MULTI-LEVEL CITY PORTRAIT RESEARCH BASED ON MULTI-SOURCE DATA
City portrait is a social impression generated by the interaction between the public and the city, which can help us better understand and perceive the nature and characteristics of the city, and thus provide strong support for the development and governance of the city. However, most existing studi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-12-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-1-W1-2023/533/2023/isprs-annals-X-1-W1-2023-533-2023.pdf |
| _version_ | 1797403649139277824 |
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| author | F. Zhuo C. Jing G. Xu Y. Fu |
| author_facet | F. Zhuo C. Jing G. Xu Y. Fu |
| author_sort | F. Zhuo |
| collection | DOAJ |
| description | City portrait is a social impression generated by the interaction between the public and the city, which can help us better understand and perceive the nature and characteristics of the city, and thus provide strong support for the development and governance of the city. However, most existing studies extract thematic semantic labels globally, but ignore the order of the tags and the degree of their contribution in the topic, which affects the city portrait extraction results. In addition, existing studies also lack the analysis of the impact of grid areas as the study scale on city portraits. In this paper, we propose a new approach to accurately identify city labels based on multi-source data grid fusion using a topic feature word extraction model (Weight-LdaVecNet) with fused topic word embedding and network structure analysis with feature word weight constraints. On this basis, we construct a multi-level city portrait description framework using hierarchical cluster analysis, extract tag clusters, and obtain a similarity matrix by combining topic feature tags and region feature tags using similarity analysis to construct a multi-level city region portrait, with a view to achieving a fine-grained construction of a multi-level city portrait. The experimental results show that, compared with the traditional LDA model, our method indicates that the identified city labels with similar thematic semantics have strong aggregation, thus proving the effectiveness of our proposed method. In addition, in the overall multi-level city portrait, we find that Beijing has a strong attractiveness in terms of cultural features. However, the regional distribution of cultural characteristics dimensions is not uniform in the multilevel city-region portrait, and better rational allocation and planning of cultural resources are needed to better meet people's needs. |
| first_indexed | 2024-03-09T02:42:25Z |
| format | Article |
| id | doaj.art-8789612b351b4a009b29b19f246f73ee |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-03-09T02:42:25Z |
| publishDate | 2023-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-8789612b351b4a009b29b19f246f73ee2023-12-06T02:46:12ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502023-12-01X-1-W1-202353354010.5194/isprs-annals-X-1-W1-2023-533-2023MULTI-LEVEL CITY PORTRAIT RESEARCH BASED ON MULTI-SOURCE DATAF. Zhuo0C. Jing1G. Xu2Y. Fu3School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaJD Logistics, JD Logistics, Beijing 100176, ChinaCity portrait is a social impression generated by the interaction between the public and the city, which can help us better understand and perceive the nature and characteristics of the city, and thus provide strong support for the development and governance of the city. However, most existing studies extract thematic semantic labels globally, but ignore the order of the tags and the degree of their contribution in the topic, which affects the city portrait extraction results. In addition, existing studies also lack the analysis of the impact of grid areas as the study scale on city portraits. In this paper, we propose a new approach to accurately identify city labels based on multi-source data grid fusion using a topic feature word extraction model (Weight-LdaVecNet) with fused topic word embedding and network structure analysis with feature word weight constraints. On this basis, we construct a multi-level city portrait description framework using hierarchical cluster analysis, extract tag clusters, and obtain a similarity matrix by combining topic feature tags and region feature tags using similarity analysis to construct a multi-level city region portrait, with a view to achieving a fine-grained construction of a multi-level city portrait. The experimental results show that, compared with the traditional LDA model, our method indicates that the identified city labels with similar thematic semantics have strong aggregation, thus proving the effectiveness of our proposed method. In addition, in the overall multi-level city portrait, we find that Beijing has a strong attractiveness in terms of cultural features. However, the regional distribution of cultural characteristics dimensions is not uniform in the multilevel city-region portrait, and better rational allocation and planning of cultural resources are needed to better meet people's needs.https://isprs-annals.copernicus.org/articles/X-1-W1-2023/533/2023/isprs-annals-X-1-W1-2023-533-2023.pdf |
| spellingShingle | F. Zhuo C. Jing G. Xu Y. Fu MULTI-LEVEL CITY PORTRAIT RESEARCH BASED ON MULTI-SOURCE DATA ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | MULTI-LEVEL CITY PORTRAIT RESEARCH BASED ON MULTI-SOURCE DATA |
| title_full | MULTI-LEVEL CITY PORTRAIT RESEARCH BASED ON MULTI-SOURCE DATA |
| title_fullStr | MULTI-LEVEL CITY PORTRAIT RESEARCH BASED ON MULTI-SOURCE DATA |
| title_full_unstemmed | MULTI-LEVEL CITY PORTRAIT RESEARCH BASED ON MULTI-SOURCE DATA |
| title_short | MULTI-LEVEL CITY PORTRAIT RESEARCH BASED ON MULTI-SOURCE DATA |
| title_sort | multi level city portrait research based on multi source data |
| url | https://isprs-annals.copernicus.org/articles/X-1-W1-2023/533/2023/isprs-annals-X-1-W1-2023-533-2023.pdf |
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