Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76316
Title: Characterizing the growth patterns of 45 major metropolitans in mainland China using DMSP/OLS data
Authors: Jia, T 
Chen, K
Wang, JY
Keywords: DMSP/OLS data
Metropolitan growth
Intensification
Expansion
Sustainable urban development
Issue Date: 2017
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Remote sensing, 2017, v. 9, no. 6, 571 How to cite?
Journal: Remote sensing 
Abstract: Understanding growth patterns at the metropolitan level is instructive for better planning and policy making on sustainable urban development. Using DMSP/OLS data from 1992 to 2013, this article aims to investigate growth patterns of major metropolitans in Mainland China from the aspects of intensification and expansion. We start by calibrating the DMSP/OLS data and selecting 45 major metropolitans. On intensification, results suggest that aggregately, metropolitans displayed cyclical pattern over time and large metropolitans tended to have higher levels of intensification than moderate or small ones. Individually, metropolitans with similar intensification over time could be clustered together using Dendrogram, and evolution pattern of the clusters exhibited similarity to the aggregated one. On expansion, results show that aggregately metropolitans displayed a decreasing trend over time, and moderate or small metropolitans tended to have higher levels of expansion than large ones. Particularly, moderate metropolitans were more likely to expand adjacently, and small ones were more likely to experience scatter or corridor expansion. Each metropolitan can be represented by a mixed expansion model over time, which might tell where and how much expansion occurred in the current year. Furthermore, intensification is highly correlated with expansion over time for small metropolitans, but they are poorly correlated for large or moderate ones. Lastly, the high correlation of intensification and expansion with the change of GDP in each year indicates the reliability of our work.
URI: http://hdl.handle.net/10397/76316
ISSN: 2072-4292
EISSN: 2072-4292
DOI: 10.3390/rs9060571
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