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Understanding geospatial impacts of multi-sourced drivers on the tourism industry is of great significance for formulating tourism development policies tailored to regional-specific needs. To date, no research in China has explored the combined impacts of socioeconomic and environmental drivers on city-level tourism from a spatiotemporal heterogeneous perspective. We collected the total tourism revenue indicator and 30 potential influencing factors from 343 cities across China during 2008–2017. Three mainstream regressions and an emerging local spatiotemporal regression named the Bayesian spatiotemporally varying coefficients (Bayesian STVC) model were constructed to investigate the global-scale stationary and local-scale spatiotemporal nonstationary relationships between city-level tourism and various vital drivers. The Bayesian STVC model achieved the best model performance. Globally, eight socioeconomic and environmental factors, average wage (coefficient: 0.47, 95% credible intervals: 0.43–0.51), employed population (−0.14, −0.17–−0.11), GDP per capita (0.47, 0.42–0.52), population density (0.21, 0.16–0.27), night-time light index (−0.01, −0.08–0.05), slope (0.10, 0.06–0.14), vegetation index (0.66, 0.63–0.70), and road network density (0.34, 0.29–0.38), were identified to have nonlinear effects on tourism. Temporally, the main drivers might have gradually changed from the local macro-economic level, population density, and natural environment conditions to the individual economic level over the last decade. Spatially, city-specific dynamic maps of tourism development and geographically clustered influencing maps for eight drivers were produced. In 2017, China formed four significant city-level tourism industry clusters (hot spots, 90% confidence), the locations of which coincide with China’s top four urban agglomerations. Our local spatiotemporal analysis framework for geographical tourism data is expected to provide insights into adjusting regional measures to local conditions and temporal variations in broader social and natural sciences.
Xu Zhang; Chao Song; Chengwu Wang; Yili Yang; Zhoupeng Ren; Mingyu Xie; Zhangying Tang; Honghu Tang. Socioeconomic and Environmental Impacts on Regional Tourism across Chinese Cities: A Spatiotemporal Heterogeneous Perspective. ISPRS International Journal of Geo-Information 2021, 10, 410 .
AMA StyleXu Zhang, Chao Song, Chengwu Wang, Yili Yang, Zhoupeng Ren, Mingyu Xie, Zhangying Tang, Honghu Tang. Socioeconomic and Environmental Impacts on Regional Tourism across Chinese Cities: A Spatiotemporal Heterogeneous Perspective. ISPRS International Journal of Geo-Information. 2021; 10 (6):410.
Chicago/Turabian StyleXu Zhang; Chao Song; Chengwu Wang; Yili Yang; Zhoupeng Ren; Mingyu Xie; Zhangying Tang; Honghu Tang. 2021. "Socioeconomic and Environmental Impacts on Regional Tourism across Chinese Cities: A Spatiotemporal Heterogeneous Perspective." ISPRS International Journal of Geo-Information 10, no. 6: 410.
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.
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 StyleYong 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 StyleYong 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.
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.
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 StyleYong 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 StyleYong 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.
Recent studies have reported a variety of health consequences of climate change. However, the vulnerability of individuals and cities to climate change remains to be evaluated. We project the excess cause-, age-, region-, and education-specific mortality attributable to future high temperatures in 161 Chinese districts/counties using 28 global climate models (GCMs) under two representative concentration pathways (RCPs). To assess the influence of population ageing on the projection of future heat-related mortality, we further project the age-specific effect estimates under five shared socioeconomic pathways (SSPs). Heat-related excess mortality is projected to increase from 1.9% (95% eCI: 0.2–3.3%) in the 2010s to 2.4% (0.4–4.1%) in the 2030 s and 5.5% (0.5–9.9%) in the 2090 s under RCP8.5, with corresponding relative changes of 0.5% (0.0–1.2%) and 3.6% (−0.5–7.5%). The projected slopes are steeper in southern, eastern, central and northern China. People with cardiorespiratory diseases, females, the elderly and those with low educational attainment could be more affected. Population ageing amplifies future heat-related excess deaths 2.3- to 5.8-fold under different SSPs, particularly for the northeast region. Our findings can help guide public health responses to ameliorate the risk of climate change.
