Crowdsourcing is a computing paradigm where humans are actively involved in a computing task, especially for tasks that are intrinsically easier for humans than for computers. Spatial crowdsourcing is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy, where tasks are spatiotemporal and must be completed at a specific location and time. In fact, spatial crowdsourcing has stimulated a series of recent industrial successes including sharing economy for urban services (Uber and Gigwalk) and spatiotemporal data collection (OpenStreetMap and Waze). This survey dives deep into the challenges and techniques brought by the unique characteristics of spatial crowdsourcing. Particularly, we identify four core algorithmic issues in spatial crowdsourcing: (1) task assignment, (2) quality control, (3) incentive mechanism design, and (4) privacy protection. We conduct a comprehensive and systematic review of existing research on the aforementioned four issues. We also analyze representative spatial crowdsourcing applications and explain how they are enabled by these four technical issues. Finally, we discuss open questions that need to be addressed for future spatial crowdsourcing research and applications.

中文翻译：

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空间众包：一项调查

众包是一种计算范式，其中人积极参与计算任务，尤其是对于人类而言，本质上比计算机容易的任务。在移动互联网和共享经济时代，空间众包是一种日益流行的众包类别，其中任务是时空性的，必须在特定的位置和时间完成。实际上，空间众包刺激了一系列近期的工业成功，包括为城市服务共享经济（Uber和Gigwalk）和时空数据收集（OpenStreetMap和Waze）。这项调查深入探讨了空间众包的独特特征带来的挑战和技术。特别是，我们确定了空间众包中的四个核心算法问题：（1）任务分配，（2）质量控制，（3）激励机制设计，以及（4）隐私保护。我们对上述四个问题的现有研究进行了全面而系统的审查。我们还将分析代表性的空间众包应用程序，并解释这四个技术问题如何实现它们。最后，我们讨论了未来空间众包研究和应用需要解决的未解决问题。