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Jianghao Wang
State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing, China

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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: 07 December 2020 in International Journal of Geographical Information Science
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Fine-grained inner-annual population data are instrumental in climate change response, resource allocation, and epidemic control. However, such data are currently scarce due to the lack of human-related indicators with both high temporal resolution and long-term coverage that can be used in the process of population spatialization. Here, we estimate monthly 1-km gridded population distribution across China in 2015 using time-series mobile phone positioning data. We construct a hybrid downscaling model to map the gridded population by incorporating random forest and area-to-point kriging. The estimated monthly population products appear to capture inner-annual population variations, especially during special periods, such as the festival, holiday, and short-term labor flow period, which are characterized by large-scale population movements. Additionally, compared with census data, the hybrid model-based results obtained exhibit higher consistency than popular global population products across all spatial extents. Our monthly 1-km data products for the population distribution across China in 2015 provide a credible dataset that can be employed in studies aimed at accurate population-dependent decisions.

ACS Style

Zhifeng Cheng; Jianghao Wang; Yong Ge. Mapping monthly population distribution and variation at 1-km resolution across China. International Journal of Geographical Information Science 2020, 1 -19.

AMA Style

Zhifeng Cheng, Jianghao Wang, Yong Ge. Mapping monthly population distribution and variation at 1-km resolution across China. International Journal of Geographical Information Science. 2020; ():1-19.

Chicago/Turabian Style

Zhifeng Cheng; Jianghao Wang; Yong Ge. 2020. "Mapping monthly population distribution and variation at 1-km resolution across China." International Journal of Geographical Information Science , no. : 1-19.

Journal article
Published: 26 October 2020 in Global Biogeochemical Cycles
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Soil organic carbon (SOC) is the most critical component of global carbon cycle in grassland ecosystems. There has been growing interest in understanding SOC dynamics and driving forces of grassland biomes at various temporal and spatial scales. Up to now, estimates of long‐term and large‐scale changes in SOC of grassland biomes have been mostly based on modelling approaches and manipulative experiments, rather than direct measurements. During 2007–2011, we repeated 141 soil profiles of the sampling in 1963–1964 (up to one meter depth) to quantify the long‐term changes of SOC storage in the major grassland types of Inner Mongolia in order to tease apart the relative contributions of climate change and grazing. We found that SOC decreased in all soil types, except in the Aeolian sandy soils, from 1963 to 2007, with an average reduction rate of 1.8 kg C m–2 (~22.9% or 0.52% yr–1) in the grassland biome of Inner Mongolia. We quantitatively clustered the soils into four groups using principal component analysis (PCA), and detected clear spatial dependency of the changes on climate and grazing. The climate change was responsible for 15.3–34.9% of the total SOC variations, whereas grazing intensity accounted for <9.5% of the changes. Our findings indicated that climate change, rather than grazing, was the primary forcing for the changes in SOC of Inner Mongolia grasslands. We presume that other driving forces, such as changes in non‐grazing‐resultant wind erosion and atmospheric nitrogen deposition, might have played a role albeit their effects need to be further examined.

ACS Style

Xiaoping Xin; Dongyan Jin; Yong Ge; Jianghao Wang; Jiquan Chen; Jiaguo Qi; Housen Chu; Changliang Shao; Philip J. Murray; Ruixue Zhao; Qi Qin; Huajun Tang. Climate Change Dominated Long‐Term Soil Carbon Losses of Inner Mongolian Grasslands. Global Biogeochemical Cycles 2020, 34, 1 .

AMA Style

Xiaoping Xin, Dongyan Jin, Yong Ge, Jianghao Wang, Jiquan Chen, Jiaguo Qi, Housen Chu, Changliang Shao, Philip J. Murray, Ruixue Zhao, Qi Qin, Huajun Tang. Climate Change Dominated Long‐Term Soil Carbon Losses of Inner Mongolian Grasslands. Global Biogeochemical Cycles. 2020; 34 (10):1.