Jun Yang; Maigeng Zhou; Zhoupeng Ren; Mengmeng Li; Boguang Wang; De Li Liu; Chun-Quan Ou; Peng Yin; Jimin Sun; Shilu Tong; Hao Wang; Chunlin Zhang; Jinfeng Wang; Yuming Guo; Qiyong Liu. Projecting heat-related excess mortality under climate change scenarios in China. Nature Communications 2021, 12, 1 -11.
AMA StyleJun Yang, Maigeng Zhou, Zhoupeng Ren, Mengmeng Li, Boguang Wang, De Li Liu, Chun-Quan Ou, Peng Yin, Jimin Sun, Shilu Tong, Hao Wang, Chunlin Zhang, Jinfeng Wang, Yuming Guo, Qiyong Liu. Projecting heat-related excess mortality under climate change scenarios in China. Nature Communications. 2021; 12 (1):1-11.
Chicago/Turabian StyleJun Yang; Maigeng Zhou; Zhoupeng Ren; Mengmeng Li; Boguang Wang; De Li Liu; Chun-Quan Ou; Peng Yin; Jimin Sun; Shilu Tong; Hao Wang; Chunlin Zhang; Jinfeng Wang; Yuming Guo; Qiyong Liu. 2021. "Projecting heat-related excess mortality under climate change scenarios in China." Nature Communications 12, no. 1: 1-11.
Living closer to greenness were thought to benefit various health outcomes. We aimed to assess the association between residential greenness and mortality among patients undergoing multidrug resistant tuberculosis (MDR-TB) treatment. We enrolled all local MDR-TB patients reported in Zhejiang, China from 2009 to 2017 and followed them throughout the treatment. We calculated the contemporaneous normalized difference vegetation index (NDVI) in the 250 and 500 m radius around patient’s residence. Cox proportional hazards regression models with time-varying NDVI were used to assess the impact of greenness exposure on all-cause mortality during MDR-TB treatment, adjusting for potential individual and contextual covariates. We ascertained 1,621 active MDR-TB cases, which contributed 3036 person-years at risk with an average follow-up of 684 days (s.d. 149 days) per patient. Among them, there were 163 deaths during follow-up, representing a crude mortality rate of 537 deaths per 10,000 person-years. Patients exposed to the second quintile (Q2) of greenness within the 500 m buffer had around 64% reduced mortality risk over the lowest quintile of greenness with hazard ratio (HR) = 0.364 (95% CI: 0.109–1.22). In lower nighttime light (NTL) areas, the hazard ratios (HR) per quintile increase in NDVI within the 500 m buffer were Q2: 0.35 (95% CI: 0.10–1.18), Q3: 0.24 (95% CI: 0.09–0.66), Q4: 0.26 (95% CI: 0.10–0.69), and Q5: 0.26 (95% CI: 0.10–0.71) relevant to the lowest quintile Q1, with a trend of p-value ≤0.01. Patients who were female, younger (<60 years), resided in urban areas, or had high PM2.5 (i.e. particles with diagram ≤2.5 μm) exposure were more likely to benefit from greenness exposure. Associations were neither observed with NDVI in the 250 m buffer nor for patients living in higher NTL areas. There was a non-linear exposure-response relationship between greenness and deaths with p-value ≤0.05. Increasing greenness exposure along with medical treatment reduces all-cause mortality among patients living in lower NTL areas.
Erjia Ge; Jianhui Gao; Zhoupeng Ren; Xin Liu; Ming Luo; Jieming Zhong; Fangrong Fei; Bin Chen; Xiaomeng Wang; Xiaolin Wei; Ying Peng. Greenness exposure and all-cause mortality during multi-drug resistant tuberculosis treatment: A population-based cohort study. Science of The Total Environment 2021, 771, 145422 .
AMA StyleErjia Ge, Jianhui Gao, Zhoupeng Ren, Xin Liu, Ming Luo, Jieming Zhong, Fangrong Fei, Bin Chen, Xiaomeng Wang, Xiaolin Wei, Ying Peng. Greenness exposure and all-cause mortality during multi-drug resistant tuberculosis treatment: A population-based cohort study. Science of The Total Environment. 2021; 771 ():145422.
