data on a Digital Earth System have got more attention. So far, the existing methods can be divided into geometry-based and texture-based approaches basically, and each kind of method applies to different areas with serious shortages in practice. For instance, the texture-based approaches require high memory and the accuracy of the vector data which is unpleasant when zoom in, while the geometrybased approaches have to increase the number of geometric primitives in order to avoid rendering artifacts. To remedy these deficiencies, M.Schneider, M.Guthe and R.klein proposed a texture-based method in 2005 which creates textures on-the-fly and a perspective reparameterization which is applied taking into account the current point-of-view, and then they presented a geometry-based method in 2007 which is based on the stencil shadow volume algorithm. Both of these methods have been greatly improved compared to the previous methods. In this paper, we analyse these two methods and compare their advantages and disadvantages, then conclude with a discussion of their suitable range in practice and give ideas for future improvement.

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