Chicago/Turabian Style

Xiaoping Xin; Dongyan Jin; Yong Ge; Jianghao Wang; Jiquan Chen; Jiaguo Qi; Housen Chu; Changliang Shao; Philip J. Murray; Ruixue Zhao; Qi Qin; Huajun Tang. 2020. "Climate Change Dominated Long‐Term Soil Carbon Losses of Inner Mongolian Grasslands." Global Biogeochemical Cycles 34, no. 10: 1.

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: 01 June 2020 in One Earth
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ACS Style

Jianghao Wang; Nick Obradovich; Siqi Zheng. A 43-Million-Person Investigation into Weather and Expressed Sentiment in a Changing Climate. One Earth 2020, 2, 568 -577.

AMA Style

Jianghao Wang, Nick Obradovich, Siqi Zheng. A 43-Million-Person Investigation into Weather and Expressed Sentiment in a Changing Climate. One Earth. 2020; 2 (6):568-577.

Chicago/Turabian Style

Jianghao Wang; Nick Obradovich; Siqi Zheng. 2020. "A 43-Million-Person Investigation into Weather and Expressed Sentiment in a Changing Climate." One Earth 2, no. 6: 568-577.

Journal article
Published: 30 May 2020 in Science of The Total Environment
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Ambient fine particulate matter (PM2.5) plays an important role in cardiovascular- and respiratory-related death. Empirical statistical models have been widely applied to estimate ambient PM2.5 concentrations with correlated variables. However, empirical statistical models ignore the nonlinear relationship between PM2.5 and covariates and assume that residuals are independent and identically distributed random variables. Here, a hybrid approach, which integrates random forest (RF) model and spatiotemporal kriging, is proposed to estimate the daily PM2.5 concentration. The proposed RF-based spatiotemporal kriging (RFSTK) model effectively captures nonlinear interactions among different predictors and accounts for the detailed spatiotemporal dependence of the PM2.5 concentration. The RFSTK model performs well in predicting the daily PM2.5 concentration. The 10-fold overall cross-validation R2 value is 0.881, the mean absolute error (MAE) is 6.89 μg/m3 and the root-mean-square error (RMSE) is 11.48 μg/m3, indicating better performance than the original RF model (R2 = 0.848, MAE = 7.88 μg/m3 and RMSE = 13.26 μg/m3). The spatiotemporal prediction of the PM2.5 concentration shows that approximately 90.04% of China had a daily exposure to PM2.5 in 2018 that was below the nation's air quality standard of 75 μg/m3. The proposed hybrid method is entirely general and can be applied to map the ambient PM2.5 concentration over a large spatiotemporal domain.

ACS Style

Yanchuan Shao; Zongwei Ma; Jianghao Wang; Jun Bi. Estimating daily ground-level PM2.5 in China with random-forest-based spatiotemporal kriging. Science of The Total Environment 2020, 740, 139761 .

AMA Style

Yanchuan Shao, Zongwei Ma, Jianghao Wang, Jun Bi. Estimating daily ground-level PM2.5 in China with random-forest-based spatiotemporal kriging. Science of The Total Environment. 2020; 740 ():139761.

Chicago/Turabian Style

Yanchuan Shao; Zongwei Ma; Jianghao Wang; Jun Bi. 2020. "Estimating daily ground-level PM2.5 in China with random-forest-based spatiotemporal kriging." Science of The Total Environment 740, no. : 139761.

Journal article
Published: 23 March 2020 in Sustainability
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Urban open public spaces that provide multiple services for residents are essential for improving life quality and urban ecosystem function and promoting healthy development, the safety of human settlements and the sustainable development of urban cities. Based on Sustainable Development Goal 11.7 of the United Nations (UN) 2030 Agenda, this study combines the big earth data with the Theil index, a coefficient of variation and Exploratory Spatial Data Analysis (ESDA) to analyze the regional differences and spatial distribution of urban open public space in 2015 for China, and uses the geographical detector to identify key factors that affect the distribution of open public spaces. The results show that (1) open public space scales in provincial-level cities have an ‘East–Central–West’ low-lying land pattern in spatial distribution, where the eastern region has a relatively larger open public space scale. (2) In the prefecture-level cities, the open public space scale increases with an increase in city size and economic development level, and the differences in urban open public space reduce with an increase in city size and increase with a decrease in the economic development level. (3) Factors including economic development level, residents’ living standards, the urbanization level and the population size have sound explanatory powers in varying degrees on the scale of open public spaces; interactions between these factors have improved the explanatory power of the scale of urban open public space.

