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      The Complex Network Theory-Based Urban Land-Use and Transport Interaction Studies

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      Complexity
      Hindawi Limited

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          Abstract

          The research on complex networks offers novel insight into the analysis of complex urban systems. The objective of this article is to provide a review of complex network theory in urban land-use and transport studies to date. Some traditional integrated studies of urban land-use and traffic networks are summarized and analysed; related research gaps were proposed. Then, this paper reviewed the application of complex network theory in urban land-use and transport research and practice. It shows that the node importance identification method is critical for network protection or attack studies; the multiple centrality assessment and kernel density estimation approaches can be used to represent the intuitionistic connections of urban traffic networks and surrounding land-uses; it can be used to verify the changing trend and variation of landscape connectivity; also it can be applied to the identification of key changed land-use types in land-use cover change; the coevolution process can be treated as an integrated way to discuss the relationships between urban traffic network growth and land-use change, and the multilayer networks based analysis is a novel method to measure their interactions. This paper is essential in establishing apparent research interests and points out the further potential application of complex network theory in urban traffic network and land-use related studies.

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          Most cited references75

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          Finding and evaluating community structure in networks.

          We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
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            LANDSCAPE CONNECTIVITY: A GRAPH-THEORETIC PERSPECTIVE

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              On the usage and measurement of landscape connectivity

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                Author and article information

                Journal
                Complexity
                Complexity
                Hindawi Limited
                1076-2787
                1099-0526
                June 17 2019
                June 17 2019
                : 2019
                : 1-14
                Affiliations
                [1 ]College of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang, Guizhou, China
                Article
                10.1155/2019/4180890
                5474570a-67a4-44df-aac3-b5e9f384a098
                © 2019

                http://creativecommons.org/licenses/by/4.0/

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