Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China

Urban shrinkage has become a topic of major concern to scholars of geography and urban science. However, the methods of identifying urban shrinkage and growth have mostly focused on traditional statistical methods, and studies based on nighttime light (NTL) data are rare. Here, we use the NTL data for 56 months from 2012 to 2019 obtained by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar Orbiting Partnership (NPP) to identify the shrinkage and growth patterns of Yichun in China, by calculating the slope of the NTL radiance value after denoising. At the same time, by combining high-resolution Google satellite images and traditional demographic data, we analyzed the shrinkage characteristics of Yichun. The results of the study confirmed the characteristics of partial shrinkage in China’s shrinking cities. In addition, the use of NPP-VIIRS NTL data was able to more accurately identify the urban shrinkage and growth patterns, and may also be seen to present a more objective picture of reality, thus providing a new perspective for studies of urban shrinkage.

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Mining qualitative patterns in spatial cluster analysis

Expert Systems with Applications ◽

10.1016/j.eswa.2011.08.079 ◽

2012 ◽

Vol 39 (2) ◽

pp. 1753-1762 ◽

Cited By ~ 7

Author(s):

Ickjai Lee ◽

Yang Qu ◽

Kyungmi Lee

Keyword(s):

Cluster Analysis ◽

Spatial Cluster ◽

Spatial Cluster Analysis

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Detection of major climatic and environmental predictors of liver fluke exposure risk in Ireland using spatial cluster analysis

Veterinary Parasitology ◽

10.1016/j.vetpar.2015.02.029 ◽

2015 ◽

Vol 209 (3-4) ◽

pp. 242-253 ◽

Cited By ~ 15

Author(s):

Nikolaos Selemetas ◽

Theo de Waal

Keyword(s):

Cluster Analysis ◽

Liver Fluke ◽

Spatial Cluster ◽

Environmental Predictors ◽

Exposure Risk ◽

Spatial Cluster Analysis

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Spatial Cluster Analysis by the Bin-Packing Problem and DNA Computing Technique

Discrete Dynamics in Nature and Society ◽

10.1155/2013/891428 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Xiyu Liu ◽

Jie Xue

Keyword(s):

Cluster Analysis ◽

Time Complexity ◽

Dna Computing ◽

Bin Packing ◽

Packing Problem ◽

Spatial Cluster ◽

Computing Technique ◽

Bin Packing Problem ◽

Data Points ◽

Spatial Cluster Analysis

Spatial cluster analysis is an important data mining task. Typical techniques include CLARANS, density- and gravity-based clustering, and other algorithms based on traditional von Neumann's computing architecture. The purpose of this paper is to propose a technique for spatial cluster analysis based on sticker systems of DNA computing. We will adopt the Bin-Packing Problem idea and then design algorithms of sticker programming. The proposed technique has a better time complexity. In the case when only the intracluster dissimilarity is taken into account, this time complexity is polynomial in the amount of data points, which reduces the NP-completeness nature of spatial cluster analysis. The new technique provides an alternative method for traditional cluster analysis.

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