ACS Style

Penglong Wang; Yanyan Ma; Xueyan Zhao; Bao Wang; Jianghao Wang; Feng Gao. Regional Differences and Influential Factors of Open Public Space in Chinese Cities Based on Big Earth Data. Sustainability 2020, 12, 2514 .

AMA Style

Penglong Wang, Yanyan Ma, Xueyan Zhao, Bao Wang, Jianghao Wang, Feng Gao. Regional Differences and Influential Factors of Open Public Space in Chinese Cities Based on Big Earth Data. Sustainability. 2020; 12 (6):2514.

Chicago/Turabian Style

Penglong Wang; Yanyan Ma; Xueyan Zhao; Bao Wang; Jianghao Wang; Feng Gao. 2020. "Regional Differences and Influential Factors of Open Public Space in Chinese Cities Based on Big Earth Data." Sustainability 12, no. 6: 2514.

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.

Original manuscript
Published: 09 April 2019 in Journal of Regional Science
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Cities offer a large menu of possible employment and leisure opportunities. The gains from such consumer city leisure are likely to be lower on more polluted days. We study the association between daily consumption activity and outdoor air pollution in China and find evidence in favor of the hypothesis that clean air and leaving one's home for leisure trips are complements. Given the high levels of air pollution in cities in the developing world, regulation induced improvement in environmental quality is likely to further stimulate demand for the consumer city.

ACS Style

Cong Sun; Siqi Zheng; Jianghao Wang; Matthew E. Kahn. Does clean air increase the demand for the consumer city? Evidence from Beijing. Journal of Regional Science 2019, 59, 409 -434.

AMA Style

Cong Sun, Siqi Zheng, Jianghao Wang, Matthew E. Kahn. Does clean air increase the demand for the consumer city? Evidence from Beijing. Journal of Regional Science. 2019; 59 (3):409-434.

Chicago/Turabian Style

Cong Sun; Siqi Zheng; Jianghao Wang; Matthew E. Kahn. 2019. "Does clean air increase the demand for the consumer city? Evidence from Beijing." Journal of Regional Science 59, no. 3: 409-434.

Journal article
Published: 01 March 2019 in Transportation Research Part D: Transport and Environment
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ACS Style

Siqi Zheng; Xiaonan Zhang; Weizeng Sun; Jianghao Wang. The effect of a new subway line on local air quality: A case study in Changsha. Transportation Research Part D: Transport and Environment 2019, 68, 26 -38.

AMA Style

Siqi Zheng, Xiaonan Zhang, Weizeng Sun, Jianghao Wang. The effect of a new subway line on local air quality: A case study in Changsha. Transportation Research Part D: Transport and Environment. 2019; 68 ():26-38.

Chicago/Turabian Style

Siqi Zheng; Xiaonan Zhang; Weizeng Sun; Jianghao Wang. 2019. "The effect of a new subway line on local air quality: A case study in Changsha." Transportation Research Part D: Transport and Environment 68, no. : 26-38.

Letter
Published: 21 January 2019 in Nature Human Behaviour
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High levels of air pollution in China may contribute to the urban population’s reported low level of happiness1,2,3. To test this claim, we have constructed a daily city-level expressed happiness metric based on the sentiment in the contents of 210 million geotagged tweets on the Chinese largest microblog platform Sina Weibo4,5,6, and studied its dynamics relative to daily local air quality index and PM2.5 concentrations (fine particulate matter with diameters equal or smaller than 2.5 μm, the most prominent air pollutant in Chinese cities). Using daily data for 144 Chinese cities in 2014, we document that, on average, a one standard deviation increase in the PM2.5 concentration (or Air Quality Index) is associated with a 0.043 (or 0.046) standard deviation decrease in the happiness index. People suffer more on weekends, holidays and days with extreme weather conditions. The expressed happiness of women and the residents of both the cleanest and dirtiest cities are more sensitive to air pollution. Social media data provides real-time feedback for China’s government about rising quality of life concerns.

