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Study On Spatial Distribution Of Commodity Housing Price In Taiyu Co-construction Area

Posted on:2020-07-23Degree:MasterType:Thesis
Country:ChinaCandidate:W DouFull Text:PDF
GTID:2439330572498689Subject:Land Resource Management
Abstract/Summary:PDF Full Text Request
Urban integration of Taiyuan and Yuci is an important strategic deployment for upgrading regional competitiveness in Taiyuan City and Jinzhong City of Shanxi Province.It is an important core step in building Taiyuan Metropolitan Area,and it is a leap-forward development in Shanxi Province in the context of the rise of Central China.The core link.Based on the construction of the Shanxi Comprehensive Reform Experimental Zone,the Taiyu Co-construction Area is committed to creating a regional service center,a high-tech industrial cluster and an independent innovation industrial base.Since 2002,urban integration of Taiyuan and Yuci has entered the actual construction and has built up the “Taiyuan South Station Business Circle”,“Wanda,Outlet Business Circle”,“Provincial New District”,“Economic and Technological Development Zone”,etc.The functional area has gradually formed,the infrastructure has been gradually improved,the transportation network has become more complete,and real estate development projects have increased rapidly and formed agglomeration around the industrial functional areas.Among them,the common commodity housing is the main market,the market is relatively large,and the spatial distribution is relatively wide.Therefore,the commercial housing in the co-construction area is selected as the research object,and the spatial distribution characteristics of the commodity housing price and its changing law with time are analyzed.First of all,according to the development speed of the Taiyu Co-construction Area,the development and construction degree of each functional area,the time when various infrastructures are put into operation,and the availability of basic data of commercial housing,the selection of new commercial housing in 2010-2018 is studied.Targets are divided into three periods: 2010-2012,2013-2015 and 2016-2018.Collected and collected 329 samples of new commercial housing in the co-construction area in 2010-2018,and selected 311 sample points by filtering out the missing data samples.Based on the extensive reading of the literature,the GIS technology was selected to analyze the spatial distribution characteristics of commercial residential buildings based on the actual situation of the Taiyu Co-construction Area.Through the ArcGIS software,the point,line and surface map of the commercial housing in the co-construction area are drawn,and a spatial database is established.The spatial data structure analysis of commercial residential samples shows that the three-stage data conforms to the normal distribution;the spatial three-dimensional perspective is drawn for full trend analysis;the geostatistical wizard is used for spatial variation and global autocorrelation analysis,indicating that the three co-construction areas The phase is spatially significant in different directions.Using the geostatistical module to select Kriging interpolation to establish a three-stage commodity housing price forecast map of the co-construction area,and draw a contour map,and analyze that the commodity housing price in the co-construction area is spatially distributed from the center to the surrounding area.Declining trend;the range of commodity residential price extensions expands and protrudes to the southeast,indicating that the price of commercial housing in the Taiyuan border area has steadily increased,and the spatial difference has gradually decreased.Based on the spatial distribution characteristics of commodity housing prices in the co-construction area,this paper uses the buffer function to select the appropriate buffer distance to qualitatively analyze the urban planning,basic facilities,location conditions and traffic conditions,and explore the influence of various factors on the residential price of the co-construction area..
Keywords/Search Tags:Urban integration, Spatial distribution, Evolution, Influence Factor
PDF Full Text Request
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