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Prof. Yong Ge
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

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0 Information Extraction
0 Image Processing and Analysis
0 spatial statistics
0 Uncertainty Assessment

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Journal article
Published: 22 April 2021 in ISPRS International Journal of Geo-Information
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Geo-environmental factors are believed to be major determinants of rural poverty. However, few studies have quantified the effects of these factors on rural poverty in China. In this paper, we used county-level poverty incidence data and geo-environmental factors to explore spatial patterns of the incidence of poverty using global and local spatial autocorrelation analysis and to investigate the effect of geo-environment factors on rural poverty using a geo-detector model. Our results demonstrated that there was spatial clustering of the incidence of poverty in the study area. The incidence of poverty decreased from south to north and from the east and west to the central area. The incidence of high–high poverty areas was mainly distributed in the southeast of Guizhou Province and the incidence of low–low poverty areas was distributed in the northeast. The results also demonstrated that percentage of effective irrigation on arable land, slope, elevation and vegetation cover were the dominant factors explaining the spatial pattern of poverty. Interaction analysis demonstrated that the slope non-linearly enhanced the percentage of effective irrigation on arable land. Our findings suggested that geo-environment is the fundamental control factor explaining the spatial pattern of rural poverty in China. Through analysis of the impact of the geo-environment on the spatial pattern of poverty, this study provides a reference for effectively implementing targeted alleviation of poverty.

ACS Style

Yong Ge; Zhoupeng Ren; Yangyang Fu. Understanding the Relationship between Dominant Geo-Environmental Factors and Rural Poverty in Guizhou, China. ISPRS International Journal of Geo-Information 2021, 10, 270 .

AMA Style

Yong Ge, Zhoupeng Ren, Yangyang Fu. Understanding the Relationship between Dominant Geo-Environmental Factors and Rural Poverty in Guizhou, China. ISPRS International Journal of Geo-Information. 2021; 10 (5):270.

Chicago/Turabian Style

Yong Ge; Zhoupeng Ren; Yangyang Fu. 2021. "Understanding the Relationship between Dominant Geo-Environmental Factors and Rural Poverty in Guizhou, China." ISPRS International Journal of Geo-Information 10, no. 5: 270.

Preprint content
Published: 15 April 2021
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Worldwide governments have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic, together with the large-scale rollout of vaccines since late 2020. However, the effect of these individual NPI and vaccination measures across space and time has not been sufficiently explored. By the decay ratio in the suppression of COVID-19 infections, we investigated the performance of different NPIs across waves in 133 countries, and their integration with vaccine rollouts in 63 countries as of 25 March 2021. The most effective NPIs were gathering restrictions (contributing 27.83% in the infection rate reductions), facial coverings (16.79%) and school closures (10.08%) in the first wave, and changed to facial coverings (30.04%), gathering restrictions (17.51%) and international travel restrictions (9.22%) in the second wave. The impact of NPIs had obvious spatiotemporal variations across countries by waves before vaccine rollouts, with facial coverings being one of the most effective measures consistently. Vaccinations had gradually contributed to the suppression of COVID-19 transmission, from 0.71% and 0.86% within 15 days and 30 days since Day 12 after vaccination, to 1.23% as of 25 March 2021, while NPIs still dominated the pandemic mitigation. Our findings have important implications for continued tailoring of integrated NPI or NPI-vaccination strategies against future COVID-19 waves or similar infectious diseases.

ACS Style

Yong Ge; Wenbin Zhang; Haiyan Liu; Corrine W Ruktanonchai; Maogui Hu; Xilin Wu; Yongze Song; Nick Ruktanonchai; Wei Yan; Luzhao Feng; Zhongjie Li; Weizhong Yang; Mengxiao Liu; Andrew Tatem; Shengjie Lai. Effects of worldwide interventions and vaccination on COVID-19 between waves and countries. 2021, 1 .

AMA Style

Yong Ge, Wenbin Zhang, Haiyan Liu, Corrine W Ruktanonchai, Maogui Hu, Xilin Wu, Yongze Song, Nick Ruktanonchai, Wei Yan, Luzhao Feng, Zhongjie Li, Weizhong Yang, Mengxiao Liu, Andrew Tatem, Shengjie Lai. Effects of worldwide interventions and vaccination on COVID-19 between waves and countries. . 2021; ():1.

Chicago/Turabian Style

Yong Ge; Wenbin Zhang; Haiyan Liu; Corrine W Ruktanonchai; Maogui Hu; Xilin Wu; Yongze Song; Nick Ruktanonchai; Wei Yan; Luzhao Feng; Zhongjie Li; Weizhong Yang; Mengxiao Liu; Andrew Tatem; Shengjie Lai. 2021. "Effects of worldwide interventions and vaccination on COVID-19 between waves and countries." , no. : 1.

