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A new generation of bike-sharing services is emerging in China. With this service, bikes can be unlocked and paid by using a smartphone and then picked up and left anywhere at users' convenience. The unprecedented development of dockless bike-sharing services results in considerable socioeconomic and environmental benefits but also creates new urban issues. One of the most severe issues is users' inappropriate parking behaviour. To solve this problem, electric fence (or geo-fence) policy and technology have been introduced in China to guide users to park bikes in designated zones. In this paper, we first propose a methodological framework to support electric fence planning for dockless bike-sharing services. We then apply our framework in a case study of Shanghai using a big dataset of bike trips. Results show that when the number of planned electric fences is 7,500, our electric fence plan can cover 91.8% of total parking demand. In addition, our plan can ensure that at least 95.8% of all bikes can be docked at one of planned electric fences and can help efficiently and accurately determine suitable locations for setting up planned electric fences.

Studying the impact of social events is important for the sustainable development of society. Given the growing popularity of social media applications, social sensing networks with users acting as smart social sensors provide a unique channel for understanding social events. Current research on social events through geo-tagged social media is mainly focused on the extraction of information about when, where, and what happened, i.e., event detection. There is a trend towards the machine learning of more complex events from even larger input data. This research work will undoubtedly lead to a better understanding of big geo-data. In this study, however, we start from known or detected events, raising further questions on how they happened, how they affect people’s lives, and for how long. By combining machine learning, natural language processing, and visualization methods in a generic analytical framework, we attempt to interpret the impact of known social events from the dimensions of time, space, and semantics based on geo-tagged social media data. The whole analysis process consists of four parts: (1) preprocessing; (2) extraction of event-related information; (3) analysis of event impact; and (4) visualization. We conducted a case study on the “2014 Shanghai Stampede” event on the basis of Chinese Sina Weibo data. The results are visualized in various ways, thus ensuring the feasibility and effectiveness of our proposed framework. Both the methods and the case study can serve as decision references for situational awareness and city management.

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