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COVID-19 has brought many unfavorable effects on humankind and taken away many lives. Only by understanding it more profoundly and comprehensively can it be soundly defeated. This paper is dedicated to studying the spatial-temporal characteristics of the epidemic development at the provincial-level in mainland China and the civic-level in Hubei Province. Moreover, a correlation analysis on the possible factors that cause the spatial differences in the epidemic's degree is conducted. After completing these works, three different methods are adopted to fit the daily-change tendencies of the number of confirmed cases in mainland China and Hubei Province. The three methods are the Logical Growth Model (LGM), Polynomial fitting, and Fully Connected Neural Network (FCNN). The analysis results on the spatial-temporal differences and their influencing factors show that: (1) The Chinese government has contained the domestic epidemic in early March 2020, indicating that the number of newly diagnosed cases has almost zero increase since then. (2) Throughout the entire mainland of China, effective manual intervention measures such as community isolation and urban isolation have significantly weakened the influence of the subconscious factors that may impact the spatial differences of the epidemic. (3) The classification results based on the number of confirmed cases also prove the effectiveness of the isolation measures adopted by the governments at all levels in China from another aspect. It is reflected in the small monthly grade changes (even no change) in the provinces of mainland China and the cities in Hubei Province during the study period. Based on the experimental results of curve-fitting and considering the time cost and goodness of fit comprehensively, the Polynomial(Degree = 18) model is recommended in this paper for fitting the daily-change tendency of the number of confirmed cases.
Biao Jin; Jianwan Ji; Wuheng Yang; Zhiqiang Yao; Dandan Huang; Chao Xu. Analysis on the spatio-temporal characteristics of COVID-19 in mainland China. Process Safety and Environmental Protection 2021, 152, 291 -303.
AMA StyleBiao Jin, Jianwan Ji, Wuheng Yang, Zhiqiang Yao, Dandan Huang, Chao Xu. Analysis on the spatio-temporal characteristics of COVID-19 in mainland China. Process Safety and Environmental Protection. 2021; 152 ():291-303.
Chicago/Turabian StyleBiao Jin; Jianwan Ji; Wuheng Yang; Zhiqiang Yao; Dandan Huang; Chao Xu. 2021. "Analysis on the spatio-temporal characteristics of COVID-19 in mainland China." Process Safety and Environmental Protection 152, no. : 291-303.
Measuring the regionally coordinated development degree quantitively at an urban agglomeration scale is vital for regional sustainable development. To date, existing studies mainly utilized statistical data to analyze coordinated development degrees between different subsystems, which failed to measure the development gap of subsystems between cities. This study integrated remote sensing and statistical data to evaluate the development degree from six subsystems. The coordinated index (CI) and coordinated development index (CDI) were then promoted to assess the coordinated degree and coordinated development degree. The main findings were: (1) The coordinated development degree of Jing-Jin-Ji (JJJ) had increased from 0.4616 in 2000 to 0.6099 in 2015, with the corresponding grade improvement from “moderate” to “good”; (2) JJJ and six subsystems’ development degree showed an increasing trend. JJJ’s whole development degree had improved from 0.34 to 0.52, and the grade had changed from “fair” to “moderate”; (3) The coordinated degree of JJJ displayed a “V” shape. However, the coordinated degree was lower in 2015 than in 2000.
Jianwan Ji; Shixin Wang; Yi Zhou; Wenliang Liu; Litao Wang. Spatiotemporal Change and Coordinated Development Analysis of “Population-Society-Economy-Resource-Ecology-Environment” in the Jing-Jin-Ji Urban Agglomeration from 2000 to 2015. Sustainability 2021, 13, 4075 .
AMA StyleJianwan Ji, Shixin Wang, Yi Zhou, Wenliang Liu, Litao Wang. Spatiotemporal Change and Coordinated Development Analysis of “Population-Society-Economy-Resource-Ecology-Environment” in the Jing-Jin-Ji Urban Agglomeration from 2000 to 2015. Sustainability. 2021; 13 (7):4075.