Article
Published: 06 April 2021
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Governments worldwide have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic. However, the effect of these individual NPI measures across space and time has yet to be sufficiently assessed, especially with the increase of policy fatigue and the urge for NPI relaxation in the vaccination era. Using the decay ratio in the suppression of COVID-19 infections, we investigated the changing performance of different NPIs across waves from global and regional levels (in 133 countries) to national and subnational (in the United States of America [USA]) scales before the implementation of mass vaccination. The synergistic effectiveness of all NPIs for reducing COVID-19 infections declined along waves, from 95.4% in the first wave to 56.0% in the third wave recently at the global level and similarly from 83.3% to 58.7% at the USA national level, while it had fluctuating performance across waves on regional and subnational scales. Regardless of geographical scale, gathering restrictions and facial coverings played significant roles in epidemic mitigation before the vaccine rollout. Our findings have important implications for continued tailoring and implementation of NPI strategies, together with vaccination, to mitigate future COVID-19 waves, caused by new variants, and other emerging respiratory infectious diseases.

ACS Style

Yong Ge; Wen-Bin Zhang; Haiyan Liu; Corrine W Ruktanonchai; Maogui Hu; Xilin Wu; Yongze Song; Nick W Ruktanonchai; Wei Yan; Eimear Cleary; Luzhao Feng; Zhongjie Li; Weizhong Yang; Mengxiao Liu; Andrew J Tatem; Jin-Feng Wang; Shengjie Lai. Impacts of worldwide individual non-pharmaceutical interventions on COVID-19 transmission across waves and space. 2021, 1 .

AMA Style

Yong Ge, Wen-Bin Zhang, Haiyan Liu, Corrine W Ruktanonchai, Maogui Hu, Xilin Wu, Yongze Song, Nick W Ruktanonchai, Wei Yan, Eimear Cleary, Luzhao Feng, Zhongjie Li, Weizhong Yang, Mengxiao Liu, Andrew J Tatem, Jin-Feng Wang, Shengjie Lai. Impacts of worldwide individual non-pharmaceutical interventions on COVID-19 transmission across waves and space. . 2021; ():1.

Chicago/Turabian Style

Yong Ge; Wen-Bin Zhang; Haiyan Liu; Corrine W Ruktanonchai; Maogui Hu; Xilin Wu; Yongze Song; Nick W Ruktanonchai; Wei Yan; Eimear Cleary; Luzhao Feng; Zhongjie Li; Weizhong Yang; Mengxiao Liu; Andrew J Tatem; Jin-Feng Wang; Shengjie Lai. 2021. "Impacts of worldwide individual non-pharmaceutical interventions on COVID-19 transmission across waves and space." , no. : 1.

Journal article
Published: 29 March 2021 in BMC Public Health
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Background The effect of the COVID-19 outbreak has led policymakers around the world to attempt transmission control. However, lockdown and shutdown interventions have caused new social problems and designating policy resumption for infection control when reopening society remains a crucial issue. We investigated the effects of different resumption strategies on COVID-19 transmission using a modeling study setting. Methods We employed a susceptible-exposed-infectious-removed model to simulate COVID-19 outbreaks under five reopening strategies based on China’s business resumption progress. The effect of each strategy was evaluated using the peak values of the epidemic curves vis-à-vis confirmed active cases and cumulative cases. Two-sample t-test was performed in order to affirm that the pick values in different scenarios are different. Results We found that a hierarchy-based reopen strategy performed best when current epidemic prevention measures were maintained save for lockdown, reducing the peak number of active cases and cumulative cases by 50 and 44%, respectively. However, the modeled effect of each strategy decreased when the current intervention was lifted somewhat. Additional attention should be given to regions with significant numbers of migrants, as the potential risk of COVID-19 outbreaks amid society reopening is intrinsically high. Conclusions Business resumption strategies have the potential to eliminate COVID-19 outbreaks amid society reopening without special control measures. The proposed resumption strategies focused mainly on decreasing the number of imported exposure cases, guaranteeing medical support for epidemic control, or decreasing active cases.

ACS Style

Yong Ge; Wen-Bin Zhang; Jianghao Wang; Mengxiao Liu; Zhoupeng Ren; Xining Zhang; Chenghu Zhou; Zhaoxing Tian. Effect of different resumption strategies to flatten the potential COVID-19 outbreaks amid society reopens: a modeling study in China. BMC Public Health 2021, 21, 1 -10.

AMA Style

Yong Ge, Wen-Bin Zhang, Jianghao Wang, Mengxiao Liu, Zhoupeng Ren, Xining Zhang, Chenghu Zhou, Zhaoxing Tian. Effect of different resumption strategies to flatten the potential COVID-19 outbreaks amid society reopens: a modeling study in China. BMC Public Health. 2021; 21 (1):1-10.

Chicago/Turabian Style

Yong Ge; Wen-Bin Zhang; Jianghao Wang; Mengxiao Liu; Zhoupeng Ren; Xining Zhang; Chenghu Zhou; Zhaoxing Tian. 2021. "Effect of different resumption strategies to flatten the potential COVID-19 outbreaks amid society reopens: a modeling study in China." BMC Public Health 21, no. 1: 1-10.

