Urban green space is closely related to the quality of life of residents. However, the traditional approach to its planning often fails to address its actual service capacity and users’ demand. In this study, facilitated by mobile phone location data, more specific features of the spatial distribution of urban residents are identified. Further, population distribution in relation to traffic analysis zones is mapped. On this basis, the two-step floating catchment area method (2SFCA) is adopted in combination with urban green space planning to evaluate the per capita area of green space and its accessibility in practice. Subsequently, classification of per capita area and spatial distribution of green spaces within the study area are obtained; thus, urban districts currently with low accessibility to green areas are identified and can be deemed as key areas for the planning of green areas in the future. The study concludes that mobile phone data can be used to more accurately map the spatial distribution of residents; while, the 2SFCA offers a more comprehensive quantitative measuring of the supply and demand of green spaces. The two combined can be used as an important basis for decision-making in the planning of urban green spaces. Since urban green space can be regarded as a kind of public facility, the methodology of the present study is also believed to be applicable in studies of other types of urban facilities.

As urban sprawl is proven to jeopardize the sustainability system of cities, the identification of urban sprawl is essential for urban studies. Compared with previous related studies which tend to utilize more and more complicated variables to recognize urban sprawl while still retaining an element of uncertainty, this paper instead proposes a simplified model to identify urban sprawl patterns. This is a working theory which is based on a diagram interpretation of the classic urban spatial structure patterns of the Chicago School. The method used in our study is K-means clustering with gridded population density and local spatial entropy. The results and comparison with open population data and mobile phone data verify the assumption and furthermore indicate that the accuracy of source population data will limit the precision of output identification. This article concludes that urban sprawl is mainly dominated by population and surrounding unevenness. Moreover, the Floating Catchment Area (FCA) local spatial entropy method presented in this research brings about an integration of Shannon entropy, Tobler’s first law of geography and the Moore neighborhood, improving the spatial homogeneity and locality of Batty’s Spatial Entropy model which can only be used in a general scope.

With the commercialization of housing and the deepening of urbanization in China, housing prices are having increasing influence on the land market, and thus indirectly affecting urban development. As various spatial features of an urban housing property directly affect its price, the study of this connection has significance for urban planning. The present study uses mainly open internet data of housing prices, supplemented by other data sources, to identify the spatial features of housing prices and the influence factors in a case study city, Wuhan. Methods employed in the study include the hedonic linear regression model, the geographically weighted regression (GWR) model and the artificial neural network (ANN) model, etc. Progress is made in the following two aspects: first, when calculating the influence factors, hierarchical values for accessibility variables of certain public facilities are used instead of simple Euclidean distance and the results shows a better model fit; second, the ANN model shows the best fit in the study, and while the three models all show respective strengths, the combined use of all models offers the possibility of a more comprehensive analysis of the influence factors of housing prices.

The commute of residents in a big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make the fine description of urban residents’ travel behaviors possible, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, we simulated the travel behaviors of commuters: the spatial context of the model is set up using the existing urban road network and by dividing the area into space units. Then, using the mobile phone call detail records of a month, statistics of residents’ travel during the four time slots in working day mornings are acquired and then used to generate the Origin-Destination matrix of travels at different time slots, and the data are imported into the model for simulation. Under the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can induce backward the causes of traffic congestion using the simulation results and the Origin-Destination matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

This study examines the relationship between equity of public green space and urban expansion/sprawl under high-speed urbanization. Equity of urban public green space indicates the degree to which urban public green spaces are distributed spatially in an equal way, with regard to the spatial variation of residents’ “need” for green space. In emerging economies such as China, especially in developing or underdeveloped cities such as Wuhan, central China, rapid urban growth challenges the capacity of the state to provide infrastructure and services for its urbanites equally. In order to research the relationship between industrial development and green space equity under the background of rapid urbanization, the use of quantitative methods to more accurately measure the degree of spatial inequality is essential. In this study, the accessibility of urban public green space in Wuhan is examined based on the two-step floating catchment area method (2SFCA) method at multilevel radius; the urban public green space accessibility of Wuhan in 2013 and 2016 are acquired, and the link between changes in accessibility of urban public green spaces and urban expansion in Wuhan is discussed. It is found that industrial development takes precedence over green space. With its vigorous development, industrial land attracts increasing population, resulting in the drastic decline of the service capacity of green spaces, which is not conducive to the long-term development of the city.

This paper presents a method for computing the visual odometry of ground vehicles. By mounting a downward-looking camera on the vehicle, it can recover the motion of the vehicle only by using the video input. Different from other feature based algorithms, the key technique deployed in this system is an appearance-based approach - template matching. A rotated template matching approach is used for motion estimation. For each two consecutive images captured from the camera, an image patch is first selected from the first image, and then the image patch is rotated by every angle within a predefined range and matched against the second image. The best result from the matching indicates both the position translation and heading changes. For performance evaluation, this algorithm is tested on the indoor granite surface. In the end, we give a comparison of the result between wheel odometry and visual odometry.