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Luo W, Liu Z, Ran Y, Li M, Zhou Y, Hou W, Lai S, Li SL, Yin L. Unraveling varying spatiotemporal patterns of dengue and associated exposure-response relationships with environmental variables in Southeast Asian countries before and during COVID-19. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.25.24304825. [PMID: 38585938 PMCID: PMC10996745 DOI: 10.1101/2024.03.25.24304825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
The enforcement of COVID-19 interventions by diverse governmental bodies, coupled with the indirect impact of COVID-19 on short-term environmental changes (e.g. plant shutdowns lead to lower greenhouse gas emissions), influences the dengue vector. This provides a unique opportunity to investigate the impact of COVID-19 on dengue transmission and generate insights to guide more targeted prevention measures. We aim to compare dengue transmission patterns and the exposure-response relationship of environmental variables and dengue incidence in the pre- and during-COVID-19 to identify variations and assess the impact of COVID-19 on dengue transmission. We initially visualized the overall trend of dengue transmission from 2012-2022, then conducted two quantitative analyses to compare dengue transmission pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). These analyses included time series analysis to assess dengue seasonality, and a Distributed Lag Non-linear Model (DLNM) to quantify the exposure-response relationship between environmental variables and dengue incidence. We observed that all subregions in Thailand exhibited remarkable synchrony with a similar annual trend except 2021. Cyclic and seasonal patterns of dengue remained consistent pre- and during-COVID-19. Monthly dengue incidence in three countries varied significantly. Singapore witnessed a notable surge during-COVID-19, particularly from May to August, with cases multiplying several times compared to pre-COVID-19, while seasonality of Malaysia weakened. Exposure-response relationships of dengue and environmental variables show varying degrees of change, notably in Northern Thailand, where the peak relative risk for the maximum temperature-dengue relationship rose from about 3 to 17, and the max RR of overall cumulative association 0-3 months of relative humidity increased from around 5 to 55. Our study is the first to compare dengue transmission patterns and their relationship with environmental variables before and during COVID-19, showing that COVID-19 has affected dengue transmission at both the national and regional level, and has altered the exposure-response relationship between dengue and the environment.
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Affiliation(s)
- Wei Luo
- GeoSpatialX Lab, Department of Geography, National University of Singapore, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Zhihao Liu
- School of Geosciences, Yangtze University, Wuhan, China
| | - Yiding Ran
- GeoSpatialX Lab, Department of Geography, National University of Singapore, Singapore, Singapore
| | - Mengqi Li
- Department of Geography, University of Zurich, Zurich, Switzerland
| | - Yuxuan Zhou
- Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Weitao Hou
- School of Design and the Built Environment, Curtin University, Perth, Australia
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - Sabrina L Li
- School of Geography, University of Nottingham, Nottingham, United Kingdom
| | - Ling Yin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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You L, Zhu R, Kwan MP, Chen M, Zhang F, Yang B, Wong MS, Qin Z. Unraveling adaptive changes in electric vehicle charging behavior toward the postpandemic era by federated meta-learning. Innovation (N Y) 2024; 5:100587. [PMID: 38426200 PMCID: PMC10901825 DOI: 10.1016/j.xinn.2024.100587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Affiliation(s)
- Linlin You
- School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Rui Zhu
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A∗STAR), 1 Fusionopolis Way, Singapore 138632, Republic of Singapore
| | - Mei-Po Kwan
- Institute of Space and Earth Information Science, Chinese University of Hong Kong, Hong Kong, China
| | - Min Chen
- Key Laboratory of Virtual Geographic Environment (Ministry of Education), Nanjing Normal University, Nanjing 210023, China
| | - Fan Zhang
- Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
| | - Bisheng Yang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
| | - Man Sing Wong
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Zheng Qin