Chicago/Turabian StyleJianwan Ji; Shixin Wang; Yi Zhou; Wenliang Liu; Litao Wang. 2021. "Spatiotemporal Change and Coordinated Development Analysis of “Population-Society-Economy-Resource-Ecology-Environment” in the Jing-Jin-Ji Urban Agglomeration from 2000 to 2015." Sustainability 13, no. 7: 4075.
Exploring the regional eco-environmental quality (EEQ) and its driving factors is of great significance for regional management. Although existing studies have paid much attention to evaluate EEQ, few studies have been performed to investigate the spatiotemporal variations of EEQ and its driving factors in different ecosystem service regions (ESR) at an urban agglomeration scale. In this study, we selected Jing-Jin-Ji urban agglomeration (JJJ) as the study area to evaluate its EEQ, analyze its spatiotemporal variations, and investigate potential driving factors explanatory power based on the geographical detector methods in different ESR during 2001∼2015. The main conclusions were as follows: (1) The EEQ of JJJ had improved from 2001 to 2015, with the average RSEI increased from 0.43 to 0.46; among them, Bashang Plateau and Western Hebei Ecosystem Service Region (BWHE) had the highest RSEI change rate (±26.19%) and the highest NTEDI value (0.13), while Central Hebei Plain Ecosystem Service Region (CHPE) had the lowest RSEI change rate (-5.41%) and the lowest NTEDI value (-0.02). (2) The EEQ of JJJ had strong spatial agglomeration effects, with the global Moran’s I increased from 0.82 to 0.88. Spatially, the LL regions mainly changed into the HH regions in the northwestern part, while in the central and eastern areas, some isolated LL regions displayed an aggregated trend. (3) In terms of the driving factors, soil type and elevation were primary factors in explaining the variations of EEQ. Specifically, natural factors explained the highest variations in BWHE. The interaction of topographical and socio-economic factors had high explanatory power in Yanshan and Taihang Mountain Ecosystem Service Region (YTME) and CHPE; To Bohai and Coastal Ring Ecosystem Service Region (BCRE), the interaction of meteorological and socio-economic factors accounted for the high variations of EEQ. All these findings could provide more valuable advice for relevant policy-makers.
Jianwan Ji; Shixin Wang; Yi Zhou; Wenliang Liu; Litao Wang. Studying the Eco-Environmental Quality Variations of Jing-Jin-Ji Urban Agglomeration and Its Driving Factors in Different Ecosystem Service Regions From 2001 to 2015. IEEE Access 2020, 8, 154940 -154952.
AMA StyleJianwan Ji, Shixin Wang, Yi Zhou, Wenliang Liu, Litao Wang. Studying the Eco-Environmental Quality Variations of Jing-Jin-Ji Urban Agglomeration and Its Driving Factors in Different Ecosystem Service Regions From 2001 to 2015. IEEE Access. 2020; 8 (99):154940-154952.
Chicago/Turabian StyleJianwan Ji; Shixin Wang; Yi Zhou; Wenliang Liu; Litao Wang. 2020. "Studying the Eco-Environmental Quality Variations of Jing-Jin-Ji Urban Agglomeration and Its Driving Factors in Different Ecosystem Service Regions From 2001 to 2015." IEEE Access 8, no. 99: 154940-154952.