Chicago/Turabian StyleErjia Ge; Jianhui Gao; Zhoupeng Ren; Xin Liu; Ming Luo; Jieming Zhong; Fangrong Fei; Bin Chen; Xiaomeng Wang; Xiaolin Wei; Ying Peng. 2021. "Greenness exposure and all-cause mortality during multi-drug resistant tuberculosis treatment: A population-based cohort study." Science of The Total Environment 771, no. : 145422.
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.
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 StyleYong 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 StyleYong 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.
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.
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 StyleMengxiao 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 StyleMengxiao 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.
Protected areas (PAs) play an important role in biodiversity conservation and ecosystem integrity. However, human development has threatened and affected the function and effectiveness of PAs. The Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night-time stable light (NTL) data have proven to be an effective indicator of the intensity and change of human-induced urban development over a long time span and at a larger spatial scale. We used the NTL data from 1992 to 2013 to characterize the human-induced urban development and studied the spatial and temporal variation of the NTL of global terrestrial PAs. We selected seven types of PAs defined by the International Union for Conversation of Nature (IUCN), including strict nature reserve (Ia), wilderness area (Ib), national park (II), natural monument or feature (III), habitat/species management area (IV), protected landscape/seascape (V), and protected area with sustainable use of natural resources (VI). We evaluated the NTL digital number (DN) in PAs and their surrounding buffer zones, i.e., 0–1 km, 1–5 km, 5–10 km, 10–25 km, 25–50 km, and 50–100 km. The results revealed the level, growth rate, trend, and distribution pattern of NTL in PAs. Within PAs, areas of types V and Ib had the highest and lowest NTL levels, respectively. In the surrounding 1–100 km buffer zones, type V PAs also had the highest NTL level, but type VI PAs had the lowest NTL level. The NTL level in the areas surrounding PAs was higher than that within PAs. Types Ia and III PAs showed the highest and lowest NTL growth rate from 1992 to 2013, respectively, both inside and outside of PAs. The NTL distributions surrounding the Ib and VI PAs were different from other types. The areas close to Ib and VI boundaries, i.e., in the 0–25 km buffer zones, showed lower NTL levels, for which the highest NTL level was observed within the 25–100 km buffer zone. However, other types of PAs showed the opposite NTL patterns. The NTL level was lower in the distant buffer zones, and the lowest night light was within the 1–25 km buffer zones. Globally, 6.9% of PAs are being affected by NTL. Conditions of wilderness areas, e.g., high latitude regions, Tibetan Plateau, Amazon, and Caribbean, are the least affected by NTL. The PAs in Europe, Asia, and North America are more affected by NTL than South America, Africa, and Oceania.
Liangxian Fan; Jianjun Zhao; Yeqiao Wang; Zhoupeng Ren; Hongyan Zhang; Xiaoyi Guo. Assessment of Night-Time Lighting for Global Terrestrial Protected and Wilderness Areas. Remote Sensing 2019, 11, 2699 .
AMA StyleLiangxian Fan, Jianjun Zhao, Yeqiao Wang, Zhoupeng Ren, Hongyan Zhang, Xiaoyi Guo. Assessment of Night-Time Lighting for Global Terrestrial Protected and Wilderness Areas. Remote Sensing. 2019; 11 (22):2699.
Chicago/Turabian StyleLiangxian Fan; Jianjun Zhao; Yeqiao Wang; Zhoupeng Ren; Hongyan Zhang; Xiaoyi Guo. 2019. "Assessment of Night-Time Lighting for Global Terrestrial Protected and Wilderness Areas." Remote Sensing 11, no. 22: 2699.