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A∗STAR), 1 Fusionopolis Way, Singapore 138632, Republic of Singapore
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Huang J, Zhao Y, Yan W, Lian X, Wang R, Chen B, Chen S. Multi-source dynamic ensemble prediction of infectious disease and application in COVID-19 case. J Thorac Dis 2023; 15:4040-4052. [PMID: 37559615 PMCID: PMC10407500 DOI: 10.21037/jtd-23-234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 06/18/2023] [Indexed: 08/11/2023]
Abstract
BACKGROUND The development of an epidemic always exhibits multiwave oscillation owing to various anthropogenic sources of transmission. Particularly in populated areas, the large-scaled human mobility led to the transmission of the virus faster and more complex. The accurate prediction of the spread of infectious diseases remains a problem. To solve this problem, we propose a new method called the multi-source dynamic ensemble prediction (MDEP) method that incorporates a modified susceptible-exposed-infected-removed (SEIR) model to improve the accuracy of the prediction result. METHODS The modified SEIR model is based on the compartment model, which is suitable for local-scale and confined spaces, where human mobility on a large scale is not considered. Moreover, compartmental models cannot be used to predict multiwave epidemics. The proposed MDEP method can remedy defects in the compartment model. In this study, multi-source prediction was made on the development of coronavirus disease 2019 (COVID-19) and dynamically assembled to obtain the final integrated result. We used the real epidemic data of COVID-19 in three cities in China: Beijing, Lanzhou, and Beihai. Epidemiological data were collected from 17 April, 2022 to 12 August, 2022. RESULTS Compared to the one-wave modified SEIR model, the MDEP method can depict the multiwave development of COVID-19. The MDEP method was applied to predict the number of cumulative cases of recent COVID-19 outbreaks in the aforementioned cities in China. The average accuracy rates in Beijing, Lanzhou, and Beihai were 89.15%, 91.74%, and 94.97%, respectively. CONCLUSIONS The MDEP method improved the prediction accuracy of COVID-19. With further application to other infectious diseases, the MDEP method will provide accurate predictions of infectious diseases and aid governments make appropriate directives.
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Affiliation(s)
- Jianping Huang
- Collaborative Innovation Centre for Western Ecological Safety (CIWES), College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
| | - Yingjie Zhao
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
| | - Wei Yan
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
| | - Xinbo Lian
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
| | - Rui Wang
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
| | - Bin Chen
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
| | - Siyu Chen
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
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Bagheri B, Azadi H, Soltani A, Witlox F. Global city data analysis using SciMAT: a bibliometric review. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2023:1-25. [PMID: 37363031 PMCID: PMC10198599 DOI: 10.1007/s10668-023-03255-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 04/13/2023] [Indexed: 06/28/2023]
Abstract
Global cities play a significant role in world economy as they serve as key hubs of economic activity and trade. These cities are centers of innovation, finance, culture, and commerce, attracting businesses and entrepreneurs from all over the world. They are characterized by their openness, diversity, and their ability to attract and retain talent. This paper includes a bibliometric analysis of the structure of global cities through examining the literature on global cities, including the document type, country/territory distribution, institution distribution, geographical distribution of authors, specially most active authors and their interests or research areas, relationships between principal authors and more relevant journals, and the research hot spots. The input data consists of journal articles archived by the Web of Science from 1991 to 2023, and the analysis is performed using SciMAT and VOS Viewer. The result of this paper would provide valuable insights into the state of research on this topic, including who is conducting research, where it is being conducted, what types of publications are being produced, and which themes are having the most impact on the field. Such an analysis would be useful for researchers, policymakers, and other stakeholders interested in understanding the role of global cities in the world economy. Graphical abstract