Identifying the changes and relationships between regional eco-environment quality and landscape pattern in an urban agglomeration have a great significance in realizing sustainable development goal. However, limited research has been performed to understand the spatiotemporal change of eco-environment quality, the variation of landscape pattern, and their relationship in an urban agglomeration. This study selected the Jing-Jin-Ji (JJJ) urban agglomeration as the study area. A comprehensive index, the remote sensing ecological index (RSEI), was utilized to understand the eco-environment spatiotemporal change and landscape pattern variation at class-level and landscape-level of JJJ during 2001~2015, then, their relationship was explored. The major conclusions were as follows: (1) The average RSEI value of JJJ increased from 0.43 to 0.46, which represented that the eco-environment of JJJ had improved in the fourteen years. Among it, the improved region was mainly located in Zhangjiakou city, while the degraded region was mainly distributed in the eastern Hebei plain. (2) The landscape characteristics of entire JJJ eco-environment were becoming more aggregated, connected, diverse, and regular. However, fair, moderate, and good grades were getting more concentrated and continuous; poor grade indicated a more fragmented and disconnected trend; excellent grade displayed an expanded and concentrated situation. (3) Human factors have an increasing influence on regional eco-environment changes. (4) Fair, moderate, and good grades showed a more dominant and stronger influence on the variation of landscape pattern in JJJ. Specifically, the fair grade had a positive correlation with the variation of landscape pattern, while moderate and good grades had a negative one. All of these conclusions could be valuable information for relevant decision-makers in managing or achieving the optimal eco-environment landscape pattern.
Jianwan Ji; Shixin Wang; Yi Zhou; Wenliang Liu; Litao Wang. Spatiotemporal Change and Landscape Pattern Variation of Eco-Environmental Quality in Jing-Jin-Ji Urban Agglomeration From 2001 to 2015. IEEE Access 2020, 8, 125534 -125548.
AMA StyleJianwan Ji, Shixin Wang, Yi Zhou, Wenliang Liu, Litao Wang. Spatiotemporal Change and Landscape Pattern Variation of Eco-Environmental Quality in Jing-Jin-Ji Urban Agglomeration From 2001 to 2015. IEEE Access. 2020; 8 ():125534-125548.
Chicago/Turabian StyleJianwan Ji; Shixin Wang; Yi Zhou; Wenliang Liu; Litao Wang. 2020. "Spatiotemporal Change and Landscape Pattern Variation of Eco-Environmental Quality in Jing-Jin-Ji Urban Agglomeration From 2001 to 2015." IEEE Access 8, no. : 125534-125548.
The knowledge of farmland dynamics is pivotal to design its management sustainably and enhance food security. Using remote sensing and socioeconomic data, this paper analyses farmland transition, its landscape structure, and the associated drivers in Pingtan Island. The results revealed that the decline of farmland was much faster (317 ha/year) compared with its expansion (106.9 ha/year) during the study period. Across periods, farmland experienced a moderate-to-very rapid intensity of change. It tends to lose than to persist and its stable part decreased continuously. The changed part of farmland was largely attributable to the swap changes than that of the net change. Both its inward and outward conversions were generally limited to a few dominant cover types that include urban, shrub, forest and grassland. In response to farmland shrinkage and the increase of labour wages in other economic sectors, farmers have engaged in off-farm activities to support their economy, besides the compensation they owned from the government. The shape complexity and the variability of size among farmland patches showed a decrease. Except in the first period; however, the level of disintegration among patches increased successively. Both expanded and stable part of farmland exhibited more dispersed spatial configuration than that of the decreased part in the last two decades (1996–2017). Farmland change was significantly influenced by factors such as GDP, wage, per capita GDP, road expansion and the total population in the first component of a regression model (C1) that contains the highest proportion of variance in both dependent and explanatory variables (≥ 72%). Policies of ecological management and economic development also accelerated farmland decline, which calls for its stricter protection policies. The result of this study would serve as the baseline information for farmland management in the study area and as a reference for future studies.
Eshetu Shifaw; Jinming Sha; Xiaomei Li; Zhongcong Bao; Asmamaw Legass; MaryE Belete; Ji Jianwan; Yung-Chih Su; Amsalu K. Addis. Farmland dynamics in Pingtan, China: understanding its transition, landscape structure and driving factors. Environmental Earth Sciences 2019, 78, 535 .
