Spatial and temporal characteristics and evolutionary prediction of urban health development efficiency in China: Based on super-efficiency SBM model and spatial Markov chain model

Wang, Yujie; Chen, Feiyu; Fang, Wei; Yang, Menghua; Gu, Xiao; Sun, Qingqing; Wang, Xinru

doi:10.1016/j.ecolind.2023.109985

Cited by 27 publications

(7 citation statements)

References 36 publications

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“…In order to predict China's urban industrial comprehensive water use efficiency, this paper introduces the adjacency weight matrix into the spatial Markov chain to calculate the spatial lag value, so as to show the relationship between spatial units. The spatial lag type of a spatial unit is determined by its spatial lag value, which is the spatially weighted average value of the neighbor area attribute value of the spatial unit, as shown in Equation (10) below [ 45 , 46 ].

In the above equation (10) , Y i is the attribute value of space unit; W ij is the element in row i and column j of spatial weight matrix W , that is, the relation matrix between space unit and neighbor region.…”

Section: Methodsmentioning

confidence: 99%

Spatial difference analysis and dynamic evolution prediction of urban industrial integrated water use efficiency in China

Jingfeng,

Fan,

Yan

2024

Heliyon

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Section: Methodsmentioning

confidence: 99%

Spatial difference analysis and dynamic evolution prediction of urban industrial integrated water use efficiency in China

Jingfeng,

Fan,

Yan

2024

Heliyon

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“…The Markov chain is a mathematical analysis tool to analyze the stochastic process [15]. This method treats the evolution of a system or phenomenon at different times as a Markov process, characterizing the dynamic changes in a system or phenomenon by using the transfer probability matrix [18,19]. Compared with the analysis methods of spatial and temporal distribution characteristics of elements, such as Thiel index, Sill coefficient, and kernel density estimation [20], the Markov chain model can reflect the dynamic change in elements and predict future distribution [21].…”

Section: Spatial Markov Chain Modelmentioning

confidence: 99%

Regional High-Rise Building Fire Risk Assessment Based on the Spatial Markov Chain Model and an Indicator System

Zhang,

Wang,

Wang

et al. 2024

Fire

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High-rise buildings (HRBs) are prone to high fire hazards due to their high occupant density, limited evacuation routes, and high fire load. The indicator system method, as a systematic evaluation method, is widely applied to assess HRB fire risk. However, the method is subjective because the determination of the indicator weights mainly relies on expert experience. In order to reduce the subjectivity of the indicator system method in assessing the fire risk of HRBs, this study proposes a new assessment method by combining the spatial Markov chain model and the indicator system method. In this new method, fire occurrence probability is calculated by the spatial Markov chain model using historical HRB fire accident data. An indicator system is built to characterize the fire consequence by the structure entropy weight method. Subsequently, HRBs in Beijing are used as a case to illustrate the practicality of this approach. Firstly, the spatial Markov chain model is trained and validated using the chi-square goodness-of-fit test based on fire accident data from 2018 to 2023 in Beijing. It was found that the best performance was achieved with the monthly period and the four-state. Then, the distribution of regional fire occurrence probability in April was predicted based on fire accident data in March 2023 in Beijing. It showed that areas with higher fire occurrence probability are mainly located in the central region, especially in the I District. Then, the indicator system was used to evaluate the HRB fire consequence in the I District. The assessment results showed that the areas with more severe fire consequences are mainly located in the II and IV Districts, due to the poor performance of the fire system or the absence of fire protection systems. Coupling the fire occurrence probability and its consequences shows that HRBs with higher fire risk are mainly located in area II and should be carefully supervised for fire management. This developed method can provide some insights into the fire safety management of HRBs and the layout of the fire stations.

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“…The Markov model is also known as a Markov chain. It can use the empirical transition probability of the existing discrete state of the system to simulate and predict future development [34]. Markov chains have "memoryless" properties, meaning that the probability distribution of the system state at time t + 1 is only related to the state at time t, and is independent of the state before time t. It can be expressed by the formulas below [35]:…”

Section: Ca-markov Modelmentioning

confidence: 99%

Vulnerability Assessment and Future Prediction of Urban Waterlogging—A Case Study of Fuzhou

Wang,

Zhang,

et al. 2023

Water

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Evaluating waterlogging vulnerability and analyzing its characteristics and future trends can provide scientific support for urban disaster prevention and reduction. For this study, taking Fuzhou as an example, an urban waterlogging vulnerability assessment system was constructed from the three dimensions of exposure, sensitivity, and adaptive capacity. The entropy method was used to evaluate urban waterlogging vulnerability in Fuzhou during 2014–2020. The use of CA–Markov to predict waterlogging vulnerability in 2023, 2026, and 2029 in Fuzhou is an important innovation reported in this paper. Study results showed that: (1) Vulnerability to waterlogging in Fuzhou follows a gradually decreasing “center-southeast” distribution pattern, with Level 5 areas mainly located in Cangshan District, Gulou District, and Taijiang District. (2) Changes in waterlogging vulnerability in Fuzhou from 2014 to 2020 can be divided into five change modes, with changing areas, mainly of the late-change type, accounting for 14.13% of the total area. (3) Prediction accuracy verification shows that the CA–Markov model is suitable for predicting waterlogging vulnerability in Fuzhou with high accuracy and a kappa coefficient of 0.9079. (4) From 2020 to 2029, the vulnerability level of the eastern coastal region of Fuzhou is expected to generally increase, and the vulnerability degree will continue to deteriorate. The proportion of Level 5 vulnerable areas will increase by 4.5%, and the growth rate will increase faster and faster with the passage of time.

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Spatial and temporal characteristics and evolutionary prediction of urban health development efficiency in China: Based on super-efficiency SBM model and spatial Markov chain model

Cited by 27 publications

References 36 publications

Spatial difference analysis and dynamic evolution prediction of urban industrial integrated water use efficiency in China

Spatial difference analysis and dynamic evolution prediction of urban industrial integrated water use efficiency in China

Regional High-Rise Building Fire Risk Assessment Based on the Spatial Markov Chain Model and an Indicator System

Vulnerability Assessment and Future Prediction of Urban Waterlogging—A Case Study of Fuzhou

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