Research article
Published: 04 March 2021 in Transactions in GIS
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The quality of volunteered geographic information (VGI) is questionable as it emerges through a diversity of contributors. The reputation of a contributor is increasingly applied to VGI quality assessment and its assurance. Research on how to measure and validate reputation, however, is still required. This study proposes an evaluation‐based weighted PageRank (EWPR) algorithm to provide a ranking metric of reputation. Ranking is established on the basis of the assumptions that: (a) there is an evaluation relationship between VGI contributors; (b) the reputation of a contributor is movable within the VGI community; and (c) highly active contributors are more likely to have high reputation. By means of case studies using OpenStreetMap, two existing methods are compared with the EWPR algorithm. The results show that the algorithms used for web pages and social network participants are applicable to understand the reputation of VGI contributors. This study also applies credibility, based on reputation, as a popular indicator of VGI quality. The high correlation between credibility and VGI characteristics indicates that reputation is useful for dealing with quality variability in VGI.

ACS Style

Die Zhang; Yong Ge; Alfred Stein; Wen‐Bin Zhang. Ranking of VGI contributor reputation using an evaluation‐based weighted pagerank. Transactions in GIS 2021, 1 .

AMA Style

Die Zhang, Yong Ge, Alfred Stein, Wen‐Bin Zhang. Ranking of VGI contributor reputation using an evaluation‐based weighted pagerank. Transactions in GIS. 2021; ():1.

Chicago/Turabian Style

Die Zhang; Yong Ge; Alfred Stein; Wen‐Bin Zhang. 2021. "Ranking of VGI contributor reputation using an evaluation‐based weighted pagerank." Transactions in GIS , no. : 1.

Journal article
Published: 17 January 2021 in Remote Sensing
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Freeze-thawing erosion is mainly distributed in the tundra, which is one of the main factors affecting soil erosion and soil conservation and affects the economic development of relevant countries and regions. The study area was selected to the north of Tanggula Mountain and the south of Bayankera Mountain, to the east of The Qinghai-Tibet Plateau, as the headwaters of the Yangtze River and lancang River. The topography and climate were particularly prone to soil freeze-thawing erosion, and the ecological damage would seriously affect the production and life of people in the whole downstream area. Therefore, based on the analytic hierarchy process (AHP), this paper selects seven evaluation factors to analyze the temporal and spatial characteristics of freeze-thaw erosion in the study area and establishes a comprehensive weight evaluation model for freeze-thaw erosion. The results show that: (1) the evaluation model is effective, and the soil freeze-thawing erosion is strong in the whole research area; (2) the total area of the research area and the freeze-thawing erosion area are 418,843 km2 and 375,514 km2 respectively, the freeze-thawing erosion area accounting for 89.7% of the total research area, and the freeze-thawing erosion intensity ranged from 0.165 to 0.737; (3) the spatial distribution differs significantly, the freeze-thawing erosion intensity is mainly concentrated in high altitude areas, especially in the Tanggula Mountains; (4) slope, poor annual temperature, illumination, altitude and content of sand in soil accelerate soil freeze-thawing erosion, whereas vegetation index does not; wetness index enhanced the influence of vegetation coverage and sand content. (5) this research will provide scientific evidence for protection and restoration of ecological environment in the area.

ACS Style

Yuefeng Lu; Cong Liu; Yong Ge; Yulong Hu; Qiao Wen; Zhongliang Fu; Shaobo Wang; Yong Liu. Spatiotemporal Characteristics of Freeze-Thawing Erosion in the Source Regions of the Chin-Sha, Ya-Lung and Lantsang Rivers on the Basis of GIS. Remote Sensing 2021, 13, 309 .

AMA Style

Yuefeng Lu, Cong Liu, Yong Ge, Yulong Hu, Qiao Wen, Zhongliang Fu, Shaobo Wang, Yong Liu. Spatiotemporal Characteristics of Freeze-Thawing Erosion in the Source Regions of the Chin-Sha, Ya-Lung and Lantsang Rivers on the Basis of GIS. Remote Sensing. 2021; 13 (2):309.

Chicago/Turabian Style

Yuefeng Lu; Cong Liu; Yong Ge; Yulong Hu; Qiao Wen; Zhongliang Fu; Shaobo Wang; Yong Liu. 2021. "Spatiotemporal Characteristics of Freeze-Thawing Erosion in the Source Regions of the Chin-Sha, Ya-Lung and Lantsang Rivers on the Basis of GIS." Remote Sensing 13, no. 2: 309.

Research article
Published: 15 December 2020 in International Journal of Geographical Information Science
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The growth of georeferenced data sources calls for advanced matching methods to improve the reliability of geospatial data processing, such as map conflation. Existing matching methods mainly focus on similarity measures at the entity scale or area scale. A measure that combines entity-scale and area-scale similarities can provide sound matching results under various circumstances. In this paper, we propose a georeferenced-graph model that integrates multiscale similarities for data matching. Specifically, a match of correspondent data objects is identified by the entity-scale measure under the constraint of the area-scale measure. Nodes in the proposed georeferenced graph model represent polygons by their centroids, whereas the links in the graph connect the nodes (i.e. centroids) according to pre-defined rules. Then, we develop an algorithm to identify many-to-many matches. We demonstrate the proposed graph model and algorithm in real-world experiments using OpenStreetMap data. The experimental results show that the proposed georeferenced-graph model can effectively integrate the context and the location-and-form distance of geospatial data matches across different datasets.

