Real estate spatial price distribution in xining from the perspective of big data

Abstract

Studying the spatial distribution of real estate price can not only help consumers to choose the suitable real estate, but also help urban planners to make better decisions. Under the background big data, a new perspective of research on urban real estate prices is produced. Based on the real estate parameters of ordinary residential buildings in Xining city, ArcGIS software is used to conduct the nearest neighbor distance analysis, and it is found that the residential buildings in the area presented significant agglomeration. Then, Moran’s I index is selected for spatial autocorrelation analysis, which proves the positive spatial correlation of real estate prices in Xining city. Finally, the spatial distribution map of real estate prices is obtained by fitting the real estate prices in the research area with the statistical analysis of land, which finds the area of Chengxi is the center of local housing price, and the price is decreasing gradually around other areas. In addition, the main factors affecting housing prices in four areas can be obtained by analyzing the distribution figure, which gives a significant reference for residents and policy planners.