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author:

Fan, K. (Fan, K..) [1] | Li, D. (Li, D..) [2] (Scholars:李代超) | Wu, H. (Wu, H..) [3] | Wang, Y. (Wang, Y..) [4] | Yu, H. (Yu, H..) [5] | Zeng, Z. (Zeng, Z..) [6]

Indexed by:

Scopus

Abstract:

Evaluating typical rural characteristics reveals certain advantages of rural revitalization and is crucial for understanding rural disparities and promoting development. Field research and statistical data can reflect the spatial distribution of local resources and development models. However, due to cost limitations and statistical constraints, it is impossible to effectively compare and evaluate the characteristics of rural development at the long time series, large scale and fine granularity required for sustainable regeneration. This study proposes a web-based method for the extraction and evaluation of rural revitalization characteristics (WERRC). The BERT-BiLSTM-Attention model categorizes rural web texts according to five themes: industrial prosperity, ecological livability, rural civilization, effective governance, and prosperous life. The Term Frequency-Inverse Document Frequency (TF-IDF) algorithm extracts rural characteristics, and the relative advantages of these features are compared among 100 Chinese villages. WERRC extracts the typical characteristics, obtains the spatial distribution and relative advantage, and then ranks them according to the five themes. The relationship between national policy guidance and rural development is explored. The results support further exploration of differentiated, high-quality development modes that incorporate rural advantages into policy, adjust industrial structure, and optimise revitalization strategies at the rural scale. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Keyword:

characteristic extraction regional sustainable development Rural revitalization typical village characteristics web text mining

Community:

  • [ 1 ] [Fan K.]The Academy of Digital China (Fujian), Fuzhou University, Fuzhou, China
  • [ 2 ] [Fan K.]Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou, China
  • [ 3 ] [Li D.]The Academy of Digital China (Fujian), Fuzhou University, Fuzhou, China
  • [ 4 ] [Li D.]Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou, China
  • [ 5 ] [Wu H.]School of Economics and Management, Fuzhou University, Fuzhou, China
  • [ 6 ] [Wang Y.]Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
  • [ 7 ] [Yu H.]Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
  • [ 8 ] [Zeng Z.]Hunan Cartographic Publishing House, Changsha, China

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Source :

International Journal of Geographical Information Science

ISSN: 1365-8816

Year: 2023

Issue: 2

Volume: 38

Page: 297-321

4 . 3

JCR@2023

4 . 3 0 0

JCR@2023

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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