ACS Style

Siqi Zheng; Jianghao Wang; Cong Sun; Xiaonan Zhang; Matthew E. Kahn. Air pollution lowers Chinese urbanites’ expressed happiness on social media. Nature Human Behaviour 2019, 3, 237 -243.

AMA Style

Siqi Zheng, Jianghao Wang, Cong Sun, Xiaonan Zhang, Matthew E. Kahn. Air pollution lowers Chinese urbanites’ expressed happiness on social media. Nature Human Behaviour. 2019; 3 (3):237-243.

Chicago/Turabian Style

Siqi Zheng; Jianghao Wang; Cong Sun; Xiaonan Zhang; Matthew E. Kahn. 2019. "Air pollution lowers Chinese urbanites’ expressed happiness on social media." Nature Human Behaviour 3, no. 3: 237-243.

Journal article
Published: 28 November 2018 in International Journal of Environmental Research and Public Health
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Fine-particulate pollution is a major public health concern in China. Accurate assessment of the population exposed to PM2.5 requires high-resolution pollution and population information. This paper assesses China’s potential population exposure to PM2.5, maps its spatiotemporal variability, and simulates the effects of the recent air pollution control policy. We relate satellite-based Aerosol Optical Depth (AOD) retrievals to ground-based PM2.5 observations. We employ block cokriging (BCK) to improve the spatial interpolation of PM2.5 distribution. We use the subdistrict level population data to estimate and map the potential population exposure to PM2.5 pollution in China at the subdistrict level, the smallest administrative unit with public demographic information. During 8 April 2013 and 7 April 2014, China’s population-weighted annual average PM2.5 concentration was nearly 7 times the annual average level suggested by the World Health Organization (WHO). About 1322 million people, or 98.6% of the total population, were exposed to PM2.5 at levels above WHO’s daily guideline for longer than half a year. If China can achieve its Action Plan on Prevention and Control of Air Pollution targets by 2017, the population exposed to PM2.5 above China’s daily standard for longer than half a year will be reduced by 85%.

ACS Style

Ying Long; Jianghao Wang; Kang Wu; Junjie Zhang. Population Exposure to Ambient PM2.5 at the Subdistrict Level in China. International Journal of Environmental Research and Public Health 2018, 15, 2683 .

AMA Style

Ying Long, Jianghao Wang, Kang Wu, Junjie Zhang. Population Exposure to Ambient PM2.5 at the Subdistrict Level in China. International Journal of Environmental Research and Public Health. 2018; 15 (12):2683.

Chicago/Turabian Style

Ying Long; Jianghao Wang; Kang Wu; Junjie Zhang. 2018. "Population Exposure to Ambient PM2.5 at the Subdistrict Level in China." International Journal of Environmental Research and Public Health 15, no. 12: 2683.

Research article
Published: 07 September 2018 in International Journal of Geographical Information Science
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The modelling of human mobility and migration patterns has received much attention due to its substantial importance. Despite long-term efforts, we still lack a modelling framework that captures mobility patterns and further obtains a prospective view of movement trends with regards to diverse impacting factors. Here, we propose a proportional odds model of human mobility and migration (POM-HM) that takes a probabilistic approach to model human movements. Our model is based on the migration probability with a log-logistic distribution under the proportional odds assumption. Explanatory variables are introduced into the model by re-parameterizing the probability distribution function. The two resultant functions, namely, the migration strength and cumulative hazard, are used to estimate regional differences among travel fluxes and their tendencies. The performance of the POM-HM in terms of its validity and accuracy is examined and compared with the gravity model and the radiation model. The probability-based modelling framework enables us to investigate regional variations in migrant fluxes consequently further predict potential future patterns. In short, our modelling approach captures the probabilistic nature of human mobility and migration and furthers our understanding of both the spatiotemporal patterns of population movements and the impacts of various driving forces.

ACS Style

Ting Ma; Rong Zhu; Jianghao Wang; Na Zhao; Tao Pei; Yunyan Du; Chenghu Zhou; Jie Chen. A proportional odds model of human mobility and migration patterns. International Journal of Geographical Information Science 2018, 33, 81 -98.

