On the use of Markov chain models for drought class transition analysis while considering spatial effects

Yang, Wentao; Deng, Min; Tang, Jianbo; Jin, Rui

doi:10.1007/s11069-020-04113-6

Cited by 16 publications

(4 citation statements)

References 29 publications

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“…It has been demonstrated as an effective approach to explore the regional dynamics which are inherent in the data of interest [87]. The spatial Markov chain model has also been widely utilized in the area of economics, geology, environment, public health, and engineering [88], such as soil erosion [89], obesity rate [90], drought class transition [91], temporal and spatial evolution of skin cancer [92], etc. Based on Markov chain analysis and the condition of the spatial lag type of RNCUE in the initial year, N models can be formed as an N × N conditional probability transfer matrix (Table 4), and then we can analyse the probability of improving and reducing the RNCUE in a certain region under different geographical spatial backgrounds.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Evolution and Influencing Factors of the Rural Natural Capital Utilization Efficiency: A Case Study of Chongqing, China

Shi

2022

Land

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The efficient utilization and optimal allocation of natural capital play an important role in economic development and human well-being. The production process of natural capital is the input and output processing of its ecological resources and the environment. Improving the rural natural capital utilization efficiency (RNCUE) is an important goal of natural capital investment, and the efficient utilization of natural capital is an important factor for the efficient operation of the regional economy and society. This study uses the super slack-based measure (SBM) model based on undesirable outputs to measure the RNCUE, combines the exploratory data analysis method (ESDA) and spatial Markov transfer matrix to analyse the spatiotemporal evolution characteristics of efficiency, and analyses the influencing factors of the change of the RNCUE in Chongqing through a spatial econometric model. The results show that: (1) The RNCUE in Chongqing is still at a low level as a whole and there is a large space for efficiency improvement and efficient operation. There is a certain spatial dependence on the interaction of efficiency between adjacent districts and counties. (2) High-high agglomeration is concentrated in the western area of Chongqing One-hour Economic Circle, and low-low agglomeration is concentrated in Southeast and Northeast Chongqing. The probability of a large change in the RNCUE in consecutive years is small, and it is easy to form the phenomenon of “club convergence” in space. (3) The RNCUE in Chongqing has been affected by rainfall, temperature, NDVI, the per capita GDP, proportion of fixed asset investment, expenditure for agriculture, and proportion of primary industry and rural population. The influencing factors show that the spatial heterogeneity is significant. The RNCUE has a negative correlation with forest coverage and the expenditure for agriculture, is not significantly positive or negative with the proportion of the primary industry and is positively correlated with the rural population density. This study points out that we can improve the RNCUE in Chongqing by optimizing the spatial differentiation control mechanism, clarifying property rights, enhancing liquidity, and strengthening scientific and technological innovation.

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Section: Methodsmentioning

confidence: 99%

Spatiotemporal Evolution and Influencing Factors of the Rural Natural Capital Utilization Efficiency: A Case Study of Chongqing, China

Shi

2022

Land

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“…We adopted a hierarchical clustering method, Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (REDCAP; Guo 2008), to identify clusters based on underlying parameters for the probability distributions used to calculate SPI (gamma), SPEI (Pearson-Type III), and 1-to 4-day precipitation (gamma). REDCAP is a widely used clustering algorithm with previous hydroclimate applications (Gao et al 2018;Yang et al 2020). REDCAP offers a flexible algorithm that overcomes limitations inherent in conventional clustering methods that do not consider spatial information, one of the key factors that shape climate regions.…”

Section: Methodsmentioning

confidence: 99%

Regionalization of Hydroclimate Variables

Carbone¹,

Gao²,

Lu³

2021

Preprint

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We apply a regionalization method to hydroclimate variables using the Parameter-elevation Relationships on Independent Slopes Model (PRISM) temperature and precipitation database. Resulting regions make intuitive sense from the perspective of driving influences on temperature and precipitation averages and anomalies, and are compatible with results from another empirically derived clustering scheme. Regions selected for individual variables show high similarity across different time frames. There is slightly less similarity when comparing regions created for different monthly or daily hydroclimate variables, and relatively low similarity between monthly vs. daily measures. It is unlikely that any one regionalization solution could summarize hydroclimate extremes given the wide range of variables used to describe them, but geographically sensitive datasets like PRISM and flexible algorithms provide useful methods for regionalization that can aid in drought monitoring and forecasting, and with impacts and planning associated with heavy precipitation.

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“…Not long ago, the data’s spatial dimension (geographic dimension) has been incorporated into the Markov chain (MC) framework. Rey ( 2001 ) had developed a spatial Markov chain (MC) methodology for analyzing the spatial–temporal dynamics of random phenomena, which had been applied in a diversity of several fields, including GDP disparities among European regions (Le Gallo, 2004 ), manufacturing in Brazilian regions (Schettini et al, 2011 ), regional wealth disparity in Zhejiang, China (Yue et al, 2014 ), pro-environmental behavior in Italian provinces (Agovino et al, 2016 ), foreign direct investment in Mexico states (Torres Preciado et al, 2017 ), electric vehicle charging in cities (Shepero & Munkhammar, 2018 ), proximity effects in the US on obesity epidemic rates (Agovino et al, 2019 ), air pollution index in Peninsular Malaysia (Alyousifi et al, 2020 ), drought class transitions in Southwest China (Yang et al, 2020 ), COVID-19 dynamics in Asian countries (Dehghan Shabani & Shahnazi, 2020 ). Despite the long history of using this methodology, two interesting problems raise to the surface.…”

Section: Introductionmentioning

confidence: 99%

A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak

Elshehawey

Qian

2023

J. Korean Stat. Soc.

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We propose a new strategy for analyzing the evolution of random phenomena over time and space simultaneously based on the high-order multivariate Markov chains. We develop a novel Markov model of order for chains consisting of possible states to gather parsimony with realism. It can capture negative and positive associations among the chains with only a reduced number of parameters, , remarkably lower than required for the full parameterized model. Our model privileges are enhanced by a Monte Carlo simulation experiment, besides application to analyze the spatial–temporal dynamics for the risk level of a recently global pandemic (COVID-19) outbreak in world health organization (WHO) regions for predicting the risk state of epidemiological prevalence and monitoring infection control.

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On the use of Markov chain models for drought class transition analysis while considering spatial effects

Cited by 16 publications

References 29 publications

Spatiotemporal Evolution and Influencing Factors of the Rural Natural Capital Utilization Efficiency: A Case Study of Chongqing, China

Spatiotemporal Evolution and Influencing Factors of the Rural Natural Capital Utilization Efficiency: A Case Study of Chongqing, China

Regionalization of Hydroclimate Variables

A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak

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