2022
DOI: 10.1155/2022/4938311
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Research on Urban Fringe Rural Design Based on Correlation Analysis of Human-Land Relationship: Taking Xiananshan Village as an Example

Abstract: Based on system theory and human-land relationship theory, the evolution characteristics of the habitat environment of scenic fringe rural tourism sites are summarized in three aspects, including self-organisation, periodicity, and volatility. Taking the tourism circle of Xiananshan Village in Liandu District in Lishui as an example, the spatial and temporal changes in the flow of tourism factors are taken as the main driving factors, and a dynamic model of the evolution of the habitat environment of scenic fr… Show more

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Cited by 2 publications
(2 citation statements)
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“…By analyzing the experimental results of a large number of public datasets, it is shown that the algorithm can not only quickly and accurately complete the automatic registration between large-scale graph pairs, but also obtain good robustness and fault tolerance. We propose a graph representation learning method combining sparse matrix decomposition and personalized random walk, which can efficiently learn large-scale graphs and obtain representation vectors that can capture the global structure information of the graph, so it is very suitable for graph alignment tasks [7]. Aiming at the challenges faced by large-scale graph alignment, such as the feature dimension is too high and it is difficult to obtain effective feature subsets, a rapid optimization strategy for massive graph data is proposed.…”
Section: Introductionmentioning
confidence: 99%
“…By analyzing the experimental results of a large number of public datasets, it is shown that the algorithm can not only quickly and accurately complete the automatic registration between large-scale graph pairs, but also obtain good robustness and fault tolerance. We propose a graph representation learning method combining sparse matrix decomposition and personalized random walk, which can efficiently learn large-scale graphs and obtain representation vectors that can capture the global structure information of the graph, so it is very suitable for graph alignment tasks [7]. Aiming at the challenges faced by large-scale graph alignment, such as the feature dimension is too high and it is difficult to obtain effective feature subsets, a rapid optimization strategy for massive graph data is proposed.…”
Section: Introductionmentioning
confidence: 99%
“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”
mentioning
confidence: 99%