Research On Spatial Distribution Pattern Of Residential Price In National Central Cities Based On Big Data Technology

The national central cities are the core leading cities of the regional urban agglomerations in which they have gathered the primary advantages of Chinese cities in space,resources,population,and policies.It is a new “tipped” city emerging at the level of municipalities directly under the Central Government and provincial capitals,namely Beijing,Shanghai,Guangzhou,Tianjin,Wuhan,Zhengzhou,Chengdu,Chongqing,and Xi'an.The changes in residential prices in these cities are deeply affected by various industries.In addition,the advent of the era of big data has brought new opportunities and challenges to the research of urban housing prices,and also provided a new research paradigm and research direction for the engineering management major.Based on this,this article attempts to use the big data technology in the new data environment to explore the spatial distribution law and driving factors of residential prices in national central cities,in order to reform the research method of urban residential prices,and to provide effective predictions for the development trend of national central urban housing price help.This paper studies the use of web crawler big data technology to obtain the data needed for research,and uses GIS technology to build a spatial big database.Exploratory spatial analysis is adopted to make the spatial distribution of housing prices in each city intuitive and visual.And explore the spatial distribution of residential prices in nine cities.Using the multiple functions of GIS technology to break through the limitations of traditional research,supplement and improve the housing price influencing factors under the background of big data,a preliminary comprehensive index system for housing price influencing factors has been established.The SPSS regression analysis was used to establish a characteristic price model of the influencing factors of national central city housing prices.After trial calculation and comparison,a semi-logarithmic function model was determined for multivariate regression to explore the significant influencing factors and mechanism of housing prices.The main research conclusions are:(1)Regional imbalances in residential prices in central cities are significant.Beijing,the capital city of the country,has the highest housing prices,followed by the eastern coastal cities and the central and western inland cities with the lowest prices.The higher the urban housing price,the greater the volatility,and the lower the housing price,the more stable the fluctuation.(2)The spatial distribution of housing prices in national central cities is polycentric,with obvious spatial agglomeration.The high-value centers of residential prices in Beijing,Shanghai,Guangzhou,and Tianjin are concentrated in urban centers and business areas.The high-value centers have a large area,and adjacent to landscape leisure areas or high-tech industrial areas form small-area high-value sub-centers of residential prices.In Wuhan,Zhengzhou,Chengdu,Chongqing and Xi'an,the areas of high-value center areas are relatively small,and they are located in urban centers or areas with obvious leisure characteristics.(3)The internal changes in housing prices in different cities have significant spatial differences.The housing prices of Beijing,Shanghai,Guangzhou,and Tianjin are generally decreasing from the center of the city in the form of "snacked flat cakes".However,the upward deceleration rates of Beijing's residential prices vary from side to side,with the characteristics of high in the north and low in the south;Shanghai's housing prices are declining with the trunk road loop line as the boundary;housing prices on the banks of the Pearl River in Guangzhou differ significantly;Tianjin's housing prices are decreasing Sudden changes occurred in some locations;Wuhan expanded outward along the Yangtze River as a “two-axis and one-zone” high-value center;Zhengzhou's high-value center of residential prices shifted northward and eastward;Chengdu's residential prices were high in the south,low in the north,and high in the west.The housing prices in northern Chongqing are significantly higher than those in the south;the housing prices in Xi'an are generally higher in the south and lower in the north.(4)House age,property management,central location,subway stations,large commercial districts,primary and secondary schools,park green spaces and water landscapes are important factors affecting urban housing prices.It has a significant impact on residential prices in the nine major cities.(5)The value-added effects of housing price characteristics on housing prices in different cities are different.For example,Beijing,Shanghai and Guangzhou's ring road locations have the greatest effect on housing prices.For each residential area ring road that increases by one unit outside the inner ring road,the housing price decreases by11.919%,9.501%,and 7.795%,respectively.(6)The characteristics of housing price factors have different degrees of influence on housing prices in different cities.In general,the residential prices in Beijing,Shanghai,and Guangzhou are greatly affected by the characteristics of the central location,large commercial districts,and corporate companies;the residential prices in Tianjin,Wuhan,and Chengdu are most affected by the characteristics of the building,the central location,and the landscape and leisure characteristics;Zhengzhou,Chongqing,and Xi'an residential prices are greatly affected by the characteristics of buildings,living facilities and landscape leisure features.In addition,Chongqing and Xi'an residential prices are also greatly affected by the characteristics of transportation locations.The research in this article provides a practical method for the construction of a data system,an index system,and a technical system for the study of the spatial distribution pattern of residential prices based on multivariate big data.Taking nine major national central cities as samples,it can provide a reference for typical city research for the analysis and simulation of residential price distribution laws in other first-tier and second-tier cities in China.At the same time,it provides a new reference direction for the paradigm change of real estate research in the engineering management specialty,as well as a new innovative perspective and practice direction for urban development research in the context of the big data era.The research in this article is innovative in technical methods,sample depth,and influencing factors.