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The Yangtze River Basin is an important part of China's ecology and social economy, and the land use/cover status is experiencing rapid changes. As the development of remote sensing technology, the Internet of things and social media, we can obtain huge, multi-dimensional relevant data of land use/cover, the process of land use/cover, driving factors and ecological response of research, the development of the Yangtze river basin planning and ecological protection provides a decision-making basis. First, this paper reviews the current research on land use/cover change in the Yangtze river basin, and then expounds the re⁃ search methods and conclusions in terms of the driving mechanism and ecological effects of land use/cover change. Finally, this paper discusses the difficulties and challenges faced by existing researches from the differences of land use/cover products, regional driving factors, complexity of ecological environmental change process and comprehensive evaluation of ecological service value and the possible directions of future researches. [ABSTRACT FROM AUTHOR]

Emerging as an urban development mode that attempts to effectively promote the sustainable feedback between land use and transport, transit oriented development (TOD) emphasizes the land use development surrounding the public transit node. Many cases across the world have showed the success of TOD and TOD has been adopted as a policy tool for China􀆳s territorial spatial governance. It requires a literature review to inform the application and practice of TOD in China. From a dual perspective of theory and methodology, this paper systematically reviewed related studies of TOD from four aspects, namely the TOD conceptualization, the typology, the consequences and the optimization. Based on the review, challenges both in theory and in methodology were summarized and implications for China 􀆳s territorial spatial governance were discussed, in order to provide references for formulating efficient TOD planning and strategies. [ABSTRACT FROM AUTHOR]

Predicting the future activity location and trajectory of residents can provide essential information for smart urban management such as epidemic control, traffic facilitation, public security, etc. However, the current personal location prediction methods generally focus on the mining of individual’s historical travel patterns, and seldom consider the feature of different travel stay points. This paper aims to propose a location prediction model utilizing stay point feature extraction. The model firstly constructs the historical trajectory links based on trajectory data, performs the location discovery rules to transform historical trajec⁃ tory links into stay point links and clusters the stay points to form clustering links. Secondly, the model ex⁃ tracts the feature information (entry time, departure time, weather and land use) from different stay points and extracts the space feature from clustering links. Finally, the cluster links with feature information is in⁃ troduced into long short ⁃ term memory (LSTM) network for customization to implement the personal loca⁃ tion prediction capability. By using a 23⁃day trajectory location data of more than 3 million volunteer users in Shenzhen city, China. The results show that the location prediction F⁃score of our proposed model is bet⁃ ter than variable order Markov model (about 5.5% performance gains) and the traditional N⁃order Markov model (about 7% performance gains). The model also introduces approximately 6.6% performance gains by utilizing the temporal and weather features of stay points. The model shows the capability of utilizing travel stay point feature for personal next location prediction. [ABSTRACT FROM AUTHOR]