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Data Details

Influence Factor Dataset of House Price in Wuhan Based on TD-GNNWR (2019)


WU Sensen1DING Jiale1DU Zhenhong*1
1 School of Earth Sciences，Zhejiang University，Hangzhou 310058，China

DOI:10.3974/geodb.2024.08.07.V1

Published:Aug. 2024

Visitors：294       Data Files Downloaded：12      
Data Downloaded：1.48 MB      Citations：

Key Words：

House pirce, Wuhan,spatial GNNWR

Abstract：

The authors took the house price in Wuhan as the research object, used crawler technology to collect basic data such as sample house prices and impact factors in Wuhan, and selected house price influencing factors based on the price characteristic model to establish the regression relationship, and model the spatial non-stationary process of house price in Wuhan using the TD-GNNWR(Geographically Neural Network Weighted Regression) method to obtain the influence factor dataset of house price in Wuhan based on TD-GNNWR (2019). The dataset includes: (1) the boundary data of the study area; (2) the spatial distribution of the predicted value of house prices in Wuhan in 2019; (3) the spatial distribution data of house price influencing factors in Wuhan in 2019. The dataset is archived in .shp, .tif and .txt data formats, and consists of 17 data files with data size of 1.65 MB (Compressed into one file with 126 KB). The research paper based on the dataset was published in Acta Geographica Sinica, Vol. 79, No. 8, 2024.

Foundation Item：

National Natural Science Foundation of China (42001323); Ministry of Science and Technology of P. R. China (2021YFB3900902); Department of Science and Technology of Zhejiang Province (2021C01031)

Data Citation：

WU Sensen, DING Jiale, DU Zhenhong*.Influence Factor Dataset of House Price in Wuhan Based on TD-GNNWR (2019)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2024. https://doi.org/10.3974/geodb.2024.08.07.V1.

References：


     [1] Du, Z. H., Wang, Z. Y., Wu, S. S, et al. Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity [J]. International Journal of Geographical Information Science, 2020, 34(7): 1353-1377.
     [2] Du, Z. H., Qi, J., Wu, S. S., et al. A spatially weighted neural network based water quality assessment method for large-scale coastal areas [J]. Environmental Science & Technology, 2021, 55(4): 2553-2563.
     [3] Chen, Y. J., Wu, S. S., Wang, Y. Y, et al. Satellite-based mapping of high-resolution ground-level PM2.5 with VIIRS IP AOD in China through spatially neural network weighted regression [J]. Remote Sensing, 2021, 13(10): 1979.
     [4] Wu, S. S. The theory and method of geographically and temporally neural network weighted regression [D]. HangZhou: Zhejiang University, 2018.
     [5] Comber, A., Chi, K., Huy, M. Q., et al. Distance metric choice can both reduce and induce collinearity in geographically weighted regression [J]. Environment and Planning B: Urban Analytics and City Science, 2020, 47(3): 489-507.
     [6] Lu, B. B., Charlton, M., Fotheringham, A. S. Geographically weighted regression using a non-Euclidean distance metric with a study on London house price data [J]. Procedia Environmental Sciences, 2011: 792-797.
     [7] Song, W. X., Mao, N., Chen, P. Y., et al. Coupling mechanism and spatial-temporal pattern of residential differentiation from the perspective of housing prices: A case study of Nanjing [J]. Acta Geographica Sinica, 2017, 72(4): 589-602.
     [8] Wang, X. L., Fu, J. Y., Lyu, T. Y. Study on the spatial heterogeneity of influencing factors of housing price from the perspective of city center functional differentiation: A case study on Wuhan city [J]. Prices Monthly, 2022(2): 1-9.
     [9] Osland, L. An application of spatial econometrics in relation to hedonic house price modeling [J]. Journal of Real Estate Research, 2010, 32(3): 289-320.
     [10] Kang, Y., Zhang, F., Gao, S., et al. Human settlement value assessment from a place perspective: Considering human dynamics and perceptions in house price modeling [J]. Cities, 2021, 118(2): 103333.
     [11] Gao, H. Research on spatial differentiation and influencing factors of second-hand house prices in the third ring road of Wuhan [D]. Wuhan: Wuhan University, 2020.
     

Data Product:

ID Data Name Data Size Operation
1 WuhanHousePriceInfFactor.rar 126.46KB
Co-Sponsors

Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences

The Geographical Society of China

Parteners

Committee on Data for Science and Technology (CODATA) Task Group on Preservation of and Access to Scientific and Technical Data in/for/with Developing Countries (PASTD)

Jomo Kenyatta University of Agriculture and Technology

Digital Linchao GeoMuseum