AMA Style

Ting Ma, Rong Zhu, Jianghao Wang, Na Zhao, Tao Pei, Yunyan Du, Chenghu Zhou, Jie Chen. A proportional odds model of human mobility and migration patterns. International Journal of Geographical Information Science. 2018; 33 (1):81-98.

Chicago/Turabian Style

Ting Ma; Rong Zhu; Jianghao Wang; Na Zhao; Tao Pei; Yunyan Du; Chenghu Zhou; Jie Chen. 2018. "A proportional odds model of human mobility and migration patterns." International Journal of Geographical Information Science 33, no. 1: 81-98.

Journal article
Published: 29 August 2018 in ISPRS International Journal of Geo-Information
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The relationship between urban human dynamics and land use types has always been an important issue in the study of urban problems in China. This paper used location data from Sina Location Microblog (commonly known as Weibo) users to study the human dynamics of the spatial-temporal characteristics of gender differences in Beijing’s Olympic Village in June 2014. We applied mathematical statistics and Local Moran’s I to analyze the spatial-temporal distribution of Sina Microblog users in 100 m × 100 m grids and land use patterns. The female users outnumbered male users, and the sex ratio ( S R varied under different land use types at different times. Female users outnumbered male users regarding residential land and public green land, but male users outnumbered female users regarding workplace, especially on weekends, as the S R on weekends ( S R was 120.5) was greater than that on weekdays ( S R was 118.8). After a Local Moran’s I analysis, we found that High–High grids are primarily distributed across education and scientific research land and residential land; these grids and their surrounding grids have more female users than male users. Low–Low grids are mainly distributed across sports centers and workplaces on weekdays; these grids and their surrounding grids have fewer female users than male users. The average number of users on Saturday was the highest value and, on weekends, the number of female and male users both increased in commercial land, but male users were more active than female users ( S R was 110).

ACS Style

Chengcheng Lei; An Zhang; Qingwen Qi; Huimin Su; Jianghao Wang. Spatial-Temporal Analysis of Human Dynamics on Urban Land Use Patterns Using Social Media Data by Gender. ISPRS International Journal of Geo-Information 2018, 7, 358 .

AMA Style

Chengcheng Lei, An Zhang, Qingwen Qi, Huimin Su, Jianghao Wang. Spatial-Temporal Analysis of Human Dynamics on Urban Land Use Patterns Using Social Media Data by Gender. ISPRS International Journal of Geo-Information. 2018; 7 (9):358.

Chicago/Turabian Style

Chengcheng Lei; An Zhang; Qingwen Qi; Huimin Su; Jianghao Wang. 2018. "Spatial-Temporal Analysis of Human Dynamics on Urban Land Use Patterns Using Social Media Data by Gender." ISPRS International Journal of Geo-Information 7, no. 9: 358.

Journal article
Published: 10 July 2018 in Journal of Geophysical Research: Atmospheres
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Land surface evapotranspiration (ET) is an important component of the surface energy budget and water cycle. To solve the problem of the spatial‐scale mismatch between in situ observations and remotely sensed ET, it is necessary to find the most appropriate upscaling approach for acquiring ground truth ET data at the satellite pixel scale. Based on a data set from two flux observation matrices in the middle stream and downstream of the Heihe River Basin, six upscaling methods were intercompared via direct validation and cross validation. The results showed that the area‐weighted method performed better than the other five upscaling methods introducing auxiliary variables (the integrated Priestley‐Taylor equation, weighted area‐to‐area regression kriging [WATARK], artificial neural network, random forest [RF], and deep belief network methods) over homogeneous underlying surfaces. Over moderately heterogeneous underlying surfaces, the WATARK method performed better. However, the RF method performed better over highly heterogeneous underlying surfaces. A combined method (using the area‐weighted and WATARK methods for homogeneous and moderately heterogeneous underlying surfaces, respectively, and using the RF method for highly heterogeneous underlying surfaces) was proposed to acquire the daily ground truth ET data at the satellite pixel scale, and the errors in the ground truth ET data were evaluated. The Dual Temperature Difference (DTD) and ETMonitor were validated using ground truth ET data, which solve the problem of the spatial‐scale mismatch and quantify uncertainties in the validation process.

