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A multiscale flow-focused geographically weighted regression modelling approach and its application for transport flows on expressways

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成果类型:
期刊论文
作者:
Zhang, Lianfa*;Cheng, Jianquan;Jin, Cheng*;Zhou, Hong
通讯作者:
Zhang, Lianfa;Jin, Cheng
作者机构:
[Zhou, Hong; Zhang, Lianfa] Wuhan Univ, Sch Power & Mech Engn, Wuhan 430072, Hubei, Peoples R China.
[Zhang, Lianfa] Cent China Normal Univ, Sch Comp Sci, Wuhan 430077, Hubei, Peoples R China.
[Cheng, Jianquan] Manchester Metropolitan Univ, Dept Nat Sci, Div Geog & Environm Sci, Chester St, Manchester M1 5GD, Lancs, England.
[Jin, Cheng] Nanjing Normal Univ, Sch Geog Sci, Nanjing 210023, Jiangsu, Peoples R China.
[Jin, Cheng] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China.
通讯机构:
[Zhang, Lianfa] W
[Zhang, Lianfa] C
[Jin, Cheng] N
[Jin, Cheng] J
Wuhan Univ, Sch Power & Mech Engn, Wuhan 430072, Hubei, Peoples R China.
语种:
英文
关键词:
big data;Moran Index;multiscale flow-focused geographically weighted regression;regional scale;transport flows
期刊:
Applied Sciences-Basel
ISSN:
2076-3417
年:
2019
卷:
9
期:
21
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41871137, 41571134, 41571124]; Natural Science Foundation of the Jiangsu Higher Education Institutions [16KJA170002]
机构署名:
本校为通讯机构
院系归属:
计算机学院
摘要:
Scale is a fundamental geographical concept and its role in different geographical contexts has been widely documented. The increasing availability of transport mobility data, in the form of big datasets, enables further incorporation of spatial dependencies and non-stationarity into spatial interaction modeling of transport flows. In this paper a newly developed multiscale flow-focused geographically weighted regression (MFGWR) approach has been applied, in addition to global and local Moran I indices of flow data, to model multiscale socio-ec...

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