Spatial Cognitive Modeling of the Site Selection for Traditional Rural Settlements: A Case Study of Kengzi Village, Southern China
Publication: Journal of Urban Planning and Development
Volume 146, Issue 4
Abstract
As a result of the booming rural reconstruction in China, the recurring problem of “village sameness” reflects the inadequate understanding of inherent spatial characteristics of traditional settlements. To better understand the internal logic driving site selection of historic rural settlements, this study proposed a machine learning method based on the Gaussian mixture model (GMM). First, significant spatial variables controlling settlement distribution were deduced using contextual analysis, then a univariate GMM was implemented to examine the settlement distribution sensitivity for every variable. Finally, a multivariate GMM was utilized to perform a multivariate regression analysis on the nonlinear nonmonotonic relationship between significant control variables and land usage development potential, which was a simulation of people's optimizing selection of living space. In accordance with the abstracted spatial rules, the model was also used for predicting the spatial trends that could support regional planning activities. In additional, a comparison between the GMM and the logistic regression model was made using spatial feature recognition and the spatial characteristic regression. The results showed the comparative advantage of the GMM for its nonlinear nonmonotonic behavior.
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Acknowledgments
This research was funded by the National Natural Science Foundation of China (51908160), the Natural Science Foundation of Guangdong Province, China (2020A1515010681), the Natural Science Foundation of Shenzhen, China (JCYJ20190806143403472), and the Fundamental Research Funds for the Central Universities (45001028).
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Published In
Journal of Urban Planning and Development
Volume 146 • Issue 4 • December 2020
Copyright
© 2020 American Society of Civil Engineers.
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Received: Feb 21, 2020
Accepted: Jun 8, 2020
Published online: Sep 17, 2020
Published in print: Dec 1, 2020
Discussion open until: Feb 17, 2021
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