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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:471       Data Files Downloaded:10      
Data Downloaded:1.23 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:


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Data Product:

ID Data Name Data Size Operation
1 WuhanHousePriceInfFactor.rar 126.46KB
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