Proceedings of the 2020 International Conference on Social Sciences and Big Data Application (ICSSBDA 2020)

Visual Analysis for the Domestic and Overseas Research on the Disposal of Construction Wastes

Authors
Yan Liu, Maohua Li
Corresponding Author
Maohua Li
Available Online 2 November 2020.
DOI
https://doi.org/10.2991/assehr.k.201030.047How to use a DOI?
Keywords
disposal of construction wastes, domestic and overseas, visual analysis, knowledge graph
Abstract
The disposal of construction waste has great significance for social modernization and is a vital issue during urbanization, an inevitability to realize a circular economy and emphasize ecological benefits. This paper illustrates co-occurrence as well as year-over-year change graphs based on keywords of the disposal of construction wastes and studies the spatial distribution from the co-occurrence graphs by institutions and individuals through CNKI and the Web of Science as search engines for kinds of literature. Using the literature between the years of 2000 and 2020 as sample data, the technique and tools of Cite Space to conduct a visual analysis of the data searched and filtered. Besides, this paper offers some proposals concerning more scientific and systematic research on focuses, perspectives, and analytical tools in this field in the future.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2020 International Conference on Social Sciences and Big Data Application (ICSSBDA 2020)
Part of series
Advances in Social Science, Education and Humanities Research
Publication Date
2 November 2020
ISBN
978-94-6239-266-3
ISSN
2352-5398
DOI
https://doi.org/10.2991/assehr.k.201030.047How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yan Liu
AU  - Maohua Li
PY  - 2020
DA  - 2020/11/02
TI  - Visual Analysis for the Domestic and Overseas Research on the Disposal of Construction Wastes
BT  - 2020 International Conference on Social Sciences and Big Data Application (ICSSBDA 2020)
PB  - Atlantis Press
SP  - 230
EP  - 235
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.201030.047
DO  - https://doi.org/10.2991/assehr.k.201030.047
ID  - Liu2020
ER  -