ACS Style

Wen-Bin Zhang; Yong Ge; Yee Leung; Yu Zhou. A georeferenced graph model for geospatial data matching by optimising measures of similarity across multiple scales. International Journal of Geographical Information Science 2020, 1 -17.

AMA Style

Wen-Bin Zhang, Yong Ge, Yee Leung, Yu Zhou. A georeferenced graph model for geospatial data matching by optimising measures of similarity across multiple scales. International Journal of Geographical Information Science. 2020; ():1-17.

Chicago/Turabian Style

Wen-Bin Zhang; Yong Ge; Yee Leung; Yu Zhou. 2020. "A georeferenced graph model for geospatial data matching by optimising measures of similarity across multiple scales." International Journal of Geographical Information Science , no. : 1-17.

Original paper
Published: 13 November 2020 in Stochastic Environmental Research and Risk Assessment
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Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. As the virus spread worldwide causing a global pandemic, China reduced transmission at considerable social and economic cost. Post-lockdown, resuming work safely, that is, while avoiding a second epidemic outbreak, is a major challenge. Exacerbating this challenge, Beijing hosts many residents and workers with origins elsewhere, making it a relatively high-risk region in which to resume work. Nevertheless, the step-by-step approach taken by Beijing appears to have been effective so far. To learn from the epidemic progression and return-to-work measures undertaken in Beijing, and to inform efforts to avoid a second outbreak of COVID-19, we simulated the epidemiological progression of COVID-19 in Beijing under the real scenario of multiple stages of resuming work. A new epidemic transmission model was developed from a modified SEIR model for SARS, tailored to the situation of Beijing and fitted using multi-source data. Because of strong spatial heterogeneity amongst the population, socio-economic factors and medical capacity of Beijing, the risk assessment was undertaken spatiotemporally with respect to each district of Beijing. The epidemic simulation confirmed that the policy of resuming work step-by step, as implemented in Beijing, was sufficient to avoid a recurrence of the epidemic. Moreover, because of the structure of the model, the simulation provided insights into the specific factors at play at different stages of resuming work, allowing district-specific recommendations to be made with respect to monitoring at different stages of resuming work . As such, this research provides important lessons for other cities and regions dealing with outbreaks of COVID-19 and implementing return-to-work policies.

ACS Style

Wen-Bin Zhang; Yong Ge; Mengxiao Liu; Peter M. Atkinson; Jinfeng Wang; Xining Zhang; Zhaoxing Tian. Risk assessment of the step-by-step return-to-work policy in Beijing following the COVID-19 epidemic peak. Stochastic Environmental Research and Risk Assessment 2020, 35, 481 -498.

AMA Style

Wen-Bin Zhang, Yong Ge, Mengxiao Liu, Peter M. Atkinson, Jinfeng Wang, Xining Zhang, Zhaoxing Tian. Risk assessment of the step-by-step return-to-work policy in Beijing following the COVID-19 epidemic peak. Stochastic Environmental Research and Risk Assessment. 2020; 35 (2):481-498.

Chicago/Turabian Style

Wen-Bin Zhang; Yong Ge; Mengxiao Liu; Peter M. Atkinson; Jinfeng Wang; Xining Zhang; Zhaoxing Tian. 2020. "Risk assessment of the step-by-step return-to-work policy in Beijing following the COVID-19 epidemic peak." Stochastic Environmental Research and Risk Assessment 35, no. 2: 481-498.

Journal article
Published: 03 November 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The Surface Soil Moisture (SSM) products derived from microwave remote sensing have a coarse spatial resolution, therefore downscaling is required to obtain accurate SSM at high spatial resolution. An effective way to handle the stratified heterogeneity is to model for various stratifications, however the number of samples is often limited under each stratification, influencing the downscaling accuracy. In this study, a machine learning-based geostatistical model, which combines various ancillary information at fine spatial scale, is developed for spatial downscaling. The proposed support vector area-to-area regression kriging (SVATARK) model incorporates support vector regression and area-to-area kriging by considering the nonlinear relationships among variables for various stratifications. SVATARK also considers the change of support problem in the downscaling interpolation process as well as for solving the small sample size in trend prediction. The SVATARK method is evaluated in the Naqu region on the Tibetan Plateau, China to downscale the European Space Agency's (ESA) 25-km-resolution SSM product. The 1-km-resolution SSM predictions have been produced every 8 days over a six-year period (2010-2015). Compared with other five downscaling methods, the downscaled predictions from the SVATARK method performs the best with in-situ observations, resulting in a 24.4 percent reduction in root mean square error with 0.08 m3.m-3 and a 8.2 percent increase in correlation coefficient with 0.72, on average. Additionally, anomalously low SSM values, an indicator of drought, had a record low anomaly in mid-July for 2015, as noted by previous studies, indicating that SVATARK could be utilized for drought monitoring.