Minimum mortality temperature (MMT) is an important indicator to assess the temperature–mortality relationship. It reflects human adaptability to local climate. The existing MMT estimates were usually based on case studies in data rich regions, and limited evidence about MMT was available at a global scale. It is still unclear what the most significant driver of MMT is and how MMT will change under global climate change. Here, by analysing MMTs in 420 locations covering six continents (Antarctica was excluded) in the world, we found that although the MMT changes geographically, it is very close to the local most frequent temperature (MFT) in the same period. The association between MFT and MMT is not changed when we adjust for latitude and study year. Based on the MFT~MMT association, we estimate and map the global distribution of MMTs in the present (2010s) and the future (2050s) for the first time.
Qian Yin; Jinfeng Wang; Zhoupeng Ren; Jie Li; Yuming Guo. Mapping the increased minimum mortality temperatures in the context of global climate change. Nature Communications 2019, 10, 1 -8.
AMA StyleQian Yin, Jinfeng Wang, Zhoupeng Ren, Jie Li, Yuming Guo. Mapping the increased minimum mortality temperatures in the context of global climate change. Nature Communications. 2019; 10 (1):1-8.
Chicago/Turabian StyleQian Yin; Jinfeng Wang; Zhoupeng Ren; Jie Li; Yuming Guo. 2019. "Mapping the increased minimum mortality temperatures in the context of global climate change." Nature Communications 10, no. 1: 1-8.
Given the limitations of current approaches for disease relative risk mapping, it is necessary to develop a comprehensive mapping method not only to simultaneously downscale various epidemiologic indicators, but also to be suitable for different disease outcomes. We proposed a three-step progressive statistical method, named disease relative risk downscaling (DRRD) model, to localize different spatial epidemiologic relative risk indicators for disease mapping, and applied it to the real world hand, foot, and mouth disease (HFMD) occurrence data over Mainland China. First, to generate a spatially complete crude risk map for disease binary variable, we employed ordinary and spatial logistic regression models under Bayesian hierarchical modeling framework to estimate county-level HFMD occurrence probabilities. Cross-validation showed that spatial logistic regression (average prediction accuracy: 80.68%) outperformed ordinary logistic regression (69.75%), indicating the effectiveness of incorporating spatial autocorrelation effect in modeling. Second, for the sake of designing a suitable spatial case–control study, we took spatial stratified heterogeneity impact expressed as Chinese seven geographical divisions into consideration. Third, for generating different types of disease relative risk maps, we proposed local-scale formulas for calculating three spatial epidemiologic indicators, i.e., spatial odds ratio, spatial risk ratio, and spatial attributable risk. The immediate achievement of this study is constructing a series of national disease relative risk maps for China’s county-level HFMD interventions. The new DRRD model provides a more convenient and easily extended way for assessing local-scale relative risks in spatial and environmental epidemiology, as well as broader risk assessment sciences.
Chao Song; Yaqian He; Yanchen Bo; Jinfeng Wang; Zhoupeng Ren; Jiangang Guo; Huibin Yang. Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China. Stochastic Environmental Research and Risk Assessment 2019, 33, 1815 -1833.
AMA StyleChao Song, Yaqian He, Yanchen Bo, Jinfeng Wang, Zhoupeng Ren, Jiangang Guo, Huibin Yang. Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China. Stochastic Environmental Research and Risk Assessment. 2019; 33 (10):1815-1833.
Chicago/Turabian StyleChao Song; Yaqian He; Yanchen Bo; Jinfeng Wang; Zhoupeng Ren; Jiangang Guo; Huibin Yang. 2019. "Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China." Stochastic Environmental Research and Risk Assessment 33, no. 10: 1815-1833.
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.
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 StyleYong 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 StyleYong 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.
