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author:

Wang, Yajun (Wang, Yajun.) [1] (Scholars:王亚军) | Yu, Shanshan (Yu, Shanshan.) [2]

Indexed by:

Scopus SCIE

Abstract:

In order to improve the feasibility of the overall design scheme of coastal urban planning, a general design scheme of coastal urban planning based on Geographic Information System (GIS) is proposed. This paper constructs a geographic information analysis model, which combines information fusion with large data information processing methods, and carries out geographic information data fusion and processing analysis of coastal urban planning. According to the distribution attributes of the data, the collected geographic information data are clustered. Fuzzy C-means clustering method is used to realize coastal urban planning geographic information data fusion clustering. Based on the adaptive correlation integral matching method, the geographic information data clustering centers of coastal urban planning are searched to realize data correlation mining. Principal component analysis is used to mine the key information of coastal urban planning and clustering, and to guide the optimal design of coastal urban planning. Finally, the software is designed and simulated. The results show that the method is effective in the overall design of coastal urban planning. Through GIS geographic information fusion and dispatch, the feasibility of coastal urban planning and design is improved.

Keyword:

coastal urban planning data geographic information GIS information fusion

Community:

  • [ 1 ] [Wang, Yajun]Fuzhou Univ, Sch Architecture, Fuzhou 350116, Peoples R China
  • [ 2 ] [Yu, Shanshan]Xiamen Univ, Sch Architecture & Civil Engn, Xiamen 361005, Peoples R China

Reprint 's Address:

  • [Yu, Shanshan]Xiamen Univ, Sch Architecture & Civil Engn, Xiamen 361005, Peoples R China

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Source :

JOURNAL OF COASTAL RESEARCH

ISSN: 0749-0208

Year: 2020

Page: 328-332

0 . 8 5 4

JCR@2020

1 . 1 1 0

JCR@2021

ESI Discipline: ENVIRONMENT/ECOLOGY;

ESI HC Threshold:159

JCR Journal Grade:4

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count: 1

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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