ACS Style

Xiang Li; Shaomin Liu; Huaixiang Li; Yanfei Ma; Jianghao Wang; Yuan Zhang; Ziwei Xu; Tongren Xu; Lisheng Song; Xiaofan Yang; Zheng Lu; Zeyu Wang; Zhixia Guo. Intercomparison of Six Upscaling Evapotranspiration Methods: From Site to the Satellite Pixel. Journal of Geophysical Research: Atmospheres 2018, 123, 6777 -6803.

AMA Style

Xiang Li, Shaomin Liu, Huaixiang Li, Yanfei Ma, Jianghao Wang, Yuan Zhang, Ziwei Xu, Tongren Xu, Lisheng Song, Xiaofan Yang, Zheng Lu, Zeyu Wang, Zhixia Guo. Intercomparison of Six Upscaling Evapotranspiration Methods: From Site to the Satellite Pixel. Journal of Geophysical Research: Atmospheres. 2018; 123 (13):6777-6803.

Chicago/Turabian Style

Xiang Li; Shaomin Liu; Huaixiang Li; Yanfei Ma; Jianghao Wang; Yuan Zhang; Ziwei Xu; Tongren Xu; Lisheng Song; Xiaofan Yang; Zheng Lu; Zeyu Wang; Zhixia Guo. 2018. "Intercomparison of Six Upscaling Evapotranspiration Methods: From Site to the Satellite Pixel." Journal of Geophysical Research: Atmospheres 123, no. 13: 6777-6803.

Journal article
Published: 09 April 2018 in Remote Sensing
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Spatial downscaling of remotely sensed products is one of the main ways to obtain earth observations at fine resolution. Area-to-point (ATP) geostatistical techniques, in which regular fine grids of remote sensing products are regarded as points, have been applied widely for spatial downscaling. In spatial downscaling, it is common to use auxiliary information to explain some of the unknown spatial variation of the target geographic variable. Because of the ubiquitously spatial heterogeneities, the observed variables always exhibit uncontrolled variance. To overcome problems caused by local heterogeneity that cannot meet the stationarity requirement in ATP regression kriging, this paper proposes a hybrid spatial statistical method which incorporates geographically weighted regression and ATP kriging for spatial downscaling. The proposed geographically weighted ATP regression kriging (GWATPRK) combines fine spatial resolution auxiliary information and allows for non-stationarity in a downscaling model. The approach was verified using eight groups of four different 25 km-resolution surface soil moisture (SSM) remote sensing products to obtain 1 km SSM predictions in two experimental regions, in conjunction with the implementation of three benchmark methods. Analyses and comparisons of the different downscaled results showed GWATPRK obtained downscaled fine spatial resolution images with greater quality and an average loss with a root mean square error value of 17.5%. The analysis indicated the proposed method has high potential for spatial downscaling in remote sensing applications.

ACS Style

Yan Jin; Yong Ge; Jianghao Wang; Gerard B. M. Heuvelink; Le Wang. Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing. Remote Sensing 2018, 10, 579 .

AMA Style

Yan Jin, Yong Ge, Jianghao Wang, Gerard B. M. Heuvelink, Le Wang. Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing. Remote Sensing. 2018; 10 (4):579.

Chicago/Turabian Style

Yan Jin; Yong Ge; Jianghao Wang; Gerard B. M. Heuvelink; Le Wang. 2018. "Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing." Remote Sensing 10, no. 4: 579.

Journal article
Published: 12 March 2018 in Remote Sensing
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Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, remote sensing and social sensing approaches have specific disadvantages regarding the description of social and physical features, respectively. Therefore, an appropriate fusion strategy is vital for large-area land use mapping. To address this issue, we propose an efficient land use mapping method that combines remote sensing imagery (RSI) and mobile phone positioning data (MPPD) for large areas. We implemented this method in two steps. First, a support vector machine was adopted to classify the RSI and MPPD. Then, the two classification results were fused using a decision fusion strategy to generate the land use map. The proposed method was applied to a case study of the central area of Beijing. The experimental results show that the proposed method improved classification accuracy compared with that achieved using MPPD alone, validating the efficacy of this new approach for identifying land use. Based on the land use map and MPPD data, activity density in key zones during daytime and nighttime was analyzed to illustrate the volume and variation of people working and living across different regions.

