Simulations of local Moran’s index in a spatio-temporal setting

Diawara, Norou; Waller, Lance A.; King, Robert; Lorio, Jennifer L.

doi:10.1080/03610918.2018.1425441

Cited by 11 publications

(1 citation statement)

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“…The comprehensive evaluation can effectively cover the effect of each index on the explained variable, and the entropy method can comprehensively and objectively calculate the SDR index. In space research, radar maps, 23 global Moran index and local Moran index 24 , 25 are often used. The research content is relatively simple, and it stops at the level of whether the research objects have a spatial correlation.…”

Section: Introductionmentioning

confidence: 99%

Spatial connection network characteristics and development trend forecast of natural gas industry's sustainable development resilience in China

Zhao

Wang

2023

Science Progress

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Objectively recognizing and improving the sustainable development resilience of China's natural gas industry will help achieve the low-carbon transformation goal of China's energy system. Taking 31 Chinese provinces as the research area, this paper measures the sustainable development resilience (SDR) of China's natural gas industry based on the Drive-Pressure-State-Impact-Response (DPSIR) model and entropy method, and integrates the gravity correction model and social network analysis methods to identify the spatial linkages and network patterns among core regions, and further explores the development trend of the SDR of China's natural gas industry using grey model (GM(1,1)) moderated by a variable-weight buffer operator. The results show that: (1) There are significant regional differences in the SDR of the natural gas industry across Chinese provinces. The SDR is a high priority in Shanghai, Shaanxi, Sichuan, Xinjiang, Guangdong and Shandong, while it is low major in Tibet, Yunnan, Guangxi, Guizhou and Ningxia. (2) The spatial connection network density is low in China's natural gas industry, and the network correlation between provinces is poor. In detail, Jiangsu, Guangdong and Shandong are the core of the entire network and the connection lines between provinces are mainly basic in the whole region with poor connection strength, but there is a trend for the better. (3) The changing trend is significantly different in the SDR of Chinese provinces, and the prediction results show a trend of “polarization” in the SDR index of the provinces in the resource endowment area.

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Section: Introductionmentioning

confidence: 99%