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Affiliation(s)
- Bagher Bagheri
- Department of Urban and Regional Planning, Faculty of Architecture and Urban Planning, Shahid Beheshti University, Tehran, Iran
| | - Hossein Azadi
- Department of Economics and Rural Development, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Ali Soltani
- UniSA Business, University of South Australia, Adelaide, Australia
| | - Frank Witlox
- Department of Geography, University of Tartu, Vanemuise, Tartu, Estonia
- College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Yudao Street, Nanjing, China
- Department of Geography, Ghent University, Ghent, Belgium
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Cross-regional analysis of the association between human mobility and COVID-19 infection in Southeast Asia during the transitional period of “living with COVID-19”. Health Place 2023; 81:103000. [PMID: 37011444 PMCID: PMC10008814 DOI: 10.1016/j.healthplace.2023.103000] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/24/2023] [Accepted: 03/06/2023] [Indexed: 03/14/2023]
Abstract
Background In response to COVID-19, Southeast Asian (SEA) countries had imposed stringent lockdowns and restrictions to mitigate the pandemic ever since 2019. Because of a gradually boosting vaccination rate along with a strong demand for economic recovery, many governments have shifted the intervention strategy from restrictions to “Living with COVID-19” where people gradually resumed their normal activities since the second half of the year 2021. Noticeably, timelines for enacting the loosened strategy varied across Southeast Asian countries, which resulted in different patterns of human mobility across space and time. This thus presents an opportunity to study the relationship between mobility and the number of infection cases across regions, which could provide support for ongoing interventions in terms of effectiveness. Objective This study aimed to investigate the association between human mobility and COVID-19 infections across space and time during the transition period of shifting strategies from restrictions to normal living in Southeast Asia. Our research results have significant implications for evidence-based policymaking at the present of the COVID-19 pandemic and other public health issues. Methods We aggregated weekly average human mobility data derived from the Facebook origin and destination Movement dataset. and weekly average new cases of COVID-19 at the district level from 01-Jun-2021 to 26-Dec-2021 (a total of 30 weeks). We mapped the spatiotemporal dynamics of human mobility and COVID-19 cases across countries in SEA. We further adopted the Geographically and Temporally Weighted Regression model to identify the spatiotemporal variations of the association between human mobility and COVID-19 infections over 30 weeks. Our model also controls for socioeconomic status, vaccination, and stringency of intervention to better identify the impact of human mobility on COVID-19 spread. Results The percentage of districts that presented a statistically significant association between human mobility and COVID-19 infections generally decreased from 96.15% in week 1 to 90.38% in week 30, indicating a gradual disconnection between human mobility and COVID-19 spread. Over the study period, the average coefficients in 7 SEA countries increased, decreased, and finally kept stable. The association between human mobility and COVID-19 spread also presents spatial heterogeneity where higher coefficients were mainly concentrated in districts of Indonesia from week 1 to week 10 (ranging from 0.336 to 0.826), while lower coefficients were mainly located in districts of Vietnam (ranging from 0.044 to 0.130). From week 10 to week 25, higher coefficients were mainly observed in Singapore, Malaysia, Brunei, north Indonesia, and several districts of the Philippines. Despite the association showing a general weakening trend over time, significant positive coefficients were observed in Singapore, Malaysia, western Indonesia, and the Philippines, with the relatively highest coefficients observed in the Philippines in week 30 (ranging from 0.101 to 0.139). Conclusions The loosening interventions in response to COVID-19 in SEA countries during the second half of 2021 led to diverse changes in human mobility over time, which may result in the COVID-19 infection dynamics. This study investigated the association between mobility and infections at the regional level during the special transitional period. Our study has important implications for public policy interventions, especially at the later stage of a public health crisis.
