Analytics of big geosocial media and crowdsourced data

Numerous crowdsourcing and social media platforms such as CrowdSpring,Idea Bounty,DesignCrowd,Facebook,Twitter,Flickr,Weibo,WeChat,and Instagram are creating and sharing vast amounts of user-generated content that can reveal timely and useful infor-mation for detecting traffic patterns,mitigating security risks and other types of time-critical events,discovering social structures characteristics,predicting human movement,etc.Crowdsourcing,also known as volunteered geographic information(VGI),has added a new dimension to traditional geospatial data acquisition by providing fine-grained proxy data for human activity research in urban studies(Chen et al.,2016;Niu&Silva,2020).However,analyzing big geosocial media and crowdsourced data brings significant methodological and theoretical challenges due to the uncertain user representability when referring to human behavior in general,the inherent noisy data that requires high-performance cost of preprocessing,and the heterogeneity in quality and quantity of sources.In particular,geosocial media data and their derived metrics can provide valuable insights and policy strategies,but they require a deep understanding of what the metrics actually measure(Zook,2017).All of these underpin complex assessments,not mention-ing the ethnic and privacy issues.Therefore,new sets of methods and tools are required to analyze the big data from crowdsourcing and social media platforms.

Clustering spatio-temporal bi-partite graphs for finding crowdsourcing communities in IoMT networks

The Internet of Moving Things is rapidly becoming a reality where intelligent devices and infrastructures are fostering real-time data sus-tainability in smart cities and advancing crowdsourced tasks to improve energy consumption,waste management,and traffic operations.These intelligent devices create a complex network scenario in which they often move together or in conjunction with one another to complete crowdsourced tasks.Our research premise is that mobility relationships matter when performing these tasks,and therefore,a graph model based on representing the changes in mobility relation-ships is needed to help identify the neighbour devices that are moving close to one another in our physical world but also seamlessly con-nected in their virtual world.We propose a bi-partite community mobility graph model for linking intelligent devices in both virtual and physical worlds,as well as reaching a trade-off between crowd-sourced tasks designed with explicit and implicit citizen participation.This paper aims to explore a bi-partite graph as a promising spatiotemporal representation of IoMT networks since changes in mobility relationships over time can indicate volunteer organisation at the device and community levels.The Louvain community detection method is proposed to find communities of intelligent devices to reveal a value conscious participation of citizens.The proposed bi-partite graph model is evaluated using a real-world scenario in transportation,confirming the main role of evolving communities in developing crowdsourcing IoMT networks.