(1) Background: Although the health effects of future climate change have been examined in previous studies, few have considered additive impacts of population expansion, ageing, and adaptation. We aimed to quantify the future heat-related years of life lost (YLLs) under different Representative Concentration Pathways (RCP) scenarios and global-scale General Circulation Models (GCMs), and further to examine relative contributions of population expansion, ageing, and adaptation on these projections. (2) Methods: We used downscaled and bias-corrected projections of daily temperature from 27 GCMs under RCP2.6, 4.5, and 8.5 scenarios to quantify the potential annual heat-related YLLs in Guangzhou, China in the 2030s, 2060s, and 2090s, compared to those in the 1980s as a baseline. We also explored the modification effects of a range of population expansion, ageing, and adaptation scenarios on the heat-related YLLs. (3) Results: Global warming, particularly under the RCP8.5 scenario, would lead to a substantial increase in the heat-related YLLs in the 2030s, 2060s, and 2090s for the majority of the GCMs. For the total population, the annual heat-related YLLs under the RCP8.5 in the 2030s, 2060s, and 2090s were 2.2, 7.0, and 11.4 thousand, respectively. The heat effects would be significantly exacerbated by rapid population expansion and ageing. However, substantial heat-related YLLs could be counteracted by the increased adaptation (75% for the total population and 20% for the elderly). (4) Conclusions: The rapid population expansion and ageing coinciding with climate change may present an important health challenge in China, which, however, could be partially counteracted by the increased adaptation of individuals.
Tao Liu; Zhoupeng Ren; Yonghui Zhang; Baixiang Feng; Hualiang Lin; Jianpeng Xiao; Weilin Zeng; Xing Li; Zhihao Li; Shannon Rutherford; Yanjun Xu; Shao Lin; Philip C. Nasca; Yaodong Du; Jinfeng Wang; Cunrui Huang; Peng Jia; Wenjun Ma. Modification Effects of Population Expansion, Ageing, and Adaptation on Heat-Related Mortality Risks Under Different Climate Change Scenarios in Guangzhou, China. International Journal of Environmental Research and Public Health 2019, 16, 376 .
AMA StyleTao Liu, Zhoupeng Ren, Yonghui Zhang, Baixiang Feng, Hualiang Lin, Jianpeng Xiao, Weilin Zeng, Xing Li, Zhihao Li, Shannon Rutherford, Yanjun Xu, Shao Lin, Philip C. Nasca, Yaodong Du, Jinfeng Wang, Cunrui Huang, Peng Jia, Wenjun Ma. Modification Effects of Population Expansion, Ageing, and Adaptation on Heat-Related Mortality Risks Under Different Climate Change Scenarios in Guangzhou, China. International Journal of Environmental Research and Public Health. 2019; 16 (3):376.
Chicago/Turabian StyleTao Liu; Zhoupeng Ren; Yonghui Zhang; Baixiang Feng; Hualiang Lin; Jianpeng Xiao; Weilin Zeng; Xing Li; Zhihao Li; Shannon Rutherford; Yanjun Xu; Shao Lin; Philip C. Nasca; Yaodong Du; Jinfeng Wang; Cunrui Huang; Peng Jia; Wenjun Ma. 2019. "Modification Effects of Population Expansion, Ageing, and Adaptation on Heat-Related Mortality Risks Under Different Climate Change Scenarios in Guangzhou, China." International Journal of Environmental Research and Public Health 16, no. 3: 376.
Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD’s spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk (RR) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health.
Chao Song; Yaqian He; Yanchen Bo; Jinfeng Wang; Zhoupeng Ren; Huibin Yang. Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models. International Journal of Environmental Research and Public Health 2018, 15, 1476 .
AMA StyleChao Song, Yaqian He, Yanchen Bo, Jinfeng Wang, Zhoupeng Ren, Huibin Yang. Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models. International Journal of Environmental Research and Public Health. 2018; 15 (7):1476.
Chicago/Turabian StyleChao Song; Yaqian He; Yanchen Bo; Jinfeng Wang; Zhoupeng Ren; Huibin Yang. 2018. "Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models." International Journal of Environmental Research and Public Health 15, no. 7: 1476.