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Chin WCB, Chan CH. Analyzing the Trends of COVID-19 and Human Activity Intensity in Malaysia. Trop Med Infect Dis 2023; 8:tropicalmed8020072. [PMID: 36828488 PMCID: PMC9967257 DOI: 10.3390/tropicalmed8020072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/07/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
COVID-19 has struck the world with multiple waves. Each wave was caused by a variant and presented different peaks and baselines. This made the identification of waves with the time series of the cases a difficult task. Human activity intensities may affect the occurrence of an outbreak. We demonstrated a metric of time series, namely log-moving-average-ratio (LMAR), to identify the waves and directions of the changes in the disease cases and check-ins (MySejahtera). Based on the detected waves and changes, we explore the relationship between the two. Using the stimulus-organism-response model with our results, we presented a four-stage model: (1) government-imposed movement restrictions, (2) revenge travel, (3) self-imposed movement reduction, and (4) the new normal. The inverse patterns between check-ins and pandemic waves suggested that the self-imposed movement reduction would naturally happen and would be sufficient for a smaller epidemic wave. People may spontaneously be aware of the severity of epidemic situations and take appropriate disease prevention measures to reduce the risks of exposure and infection. In summary, LMAR is more sensitive to the waves and could be adopted to characterize the association between travel willingness and confirmed disease cases.
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Affiliation(s)
- Wei Chien Benny Chin
- Department of Geography, National University of Singapore, Singapore 117570, Singapore
| | - Chun-Hsiang Chan
- Undergraduate Program in Intelligent Computing and Big Data, Chung Yuan Christian University, Taoyuan City 320314, Taiwan
- Correspondence: ; Tel.: +886-3-265-4086
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Luo W, Liu Z, Zhou Y, Zhao Y, Li YE, Masrur A, Yu M. Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method. JMIR Public Health Surveill 2022; 8:e35840. [PMID: 35861674 PMCID: PMC9364972 DOI: 10.2196/35840] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 05/19/2022] [Accepted: 07/19/2022] [Indexed: 12/18/2022] Open
Abstract
Background The COVID-19 Delta variant has presented an unprecedented challenge to countries in Southeast Asia (SEA). Its transmission has shown spatial heterogeneity in SEA after countries have adopted different public health interventions during the process. Hence, it is crucial for public health authorities to discover potential linkages between epidemic progression and corresponding interventions such that collective and coordinated control measurements can be designed to increase their effectiveness at reducing transmission in SEA. Objective The purpose of this study is to explore potential linkages between the spatiotemporal progression of the COVID-19 Delta variant and nonpharmaceutical intervention (NPI) measures in SEA. We detected the space-time clusters of outbreaks of COVID-19 and analyzed how the NPI measures relate to the propagation of COVID-19. Methods We collected district-level daily new cases of COVID-19 from June 1 to October 31, 2021, and district-level population data in SEA. We adopted prospective space-time scan statistics to identify the space-time clusters. Using cumulative prospective space-time scan statistics, we further identified variations of relative risk (RR) across each district at a half-month interval and their potential public health intervention linkages. Results We found 7 high-risk clusters (clusters 1-7) of COVID-19 transmission in Malaysia, the Philippines, Thailand, Vietnam, and Indonesia between June and August, 2021, with an RR of 5.45 (P<.001), 3.50 (P<.001), 2.30 (P<.001), 1.36 (P<.001), 5.62 (P<.001), 2.38 (P<.001), 3.45 (P<.001), respectively. There were 34 provinces in Indonesia that have successfully mitigated the risk of COVID-19, with a decreasing range between –0.05 and –1.46 due to the assistance of continuous restrictions. However, 58.6% of districts in Malaysia, Singapore, Thailand, and the Philippines saw an increase in the infection risk, which is aligned with their loosened restrictions. Continuous strict interventions were effective in mitigating COVID-19, while relaxing restrictions may exacerbate the propagation risk of this epidemic. Conclusions The analyses of space-time clusters and RRs of districts benefit public health authorities with continuous surveillance of COVID-19 dynamics using real-time data. International coordination with more synchronized interventions amidst all SEA countries may play a key role in mitigating the progression of COVID-19.
