Clustering Urban Multifunctional Landscapes Using the Self-Organizing Feature Map Neural Network Model
Publication: Journal of Urban Planning and Development
Volume 140, Issue 2
Abstract
Multifunctionality in urban ecosystems has received much attention in the last decade from researchers and policy makers. This paper provides research on urban multifunctional landscape clustering, using the city of Shenzhen, China, as a case study. Utilizing the self-organizing feature map (SOFM) neural network model, six different landscape functional indices were identified, and urban multifunctional landscape regionalization produced five major units. According to SOFM clustering results, each region had its respective primary function, such as gas regulation, water supply, human nature regulation, soil environmental regulation, economy, and cultural priority. The gas regulation ecological supporting region (Zone I) covers , with long coastline form a nature-dominated, less human-influenced physical environment; the water supply ecological supporting region (Zone II) is , and river network density reaches , supporting function of water conservation and water supply; the mountain forest environmental regulating region is Zone III, with substantial forest cover; covering the largest area of , zone type (IV) represents soil regulation region; the fifth zone type (V), on the west coast, is definitely a human-dominated region. The results show that the human and nature interfaced peri-urban region is the most affected and threatened area in the city. Under the control of urban sprawling local regulation, the urban population growth would slow down, but there is no convincing evidence that the limitation of build up land have negative influence on the urban economy. Thereafter, the authors analyzed the functions of each unit and compared the SOFM clustering technique with the traditional K-means clustering method. The result revealed that both methods are effective and appropriate for regionalization of urban multifunctional landscapes, but SOFM has advantages in identifying spatial patterns. Finally, approaches to achieve sustainable urban development were illustrated and their importance highlighted for policymakers and stakeholders.
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Acknowledgments
This research is financially supported by the National Natural Science Foundation of China (No. 41130534, 40635028). The authors sincerely thank the anonymous reviewers for their valuable comments that have led to the present improved version of the original manuscript.
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Published In
Journal of Urban Planning and Development
Volume 140 • Issue 2 • June 2014
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© 2014 American Society of Civil Engineers.
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Received: Jan 1, 2013
Accepted: Jun 19, 2013
Published online: Jun 21, 2013
Published in print: Jun 1, 2014
Discussion open until: Jun 9, 2014
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