Previous research suggested an association between maternal exposure to ambient air pollutants and risk of congenital heart defects (CHDs), though the effects of particulate matter ≤10 μm in aerodynamic diameter (PM10) on CHDs are inconsistent. We used two machine learning models (i.e., random forest (RF) and gradient boosting (GB)) to investigate the non-linear effects of PM10 exposure during the critical time window, weeks 3–8 in pregnancy, on risk of CHDs. From 2009 through 2012, we carried out a population-based birth cohort study on 39,053 live-born infants in Beijing. RF and GB models were used to calculate odds ratios for CHDs associated with increase in PM10 exposure, adjusting for maternal and perinatal characteristics. Maternal exposure to PM10 was identified as the primary risk factor for CHDs in all machine learning models. We observed a clear non-linear effect of maternal exposure to PM10 on CHDs risk. Compared to 40 μg m−3, the following odds ratios resulted: 1) 92 μg m−3 [RF: 1.16 (95% CI: 1.06, 1.28); GB: 1.26 (95% CI: 1.17, 1.35)]; 2) 111 μg m−3 [RF: 1.04 (95% CI: 0.96, 1.14); GB: 1.04 (95% CI: 0.99, 1.08)]; 3) 124 μg m−3 [RF: 1.01 (95% CI: 0.94, 1.10); GB: 0.98 (95% CI: 0.93, 1.02)]; 4) 190 μg m−3 [RF: 1.29 (95% CI: 1.14, 1.44); GB: 1.71 (95% CI: 1.04, 2.17)]. Overall, both machine models showed an association between maternal exposure to ambient PM10 and CHDs in Beijing, highlighting the need for non-linear methods to investigate dose-response relationships.
Zhoupeng Ren; Jun Zhu; Yanfang Gao; Qian Yin; Maogui Hu; Li Dai; Changfei Deng; Lin Yi; Kui Deng; Yanping Wang; Xiaohong Li; Jinfeng Wang. Maternal exposure to ambient PM10 during pregnancy increases the risk of congenital heart defects: Evidence from machine learning models. Science of The Total Environment 2018, 630, 1 -10.
AMA StyleZhoupeng Ren, Jun Zhu, Yanfang Gao, Qian Yin, Maogui Hu, Li Dai, Changfei Deng, Lin Yi, Kui Deng, Yanping Wang, Xiaohong Li, Jinfeng Wang. Maternal exposure to ambient PM10 during pregnancy increases the risk of congenital heart defects: Evidence from machine learning models. Science of The Total Environment. 2018; 630 ():1-10.
Chicago/Turabian StyleZhoupeng Ren; Jun Zhu; Yanfang Gao; Qian Yin; Maogui Hu; Li Dai; Changfei Deng; Lin Yi; Kui Deng; Yanping Wang; Xiaohong Li; Jinfeng Wang. 2018. "Maternal exposure to ambient PM10 during pregnancy increases the risk of congenital heart defects: Evidence from machine learning models." Science of The Total Environment 630, no. : 1-10.
This study aimed to investigate the spatial distribution pattern of the prevalence of congenital heart disease (CHD) in children in Qinghai-Tibetan Plateau (QTP), a high-altitude region in China. Epidemiological data from a survey on the prevalence of CHD in Qinghai Province including 288,066 children (4–18 years) were used in this study. The prevalence and distribution pattern of CHD was determined by sex, CHD subtype, and nationality and altitude. Spatial pattern analysis using Getis-Ord Gi⁎ was used to identify the spatial distribution of CHD. Bayesian spatial binomial regression was performed to examine the relationship between the prevalence of CHD and environmental risk factors in the QTP. The prevalence of CHD showed a significant spatial clustering pattern. The Tibetan autonomous prefecture of Yushu (average altitude > 4000 m) and the Mongolian autonomous county of Henan (average altitude > 3600 m) in Huangnan had the highest prevalence of CHD. Univariate analysis showed that with ascending altitude, the total prevalence of CHD, that in girls and boys with CHD, and that of the subtypes PDA and ASD increasing accordingly. Thus, environmental factors greatly contributed to the prevalence of CHD. The prevalence of CHD shows significant spatial clustering pattern in the QTP. The CHD subtype prevalence clustering pattern has statistical regularity which would provide convenient clues of environmental risk factors. Our results may provide support to make strategies of CHD prevention, to reduce the incidence of CHD in high altitude regions of China.
Li-Guang Ma; Qiu-Hong Chen; Yuan-Yuan Wang; Jing Wang; Zhoupeng Ren; Zong-Fu Cao; Yan-Rong Cao; Xu Ma; Bin-Bin Wang. Spatial pattern and variations in the prevalence of congenital heart disease in children aged 4–18 years in the Qinghai-Tibetan Plateau. Science of The Total Environment 2018, 627, 158 -165.