AMA StyleEshetu Shifaw, Jinming Sha, Xiaomei Li, Zhongcong Bao, Asmamaw Legass, MaryE Belete, Ji Jianwan, Yung-Chih Su, Amsalu K. Addis. Farmland dynamics in Pingtan, China: understanding its transition, landscape structure and driving factors. Environmental Earth Sciences. 2019; 78 (17):535.
Chicago/Turabian StyleEshetu Shifaw; Jinming Sha; Xiaomei Li; Zhongcong Bao; Asmamaw Legass; MaryE Belete; Ji Jianwan; Yung-Chih Su; Amsalu K. Addis. 2019. "Farmland dynamics in Pingtan, China: understanding its transition, landscape structure and driving factors." Environmental Earth Sciences 78, no. 17: 535.
With the development of remote sensing techniques and the increasing need for soil contamination monitoring, we estimated soil heavy metal zinc (Zn) content using hyperspectral imaging. Geographically weighted regression (GWR), an extension of the ordinary least squares (OLS) regression framework, was proposed. By estimating a set of parameters for any number of locations in a study area, GWR can probe the spatial heterogeneity in data relationships, whereas the regression parameters of an OLS model are global and aspatially-varied. The objectives of this study were: (1) To find the possible relationships between hyperspectral data and soil Zn content, and (2) to investigate the existence of their spatial heterogeneity. In this study, 67 soil samples collected from Pingtan Island, Fujian Province, China, were used to conduct laboratory hyperspectral modeling for soil Zn content estimation. Four transformations of square root, logarithm, reciprocal of logarithm, and reciprocal, as well as the fractional-order differential operations were applied to increase the amount of reflectance data in which the effective variables for modeling might be involved, and to enhance the spectral characteristics of soil Zn content. To find sensitive variables and to remove redundancy and multicollinearity in the spectra, a data sifting process was applied by selecting wavelengths with local maximum in the absolute values of the correlation coefficients with Zn content in one type of spectral data and by employing Variance Inflation Factors. Since a modeling sample size of 46 is insufficient to construct the appropriate OLS and GWR models, four methods are proposed using all 67 samples to choose explanatory variables. A random process to select 57 samples for modeling and 10 samples for validation was applied to assess model performance, in which the mean verification R2 (Rv2) was used as an indicator. The results show that GWR stepwise regression is the most effective method to select better variables. As the mean Rv2 converges toward the OLS value when the bandwidth of the GWR model increases, the four variables selected by the GWR stepwise regression were used to establish the representative OLS and GWR models. The representative OLS model has the best mean verification effect among all studied models, which had a mean Rv2 value that is 44.6% higher than the OLS model constructed using OLS stepwise regression.
Xue Lin; Yung-Chih Su; Jiali Shang; Jinming Sha; Xiaomei Li; Yang-Yi Sun; Jianwan Ji; Biao Jin. Geographically Weighted Regression Effects on Soil Zinc Content Hyperspectral Modeling by Applying the Fractional-Order Differential. Remote Sensing 2019, 11, 636 .
AMA StyleXue Lin, Yung-Chih Su, Jiali Shang, Jinming Sha, Xiaomei Li, Yang-Yi Sun, Jianwan Ji, Biao Jin. Geographically Weighted Regression Effects on Soil Zinc Content Hyperspectral Modeling by Applying the Fractional-Order Differential. Remote Sensing. 2019; 11 (6):636.
Chicago/Turabian StyleXue Lin; Yung-Chih Su; Jiali Shang; Jinming Sha; Xiaomei Li; Yang-Yi Sun; Jianwan Ji; Biao Jin. 2019. "Geographically Weighted Regression Effects on Soil Zinc Content Hyperspectral Modeling by Applying the Fractional-Order Differential." Remote Sensing 11, no. 6: 636.