ACS Style

Yuanxin Jia; Yong Ge; Feng Ling; Xian Guo; Jianghao Wang; Le Wang; Yuehong Chen; Xiaodong Li. Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data. Remote Sensing 2018, 10, 446 .

AMA Style

Yuanxin Jia, Yong Ge, Feng Ling, Xian Guo, Jianghao Wang, Le Wang, Yuehong Chen, Xiaodong Li. Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data. Remote Sensing. 2018; 10 (3):446.

Chicago/Turabian Style

Yuanxin Jia; Yong Ge; Feng Ling; Xian Guo; Jianghao Wang; Le Wang; Yuehong Chen; Xiaodong Li. 2018. "Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data." Remote Sensing 10, no. 3: 446.

Journal article
Published: 01 January 2018 in Journal of Environmental Management
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This paper developed internationally compatible methods for delineating boundaries of urban areas in China. By integrating emission source data with existing official statistics as well as using rescaling methodology of data mapping for 1 km grid, the authors constructed high resolution emission gridded data in Beijing-Tianjin-Hebei (Jing-Jin-Ji) region in China for 2012. Comparisons between urban and non-urban areas of carbon emissions from industry, agriculture, household and transport exhibited regional disparities as well as sectoral differences. Except for the Hebei province, per capita total direct carbon emissions from urban extents in Beijing and Tianjin were both lower than provincial averages, indicating the climate benefit of urbanization, comparable to results from developed countries. Urban extents in the Hebei province were mainly industrial centers while those in Beijing and Tianjin were more service oriented. Further decomposition analysis revealed population to be a common major driver for increased carbon emissions but climate implications of urban design, economic productivity of land use, and carbon intensity of GDP were both cluster- and sector-specific. This study disapproves the one-size-fits-all solution for carbon mitigation but calls for down-scaled analysis of carbon emissions and formulation of localized carbon reduction strategies in the Jing-Jin-Ji as well as other regions in China.

ACS Style

Bofeng Cai; Wanxin Li; Shobhakar Dhakal; Jianghao Wang. Source data supported high resolution carbon emissions inventory for urban areas of the Beijing-Tianjin-Hebei region: Spatial patterns, decomposition and policy implications. Journal of Environmental Management 2018, 206, 786 -799.

AMA Style

Bofeng Cai, Wanxin Li, Shobhakar Dhakal, Jianghao Wang. Source data supported high resolution carbon emissions inventory for urban areas of the Beijing-Tianjin-Hebei region: Spatial patterns, decomposition and policy implications. Journal of Environmental Management. 2018; 206 ():786-799.

Chicago/Turabian Style

Bofeng Cai; Wanxin Li; Shobhakar Dhakal; Jianghao Wang. 2018. "Source data supported high resolution carbon emissions inventory for urban areas of the Beijing-Tianjin-Hebei region: Spatial patterns, decomposition and policy implications." Journal of Environmental Management 206, no. : 786-799.

Book chapter
Published: 01 January 2018 in Comprehensive Geographic Information Systems
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ACS Style

Chenghu Zhou; Tao Pei; Jun Xu; Ting Ma; Zide Fan; Jianghao Wang. Urban Dynamics and GIScience. Comprehensive Geographic Information Systems 2018, 297 -312.

AMA Style

Chenghu Zhou, Tao Pei, Jun Xu, Ting Ma, Zide Fan, Jianghao Wang. Urban Dynamics and GIScience. Comprehensive Geographic Information Systems. 2018; ():297-312.

Chicago/Turabian Style

Chenghu Zhou; Tao Pei; Jun Xu; Ting Ma; Zide Fan; Jianghao Wang. 2018. "Urban Dynamics and GIScience." Comprehensive Geographic Information Systems , no. : 297-312.