AMA StyleLi-Guang Ma, Qiu-Hong Chen, Yuan-Yuan Wang, Jing Wang, Zhoupeng Ren, Zong-Fu Cao, Yan-Rong Cao, Xu Ma, Bin-Bin Wang. Spatial pattern and variations in the prevalence of congenital heart disease in children aged 4–18 years in the Qinghai-Tibetan Plateau. Science of The Total Environment. 2018; 627 ():158-165.
Chicago/Turabian StyleLi-Guang Ma; Qiu-Hong Chen; Yuan-Yuan Wang; Jing Wang; Zhoupeng Ren; Zong-Fu Cao; Yan-Rong Cao; Xu Ma; Bin-Bin Wang. 2018. "Spatial pattern and variations in the prevalence of congenital heart disease in children aged 4–18 years in the Qinghai-Tibetan Plateau." Science of The Total Environment 627, no. : 158-165.
Mosquitoes are responsible for spreading many diseases and their populations are susceptible to environmental changes. The ecosystems in the Three Gorges Region were probably altered because of changes to the environment during the construction of the Three Gorges Dam (TGD), the world's largest hydroelectric dam by generating capacity. We selected three sites at which to monitor the mosquitoes from 1997 to 2009. We captured adult mosquitoes with battery-powered aspirators fortnightly between May and September of each year in dwellings and sheds. We identified the mosquito species, and examined changes in the species density during the TGD construction. We monitored changes in the species and density of mosquitoes in this area for 13 years during the TGD construction and collected information that could be used to support the control and prevention of mosquito-borne infections. We found that the mosquito species composition around the residential areas remained the same, and the density changed gradually during the TGD construction. The changes in the populations tended to be consistent over the years, and the densities were highest in July, and were between 3 and 5 times greater in the sheds than in the dwellings. The mosquito species and populations remained stable during the construction of the TGD. The mosquito density may have increased as the reservoir filled, and may have decreased during the clean-up work. Clean-up work may be an effective way to control mosquitoes and prevent mosquito-borne diseases.
Yuhong Guo; Shengjie Lai; Jing Zhang; Qiyong Liu; Huaiqing Zhang; Zhoupeng Ren; Deqiang Mao; Chao Luo; Yuanyuan He; Haixia Wu; Guichang Li; Dongsheng Ren; Xiaobo Liu; Zhaorui Chang. Mosquito population dynamics during the construction of Three Gorges Dam in Yangtze River, China. Acta Tropica 2018, 182, 251 -256.
AMA StyleYuhong Guo, Shengjie Lai, Jing Zhang, Qiyong Liu, Huaiqing Zhang, Zhoupeng Ren, Deqiang Mao, Chao Luo, Yuanyuan He, Haixia Wu, Guichang Li, Dongsheng Ren, Xiaobo Liu, Zhaorui Chang. Mosquito population dynamics during the construction of Three Gorges Dam in Yangtze River, China. Acta Tropica. 2018; 182 ():251-256.
Chicago/Turabian StyleYuhong Guo; Shengjie Lai; Jing Zhang; Qiyong Liu; Huaiqing Zhang; Zhoupeng Ren; Deqiang Mao; Chao Luo; Yuanyuan He; Haixia Wu; Guichang Li; Dongsheng Ren; Xiaobo Liu; Zhaorui Chang. 2018. "Mosquito population dynamics during the construction of Three Gorges Dam in Yangtze River, China." Acta Tropica 182, no. : 251-256.
Jinfeng Wang; Qian Yin; Shilu Tong; Zhoupeng Ren; Maogui Hu; Hongrui Zhang. Prolonged continuous exposure to high fine particulate matter associated with cardiovascular and respiratory disease mortality in Beijing, China. Atmospheric Environment 2017, 168, 1 -7.
AMA StyleJinfeng Wang, Qian Yin, Shilu Tong, Zhoupeng Ren, Maogui Hu, Hongrui Zhang. Prolonged continuous exposure to high fine particulate matter associated with cardiovascular and respiratory disease mortality in Beijing, China. Atmospheric Environment. 2017; 168 ():1-7.