ACS Style

Yan Jin; Yong Ge; Yaojie Liu; Yuehong Chen; Haitao Zhang; Gerard B. M. Heuvelink. A Machine Learning-Based Geostatistical Downscaling Method for Coarse-Resolution Soil Moisture Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 1025 -1037.

AMA Style

Yan Jin, Yong Ge, Yaojie Liu, Yuehong Chen, Haitao Zhang, Gerard B. M. Heuvelink. A Machine Learning-Based Geostatistical Downscaling Method for Coarse-Resolution Soil Moisture Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):1025-1037.

Chicago/Turabian Style

Yan Jin; Yong Ge; Yaojie Liu; Yuehong Chen; Haitao Zhang; Gerard B. M. Heuvelink. 2020. "A Machine Learning-Based Geostatistical Downscaling Method for Coarse-Resolution Soil Moisture Products." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 1025-1037.

Chapter
Published: 11 August 2020 in Emerging Topics in Statistics and Biostatistics
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China is the most populous country in the world, especially a large number of impoverished people concentrated in rural area. The uneven distribution of impoverished people in China has made it necessary to investigate its spatial patterns and driving forces. In this chapter, several methods of spatial statistics that have been employed to poverty issues analysis were reviewed. These methods were mainly used to investigate the driving forces, spatial patterns, and spatial temporal changes of poverty. Three case studies of China were then conducted to provide the detail illustrations of the application of the methods.

ACS Style

Yong Ge; Shan Hu; Mengxiao Liu. Applications of Spatial Statistics in Poverty Alleviation in China. Emerging Topics in Statistics and Biostatistics 2020, 367 -392.

AMA Style

Yong Ge, Shan Hu, Mengxiao Liu. Applications of Spatial Statistics in Poverty Alleviation in China. Emerging Topics in Statistics and Biostatistics. 2020; ():367-392.

Chicago/Turabian Style

Yong Ge; Shan Hu; Mengxiao Liu. 2020. "Applications of Spatial Statistics in Poverty Alleviation in China." Emerging Topics in Statistics and Biostatistics , no. : 367-392.

Other
Published: 26 June 2020
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The effect of the COVID-19 outbreak has led policymakers around the world to attempt transmission control. However, lockdown and shutdown interventions have caused new social problems and designating policy resumption for infection control when reopening society remains a crucial issue. We investigated the effects of different resumption strategies on COVID-19 transmission using a modeling study setting. We employed a susceptible-exposed-infectious-removed model to simulate COVID-19 outbreaks under five reopening strategies based on China’s business resumption progress. The effect of each strategy was evaluated using the peak values of the epidemic curves vis-à-vis confirmed active cases and cumulative cases. We found that a hierarchy-based reopen strategy performed best when current epidemic prevention measures were maintained save for lockdown, reducing the peak number of active cases and cumulative cases by 50% and 44%, respectively. However, the modeled effect of each strategy decreased when the current intervention was lifted somewhat. Additional attention should be given to regions with significant numbers of migrants, as the potential risk of COVID-19 outbreaks amid society reopening is intrinsically high. Business resumption strategies have the potential to eliminate COVID-19 outbreaks amid society reopening without special control measures. The proposed resumption strategies focused mainly on decreasing the number of imported exposure cases, guaranteeing medical support for epidemic control, or decreasing active cases.

ACS Style

Yong Ge; Wenbin Zhang; Jianghao Wang; Mengxiao Liu; Zhoupeng Ren; Xining Zhang; Chenghu Zhou; Zhaoxing Tian. Effect of different resumption strategies to flatten the potential COVID-19 outbreaks amid society reopens: a modeling study. 2020, 1 .

AMA Style

Yong Ge, Wenbin Zhang, Jianghao Wang, Mengxiao Liu, Zhoupeng Ren, Xining Zhang, Chenghu Zhou, Zhaoxing Tian. Effect of different resumption strategies to flatten the potential COVID-19 outbreaks amid society reopens: a modeling study. . 2020; ():1.

Chicago/Turabian Style

Yong Ge; Wenbin Zhang; Jianghao Wang; Mengxiao Liu; Zhoupeng Ren; Xining Zhang; Chenghu Zhou; Zhaoxing Tian. 2020. "Effect of different resumption strategies to flatten the potential COVID-19 outbreaks amid society reopens: a modeling study." , no. : 1.

Journal article
Published: 25 June 2020 in Spatial Statistics
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Identifying poverty determinants in a region is crucial for taking effective poverty reduction measures. This paper utilizes two variable importance analysis methods to identify the relative importance of different geographic factors to explain the spatial distribution of poverty: the Lindeman, Merenda, and Gold (LMG) method used in multiple linear regression (MLR) and variable importance used in random forest (RF) machine learning. A case study was conducted in Yunyang, a poverty-stricken county in China, to evaluate the performances of the two methods for identifying village-level poverty determinants. The results indicated that: (1) MLR and RF had similar explanation accuracy; (2) LMG and RF were consistent in the three main determinants of poverty; (3) LMG better identified the importance of variables that were highly related to poverty but correlated with other variables, while RF better identified the non-linear relationships between poverty and explanatory variables; (4) accessibility metrics are the most important variables influencing poverty in Yunyang and have a linear relationship with poverty.

