LAUSR

Abstract Spatial crowdsourcing (SC) has gained much attention in recent years. On SC platforms, requesters can publish spatial tasks on a specific topic such as taking photos at certain locations which is reflected by some tags of the task, and workers can choose tasks according to their tags. An interesting phenomenon is that workers often form groups based on their social relationships and common interests to perform same tasks. A nature question often raised when a task is published is how to predict which (or how many) workers are attracted by the task if its tags are specified. In particular, the tags of a new task affect the willingness of the workers to choose it. On the other hand, workers are also affected by the willingness of their co-workers or friends when deciding to choose a task or not. In this paper, we study the problem of potential workers estimation for a spatial crowdsourcing task, the Worker Collaborative Group Estimation (WCGE) problem, and model whether workers will join the group of a task as a game. We present efficient algorithms to find the Nash Equilibrium of the game and estimate the potential workers for the new task. Using synthetic datasets, we experimentally study the performance of proposed solutions. Our solutions can also help understand big geospatial data for self-driving cars and more intelligent transportation applications.