Chicago/Turabian StyleJinfeng Wang; Qian Yin; Shilu Tong; Zhoupeng Ren; Maogui Hu; Hongrui Zhang. 2017. "Prolonged continuous exposure to high fine particulate matter associated with cardiovascular and respiratory disease mortality in Beijing, China." Atmospheric Environment 168, no. : 1-7.
Zhoupeng Ren; Yong Ge; Jinfeng Wang; Jingyao Mao; Qi Zhang. Understanding the inconsistent relationships between socioeconomic factors and poverty incidence across contiguous poverty-stricken regions in China: Multilevel modelling. Spatial Statistics 2017, 21, 406 -420.
AMA StyleZhoupeng Ren, Yong Ge, Jinfeng Wang, Jingyao Mao, Qi Zhang. Understanding the inconsistent relationships between socioeconomic factors and poverty incidence across contiguous poverty-stricken regions in China: Multilevel modelling. Spatial Statistics. 2017; 21 ():406-420.
Chicago/Turabian StyleZhoupeng Ren; Yong Ge; Jinfeng Wang; Jingyao Mao; Qi Zhang. 2017. "Understanding the inconsistent relationships between socioeconomic factors and poverty incidence across contiguous poverty-stricken regions in China: Multilevel modelling." Spatial Statistics 21, no. : 406-420.
Yong Ge; Yue Yuan; Shan Hu; Zhoupeng Ren; Yijin Wu. Space–time variability analysis of poverty alleviation performance in China’s poverty-stricken areas. Spatial Statistics 2017, 21, 460 -474.
AMA StyleYong Ge, Yue Yuan, Shan Hu, Zhoupeng Ren, Yijin Wu. Space–time variability analysis of poverty alleviation performance in China’s poverty-stricken areas. Spatial Statistics. 2017; 21 ():460-474.
Chicago/Turabian StyleYong Ge; Yue Yuan; Shan Hu; Zhoupeng Ren; Yijin Wu. 2017. "Space–time variability analysis of poverty alleviation performance in China’s poverty-stricken areas." Spatial Statistics 21, no. : 460-474.
Since the disclosure of the “Illegal vaccine operation series case in Jinan, Shandong” in March, 2016, this issue has attracted a great deal of attention and has led to public concerns about the safety and efficacy of the vaccines involved in this case. The main purpose of this paper is to scientifically and scrupulously predict the possible geographic distribution of illegal vaccines in China, and provide a foundation to guide future governmental policies and actions. A species distribution model was used because of the advantages of using presence/pseudo-absence or presence-only data, and it performs well with incomplete species distribution data. A series of socioeconomic variables were used to simulate habitat suitability distribution. Maxent (Maximum Entropy Model) and GARP (Genetic Algorithm for Rule set Production) were used to predict the risks of illegal vaccines in China, and define the spatial distribution and significant factors of the area at risk from illegal vaccines. Jackknife tests were used to evaluate the relative importance of socioeconomic variables. It was found that: (1) Shandong, Hebei, Henan, Jiangsu and Anhui are the main high-risk areas impacted by the vaccines involved in Jinan case. (2) Population density and industrial structure are the main socioeconomic factors affecting areas which may be at risk from illegal vaccines.
Yilan Liao; Yanhui Lei; Zhoupeng Ren; Huiyan Chen; Dongyue Li. Predicting the potential risk area of illegal vaccine trade in China. Scientific Reports 2017, 7, 3883 .
AMA StyleYilan Liao, Yanhui Lei, Zhoupeng Ren, Huiyan Chen, Dongyue Li. Predicting the potential risk area of illegal vaccine trade in China. Scientific Reports. 2017; 7 (1):3883.
Chicago/Turabian StyleYilan Liao; Yanhui Lei; Zhoupeng Ren; Huiyan Chen; Dongyue Li. 2017. "Predicting the potential risk area of illegal vaccine trade in China." Scientific Reports 7, no. 1: 3883.