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Abstract: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. Keywords: regression model; housing prices; geographically weighted regression; influence factor; hedonic model; artificial neural network (ANN); geographically weighted regression (GWR); urban planning Research BackgroundWith the commercialization of housing and the deepening of urbanization in China, housing prices show an increasing influence on the land market, and thus affect urban planning and urban development [1]. The urban spatial features of a housing property are closely associated to its price [2]. Therefore, in the urban planning discipline which directly guides the spatial development of cities, attention should be given to the study of the spatial features and the mechanism of how they influence housing price.In studies on housing price and its influence factors, the hedonic model proposed by Rosen is the most commonly used approach [3]. The influence factors in early studies include the location feature, neighborhood feature and architectural feature [4]; later studies incorporate other factors such as accessibility [5], land-use planning [6]; and more recent studies further introduce external

Abstract: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.

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