ACS Style

Mengxiao Liu; Shan Hu; Yong Ge; Gerard B.M. Heuvelink; Zhoupeng Ren; Xiaoran Huang. Using multiple linear regression and random forests to identify spatial poverty determinants in rural China. Spatial Statistics 2020, 42, 100461 .

AMA Style

Mengxiao Liu, Shan Hu, Yong Ge, Gerard B.M. Heuvelink, Zhoupeng Ren, Xiaoran Huang. Using multiple linear regression and random forests to identify spatial poverty determinants in rural China. Spatial Statistics. 2020; 42 ():100461.

Chicago/Turabian Style

Mengxiao Liu; Shan Hu; Yong Ge; Gerard B.M. Heuvelink; Zhoupeng Ren; Xiaoran Huang. 2020. "Using multiple linear regression and random forests to identify spatial poverty determinants in rural China." Spatial Statistics 42, no. : 100461.

Research article
Published: 10 March 2020 in International Journal of Geographical Information Science
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This paper proposes a new classification method for spatial data by adjusting prior class probabilities according to local spatial patterns. First, the proposed method uses a classical statistical classifier to model training data. Second, the prior class probabilities are estimated according to the local spatial pattern and the classifier for each unseen object is adapted using the estimated prior probability. Finally, each unseen object is classified using its adapted classifier. Because the new method can be coupled with both generative and discriminant statistical classifiers, it performs generally more accurately than other methods for a variety of different spatial datasets. Experimental results show that this method has a lower prediction error than statistical classifiers that take no spatial information into account. Moreover, in the experiments, the new method also outperforms spatial auto-logistic regression and Markov random field-based methods when an appropriate estimate of local prior class distribution is used.

ACS Style

Hexiang Bai; Feng Cao; M. Peter Atkinson; Qian Chen; Jinfeng Wang; Yong Ge. Incorporating spatial association into statistical classifiers: local pattern-based prior tuning. International Journal of Geographical Information Science 2020, 34, 2077 -2114.

AMA Style

Hexiang Bai, Feng Cao, M. Peter Atkinson, Qian Chen, Jinfeng Wang, Yong Ge. Incorporating spatial association into statistical classifiers: local pattern-based prior tuning. International Journal of Geographical Information Science. 2020; 34 (10):2077-2114.

Chicago/Turabian Style

Hexiang Bai; Feng Cao; M. Peter Atkinson; Qian Chen; Jinfeng Wang; Yong Ge. 2020. "Incorporating spatial association into statistical classifiers: local pattern-based prior tuning." International Journal of Geographical Information Science 34, no. 10: 2077-2114.

Journal article
Published: 24 February 2020 in ISPRS International Journal of Geo-Information
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Land use and land cover change (LULCC) has directly played an important role in the observed climate change. In this paper, we considered Dujiangyan City and its environs (DCEN) to study the future scenario in the years 2025, 2030, and 2040 based on the 2018 simulation results from 2007 and 2018 LULC maps. This study evaluates the spatial and temporal variations of future LULCC, including the future potential landscape risk (FPLR) area of the 2008 great (8.0 Mw) earthquake of south-west China. The Cellular automata–Markov chain (CA-Markov) model and multicriteria based analytical hierarchy process (MC-AHP) approach have been considered using the integration of remote sensing and GIS techniques. The analysis shows future LULC scenario in the years 2025, 2030, and 2040 along with the FPLR pattern. Based on the results of the future LULCC and FPLR scenarios, we have provided suggestions for the development in the close proximity of the fault lines for the future strong magnitude earthquakes. Our results suggest a better and safe planning approach in the Belt and Road Corridor (BRC) of China to control future Silk-Road Disaster, which will also be useful to urban planners for urban development in a safe and sustainable manner.

ACS Style

Biswajit Nath; Zhihua Wang; Yong Ge; Kamrul Islam; Ramesh P. Singh; Zheng Niu. Land Use and Land Cover Change Modeling and Future Potential Landscape Risk Assessment Using Markov-CA Model and Analytical Hierarchy Process. ISPRS International Journal of Geo-Information 2020, 9, 134 .

AMA Style

Biswajit Nath, Zhihua Wang, Yong Ge, Kamrul Islam, Ramesh P. Singh, Zheng Niu. Land Use and Land Cover Change Modeling and Future Potential Landscape Risk Assessment Using Markov-CA Model and Analytical Hierarchy Process. ISPRS International Journal of Geo-Information. 2020; 9 (2):134.

Chicago/Turabian Style

Biswajit Nath; Zhihua Wang; Yong Ge; Kamrul Islam; Ramesh P. Singh; Zheng Niu. 2020. "Land Use and Land Cover Change Modeling and Future Potential Landscape Risk Assessment Using Markov-CA Model and Analytical Hierarchy Process." ISPRS International Journal of Geo-Information 9, no. 2: 134.

