Research on the Influence of Transportation Network Centrality on Housing Price Based on Big Data
Authors: 
Feng Gao, Zhibin ZhangAuthors Info & Claims
Pages 28 - 32
Abstract
Based on the big data of transportation network and housing price, geographic weighted regression is used to measure the degree of difference in the impact of urban road network and public transportation network centrality on housing prices. The results show that the degree of influence of the public transport network on housing prices is generally higher than that of the road network; the betweenness of the public transport network has the greatest impact on housing prices, followed by the direct centrality of the road network, the proximity centrality of the public transport network, the proximity centrality of the road network, and the public transport network. Network direct centrality, road network between centrality. Lanzhou's urban housing prices have strong traffic-oriented characteristics, and differentiated traffic improvement plans should be formulated according to the characteristics of the traffic network of different land parcels.
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Published In
September 2021
139 pages
ISBN:9781450385084
DOI:10.1145/3490700
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 January 2022
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- Research-article
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- Refereed limited
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ICACS '21
ICACS '21: 2021 The 5th International Conference on Algorithms, Computing and Systems
September 24 - 26, 2021
Xi'an, China
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