The study of cyberspace is faced with the challenge of the data shortage and model verification. This paper proposed a method to explore the regional cyberspace by employing Internet sequential information flows crawled from social network platforms. Compared with previous studies which only use one type of data sources for analysis, the main contribution of this manuscript is adopting the scheme that uses one kind of Internet information flow to extract cyberspace feature while relevant data collected from the other network platform is used for verification. Moreover, starting from measuring the informatization level of a region, a modified gravity model is designed by adding the value of informatization level to the traditional method. Then, an information association matrix based on the improved gravity model is constructed for analyzing the characteristics of cyberspace. To demonstrate the efficiency, Fuzhou city is considered as an interesting regional sample in this paper. The reasonable results indicate that the proposed approach is practical for regional cyberspace.
Biao Jin; Jin-Ming Sha; Jian-Wan Ji; Yi-Su Liu; Wu-Heng Yang. Studying the Regional Cyberspace by Exploiting Internet Sequential Information Flows. Mathematical Problems in Engineering 2018, 2018, 1 -13.
AMA StyleBiao Jin, Jin-Ming Sha, Jian-Wan Ji, Yi-Su Liu, Wu-Heng Yang. Studying the Regional Cyberspace by Exploiting Internet Sequential Information Flows. Mathematical Problems in Engineering. 2018; 2018 ():1-13.
Chicago/Turabian StyleBiao Jin; Jin-Ming Sha; Jian-Wan Ji; Yi-Su Liu; Wu-Heng Yang. 2018. "Studying the Regional Cyberspace by Exploiting Internet Sequential Information Flows." Mathematical Problems in Engineering 2018, no. : 1-13.
This study aims to examine vegetation cover dynamics through quantification of its greenness index, magnitude of change, nature of transition, and fragmentation levels using Landsat images from four periods, 1984, 1996, 2007 and 2017, together with physical and socioeconomic drivers. The results show high spatiotemporal variations of vegetation cover that generally increased from plain to mountain and towards the recent decade (2007–2017). While low vegetation cover (fractional vegetation cover < 50% and NDVI < 0.3) remained to be the dominant cover type in Pingtan, spatiotemporal variations were observed during different time periods. Overall, forestland has increased by 11.43% (3682 ha), and shrub land has expanded by 0.28% (91 ha). In contrast, grassland has reduced by 5.41% (1743 ha). Considering all vegetation types, the expanded area dominated the vegetation cover change resulting in a net gain of 6.3% (2030 ha). The increase in vegetation cover was contributed mainly by farmland. The landscape pattern of each vegetation type shows variation across periods and some metrics have inconsistent trend with change in class size. Generally, patches’ shape complexity, size variability and dominance reduced, while fragmentation level increased during the study period. Slope, elevation, strong wind and policy affected vegetation cover positively. Most socio economic variables showed significant influence (variable importance in the projection > 1) on both woodland (+) and grassland (−) in first latent factor that consists the highest proportion variance in the models. Having quantitative results of vegetation change using several metrics and its change maps would provide valuable information for comprehensive understanding of vegetation cover change and valuable inputs for environmental management planners.
Eshetu Shifaw; Jinming Sha; Xiaomei Li; Zhongcong Bao; Jianwan Ji; Bingchu Chen. Spatiotemporal analysis of vegetation cover (1984–2017) and modelling of its change drivers, the case of Pingtan Island, China. Modeling Earth Systems and Environment 2018, 4, 899 -917.
AMA StyleEshetu Shifaw, Jinming Sha, Xiaomei Li, Zhongcong Bao, Jianwan Ji, Bingchu Chen. Spatiotemporal analysis of vegetation cover (1984–2017) and modelling of its change drivers, the case of Pingtan Island, China. Modeling Earth Systems and Environment. 2018; 4 (3):899-917.
Chicago/Turabian StyleEshetu Shifaw; Jinming Sha; Xiaomei Li; Zhongcong Bao; Jianwan Ji; Bingchu Chen. 2018. "Spatiotemporal analysis of vegetation cover (1984–2017) and modelling of its change drivers, the case of Pingtan Island, China." Modeling Earth Systems and Environment 4, no. 3: 899-917.