Journal article
Published: 02 August 2019 in Remote Sensing
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Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) has been shown to be effective in capturing the spatial characteristics of geographic objects and extrapolating calibrated methods to other study areas, it may be a useful approach to overcome limitations of current SRM methods. In this paper, a new SRM method based on the CNN (SRMCNN) is proposed and tested. Specifically, an encoder-decoder CNN is used to model the nonlinear relationship between coarse remote sensing images and fine-scale land cover maps. Two real-image experiments were conducted to analyze the effectiveness of the proposed method. The results demonstrate that the overall accuracy of the proposed SRMCNN method was 3% to 5% higher than that of two existing SRM methods. Moreover, the proposed SRMCNN method was validated by visualizing output features and analyzing the performance of different geographic objects.

ACS Style

Yuanxin Jia; Yong Ge; Yuehong Chen; Sanping Li; Gerard B.M. Heuvelink; Feng Ling. Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network. Remote Sensing 2019, 11, 1815 .

AMA Style

Yuanxin Jia, Yong Ge, Yuehong Chen, Sanping Li, Gerard B.M. Heuvelink, Feng Ling. Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network. Remote Sensing. 2019; 11 (15):1815.

Chicago/Turabian Style

Yuanxin Jia; Yong Ge; Yuehong Chen; Sanping Li; Gerard B.M. Heuvelink; Feng Ling. 2019. "Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network." Remote Sensing 11, no. 15: 1815.

Journal article
Published: 10 July 2019 in Remote Sensing of Environment
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China aims to end absolute poverty by 2020. In pursuit of this goal, a series of poverty reduction policies and measures have been proposed. As a vital element of poverty reduction, land use in China's poverty-stricken areas also undergone great changes accordingly. However, the land use change patterns in these areas are not well understood. It's necessary to analyze the spatial-temporal land use change patterns to provide data that support poverty alleviation programs. In this study, we proposed a framework for mapping annual land use changes in China's poverty-stricken areas from 2013 to 2018. The Landsat 8 surface reflectance datasets from 2013 to 2018 (available on Google Earth Engine) were utilized to detect the changes in arable land, built-up land, water, vegetation, and un-used land. The land use transition matrix was computed to describe characteristics of the transition, and a Bayesian hierarchical model was employed to investigate the spatial-temporal land use change patterns. Our results demonstrated that the arable land continuously decreased over the study period, while built-up land and vegetation gradually expanded. The primary land use transition occurred between the arable land and vegetation. The local trends of each county indicated obvious regional differences of land use change. Moreover, significant differences existed between deep poverty-stricken counties and normal poverty-stricken counties on arable land and built-up land change, indicating that more intense human construction activities in normal poverty-stricken areas. The annual land use mapping results generated for poverty-stricken areas, along with further analysis of overall temporal change and local change trends, could provide a better understanding of land use changes and regional differences in China's poverty-stricken areas and promote the poverty reduction and sustainable development in those areas.

ACS Style

Yong Ge; Shan Hu; Zhoupeng Ren; Yuanxin Jia; Jianghao Wang; Mengxiao Liu; Die Zhang; Weiheng Zhao; Yaowen Luo; Yangyang Fu; Hexiang Bai; Yuehong Chen. Mapping annual land use changes in China's poverty-stricken areas from 2013 to 2018. Remote Sensing of Environment 2019, 232, 111285 .

AMA Style

Yong Ge, Shan Hu, Zhoupeng Ren, Yuanxin Jia, Jianghao Wang, Mengxiao Liu, Die Zhang, Weiheng Zhao, Yaowen Luo, Yangyang Fu, Hexiang Bai, Yuehong Chen. Mapping annual land use changes in China's poverty-stricken areas from 2013 to 2018. Remote Sensing of Environment. 2019; 232 ():111285.

Chicago/Turabian Style

Yong Ge; Shan Hu; Zhoupeng Ren; Yuanxin Jia; Jianghao Wang; Mengxiao Liu; Die Zhang; Weiheng Zhao; Yaowen Luo; Yangyang Fu; Hexiang Bai; Yuehong Chen. 2019. "Mapping annual land use changes in China's poverty-stricken areas from 2013 to 2018." Remote Sensing of Environment 232, no. : 111285.

Review article
Published: 09 July 2019 in Earth-Science Reviews
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The properties of geographical phenomena vary with changes in the scale of measurement. The information observed at one scale often cannot be directly used as information at another scale. Scaling addresses these changes in properties in relation to the scale of measurement, and plays an important role in Earth sciences by providing information at the scale of interest, which may be required for a range of applications, and may be useful for inferring geographical patterns and processes. This paper presents a review of geospatial scaling methods for Earth science data. Based on spatial properties, we propose a methodological framework for scaling addressing upscaling, downscaling and side-scaling. This framework combines scale-independent and scale-dependent properties of geographical variables. It allows treatment of the varying spatial heterogeneity of geographical phenomena, combines spatial autocorrelation and heterogeneity, addresses scale-independent and scale-dependent factors, explores changes in information, incorporates geospatial Earth surface processes and uncertainties, and identifies the optimal scale(s) of models. This study shows that the classification of scaling methods according to various heterogeneities has great potential utility as an underpinning conceptual basis for advances in many Earth science research domains.