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Affiliation(s)
- Wei Luo
- Department of Geography, National University of Singapore, Singapore, Singapore
| | - Zhaoyin Liu
- Department of Geography, National University of Singapore, Singapore, Singapore
| | - Yuxuan Zhou
- Department of Geography, National University of Singapore, Singapore, Singapore
| | - Yumin Zhao
- Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore
| | - Yunyue Elita Li
- Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN, United States
| | - Arif Masrur
- Department of Geography, Pennsylvania State University, State College, PA, United States
| | - Manzhu Yu
- Department of Geography, Pennsylvania State University, State College, PA, United States
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Food Environment Inequalities and Moderating Effects of Obesity on Their Relationships with COVID-19 in Chicago. SUSTAINABILITY 2022. [DOI: 10.3390/su14116498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The COVID-19 outbreak has raised challenges for people with health problems. Obesity is a global issue related to COVID-19. The Centers for Disease Control (CDC) finds that obesity worsens COVID-19 outcomes. As body mass index increases, the COVID-19 death risk increases. Additionally, due to different restriction policies, the pandemic has transformed our food environment. Thus, it is important to develop an antivirus-enabled paradigm to decrease the COVID-19 spreading rate in neighborhoods with obesity concerns and design a sustainable and healthy food environment. It is found that both COVID-19 and obesity inequalities are associated with food environment inequalities, but few studies have examined the moderating effects of obesity and food environment on COVID-19. According to the Chicago Department of Public Health, more than 30% of the Chicago adult population is obese. Additionally, Chicago has 340,676 COVID-19 cases during the period between 1 March 2020 and 26 November 2021. This study uses regression models to examine the moderating effects of obesity and food environment on COVID-19 in Chicago. Besides food environment factors, green spaces and transportation access are considered. The results show COVID-19 is concentrated in areas with a high obesity rate and low food access. A 1 percent increase in obesity rate is associated with a 2.83 percent increase in COVID-19 death rate in a community. Additionally, the moderating effects of obesity on the association between food environment and COVID-19 are shown in the results.
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Cao M, Liang Y, Zhu Y, Lü G, Ma Z. Prediction for Origin-Destination Distribution of Dockless Shared Bicycles: A Case Study in Nanjing City. Front Public Health 2022; 10:849766. [PMID: 35462802 PMCID: PMC9024127 DOI: 10.3389/fpubh.2022.849766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/28/2022] [Indexed: 11/30/2022] Open
Abstract
Shared bicycles are currently widely welcomed by the public due to their flexibility and convenience; they also help reduce chemical emissions and improve public health by encouraging people to engage in physical activities. However, during their development process, the imbalance between the supply and demand of shared bicycles has restricted the public's willingness to use them. Thus, it is necessary to forecast the demand for shared bicycles in different urban regions. This article presents a prediction model called QPSO-LSTM for the origin and destination (OD) distribution of shared bicycles by combining long short-term memory (LSTM) and quantum particle swarm optimization (QPSO). LSTM is a special type of recurrent neural network (RNN) that solves the long-term dependence problem existing in the general RNN, and is suitable for processing and predicting important events with very long intervals and delays in time series. QPSO is an important swarm intelligence algorithm that solves the optimization problem by simulating the process of birds searching for food. In the QPSO-LSTM model, LSTM is applied to predict the OD numbers. QPSO is used to optimize the LSTM for a problem involving a large number of hyperparameters, and the optimal combination of hyperparameters is quickly determined. Taking Nanjing as an example, the prediction model is applied to two typical areas, and the number of bicycles needed per hour in a future day is predicted. QPSO-LSTM can effectively learn the cycle regularity of the change in bicycle OD quantity. Finally, the QPSO-LSTM model is compared with the autoregressive integrated moving average model (ARIMA), back propagation (BP), and recurrent neural networks (RNNs). This shows that the QPSO-LSTM prediction result is more accurate.
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Affiliation(s)
- Min Cao
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, China
- State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
| | - Ying Liang
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, China
- State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
| | - Yanhui Zhu
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, China
- State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
| | - Guonian Lü
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, China
- State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
| | - Zaiyang Ma
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, China
- State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
- *Correspondence: Zaiyang Ma
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