北京市二手房价格时空演变特征

The temporal and spatial pattern of urban housing price and its evolution characteristics are important indicators of measuring the equilibrium of urban real estate market. Based on the large amount of real time data of the Internet, we constructed a spatio-temporal data mining method using long time series. Firstly, a large number of real estate listing information and transaction data existing in different real estate business website had been obtained by web crawler technology. Secondly, the correlation between housing listing price and transaction price was tested using a linear regression model and the usability of ubiquitous online real estate data had been validated. Thirdly, a multi-scale grid model of mixed pixels was proposed, which was based on the description of the statistical characteristics of the real estate, and the problem of multi-source data fusion was solved. Moran's I and Geo-detector were used to analyze the geographic spatial autocorrelation and non-homogeneity of housing listing price. The spatial raster database of long term real estate was constructed based on the combination of adjacent spatio-temporal interpolation and P-Bshade interpolation. Finally, the inner part of Beijing Six Ring Road was selected as the study area. We analyzed the spatial-temporal evolution characteristics of second-hand housing prices by grid partition algorithm. Overall, we explored the real time dynamic analysis method of real estate. The results showed that: in the first half of 2016, the growth rate of second-hand housing price was larger, and the latter half of the growth was relatively flat. The spatial distribution of second-hand housing price in Beijing was dominated by a single center pattern. At the same time, there are distributions of high island area. Dongcheng and Xicheng district were the core area of high housing prices, and the magnitude of price volatility was not consistent in different direction. The descending velocity of south of central city was the fastest. The diminishing rate of the north and northwest of central city were the slowest. The house price difference was more remarkable in the center of the city than that in the periphery region of the city.