ACS Style

Yong Ge; Yan Jin; Alfred Stein; Yuehong Chen; Jianghao Wang; Jinfeng Wang; Qiuming Cheng; Hexiang Bai; Mengxiao Liu; Peter M. Atkinson. Principles and methods of scaling geospatial Earth science data. Earth-Science Reviews 2019, 197, 102897 .

AMA Style

Yong Ge, Yan Jin, Alfred Stein, Yuehong Chen, Jianghao Wang, Jinfeng Wang, Qiuming Cheng, Hexiang Bai, Mengxiao Liu, Peter M. Atkinson. Principles and methods of scaling geospatial Earth science data. Earth-Science Reviews. 2019; 197 ():102897.

Chicago/Turabian Style

Yong Ge; Yan Jin; Alfred Stein; Yuehong Chen; Jianghao Wang; Jinfeng Wang; Qiuming Cheng; Hexiang Bai; Mengxiao Liu; Peter M. Atkinson. 2019. "Principles and methods of scaling geospatial Earth science data." Earth-Science Reviews 197, no. : 102897.

Editorial
Published: 07 December 2018 in Remote Sensing
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Images obtained from satellites are of an increasing resolution.

ACS Style

Alfred Stein; Yong Ge; Inger Fabris-Rotelli. Introduction to the Special Issue “Uncertainty in Remote Sensing Image Analysis”. Remote Sensing 2018, 10, 1975 .

AMA Style

Alfred Stein, Yong Ge, Inger Fabris-Rotelli. Introduction to the Special Issue “Uncertainty in Remote Sensing Image Analysis”. Remote Sensing. 2018; 10 (12):1975.

Chicago/Turabian Style

Alfred Stein; Yong Ge; Inger Fabris-Rotelli. 2018. "Introduction to the Special Issue “Uncertainty in Remote Sensing Image Analysis”." Remote Sensing 10, no. 12: 1975.

Journal article
Published: 01 December 2018 in Chinese Physics B
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ACS Style

An-Ru Hou; Wen-Ting Gao; Jiao Qian; Hong-Xiang Sun; Yong Ge; Shou-Qi Yuan; Qiao-Rui Si; Xiao-Jun Liu. Thermoacoustic-reflected focusing lens based on acoustic Bessel-like beam with phase manipulation. Chinese Physics B 2018, 27, 1 .

AMA Style

An-Ru Hou, Wen-Ting Gao, Jiao Qian, Hong-Xiang Sun, Yong Ge, Shou-Qi Yuan, Qiao-Rui Si, Xiao-Jun Liu. Thermoacoustic-reflected focusing lens based on acoustic Bessel-like beam with phase manipulation. Chinese Physics B. 2018; 27 (12):1.

Chicago/Turabian Style

An-Ru Hou; Wen-Ting Gao; Jiao Qian; Hong-Xiang Sun; Yong Ge; Shou-Qi Yuan; Qiao-Rui Si; Xiao-Jun Liu. 2018. "Thermoacoustic-reflected focusing lens based on acoustic Bessel-like beam with phase manipulation." Chinese Physics B 27, no. 12: 1.

Articles
Published: 15 October 2018 in International Journal of Geographical Information Science
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When classical rough set (CRS) theory is used to analyze spatial data, there is an underlying assumption that objects in the universe are completely randomly distributed over space. However, this assumption conflicts with the actual situation of spatial data. Generally, spatial heterogeneity and spatial autocorrelation are two important characteristics of spatial data. These two characteristics are important information sources for improving the modeling accuracy of spatial data. This paper extends CRS theory by introducing spatial heterogeneity and spatial autocorrelation. This new extension adds spatial adjacency information into the information table. Many fundamental concepts in CRS theory, such as the indiscernibility relation, equivalent classes, and lower and upper approximations, are improved by adding spatial adjacency information into these concepts. Based on these fundamental concepts, a new reduct and an improved rule matching method are proposed. The new reduct incorporates spatial heterogeneity in selecting the feature subset which can preserve the local discriminant power of all features, and the new rule matching method uses spatial autocorrelation to improve the classification ability of rough set-based classifiers. Experimental results show that the proposed extension significantly increased classification or segmentation accuracy, and the spatial reduct required much less time than classical reduct.

ACS Style

Hexiang Bai; Deyu Li; Yong Ge; Jinfeng Wang. A spatial heterogeneity-based rough set extension for spatial data. International Journal of Geographical Information Science 2018, 33, 240 -268.

AMA Style

Hexiang Bai, Deyu Li, Yong Ge, Jinfeng Wang. A spatial heterogeneity-based rough set extension for spatial data. International Journal of Geographical Information Science. 2018; 33 (2):240-268.

Chicago/Turabian Style

Hexiang Bai; Deyu Li; Yong Ge; Jinfeng Wang. 2018. "A spatial heterogeneity-based rough set extension for spatial data." International Journal of Geographical Information Science 33, no. 2: 240-268.