Spatial Crowdsourcing

Crowdsourcing is rapidly evolving and applied in situations where ideas, labour, opinion or expertise of large groups of people are used. Crowdsourcing is now used in various policy-making initiatives; however, this use has usually focused on open collaboration platforms and specific stages of the policy process, such as agenda-setting and policy evaluations. Other forms of crowdsourcing have been neglected in policy-making, with a few exceptions. This article examines crowdsourcing as a tool for policy-making, and explores the nuances of the technology and its use and implications for different stages of the policy process. The article addresses questions surrounding the role of crowdsourcing and whether it can be considered as a policy tool or as a technological enabler and investigates the current trends and future directions of crowdsourcing.

Spatial Crowdsourcing (SC) is a novel platform that engages individuals, groups and communities in the act of collecting, analyzing, and disseminating various types of spatial data. This method of data collection can significantly reduce cost and turnover time, and is particularly useful in case of environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce a framework for crowdsourcing hyperlocal information, in which only those workers who are already within the spatiotemporal vicinity of a task are eligible candidates to report data, e.g., the precipitation level at their area and time. In this setting, a subset of candidate workers whose size is constrained by a predefined budget, can be activated to perform tasks. The challenge is to maximize task coverage under budget constraint, despite the dynamic arrivals of workers and tasks as well as their co-location relationship. We investigate two variants of the problem: fixed and dynamic budgets. For each variant, we study the complexity of its off-line version and then propose effective methods for the online scenario that exploit the spatial and temporal knowledge acquired over time. Extensive experiments with real-world and synthetic datasets show the effectiveness and efficiency of our proposed framework.

Spatial Crowdsourcing (SC) is a transformative platform that engages individuals in collecting and analyzing environmental, social and other spatio-temporal information. SC outsources spatio-temporal tasks to a set of workers, i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations. However, current solutions require the workers to disclose their locations to untrusted parties. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We address scenarios with both static and dynamic (i.e., moving) datasets of workers. Experimental results on real-world data show that the proposed technique protects location privacy without incurring significant performance overhead.

In many resource-rich developing countries, policymakers, academics, and practitioners alike seek to promote citizen engagement and monitoring in the management of natural resource revenues. However, many of the approaches to improve citizen engagement have not been effective as citizens often lack access to relevant information and opportunities to voice their concerns, and governments have faced financial challenges in engaging citizens. In this article, the authors propose spatial crowdsourcing as an alternative to the traditional ways of encouraging citizen engagement. The proposed approach is illustrated using a simple, intuitive multimedia-based spatial crowdsourcing platform that was tested among farmers who were benefiting from a petroleum-funded project in Ghana. The farmers accessed the platform via their mobile phones and completed a survey relating to the project and petroleum revenue management in Ghana. The findings suggest that spatial crowdsourcing is a promising approach to promote informed citizen engagement in the context of natural resource revenue management. In particular, the farmers indicated that an opportunity to give feedback on the project through a user-friendly platform was very important for them. Furthermore, the representatives of both the government and an oversight body for petroleum revenue management regarded spatial crowdsourcing as a useful tool for collecting feedback on petroleum-funded projects and to increase citizen engagement in natural resource management in general. The authors conclude that although spatial crowdsourcing can help in citizen engagement, its effectiveness in the management of natural resource revenues depends on behavioural changes in governments and citizens.

Spatial crowdsourcing (a.k.a mobile crowdsourcing) is a new paradigm of data collection, which has been emerged in the last few years to enable workers to perform tasks in the physical world. The objective of spatial crowdsourcing is to outsource a set of location-specific tasks to a set of workers , in which the workers are required to physically be at the task locations to complete them, i.e., taking pictures or collecting air quality information at specified locations of interest. In this paper, we discuss the unique challenges of spatial crowdsourcing: task assignment, incentive mechanism , worker's location privacy and the absence of real-world datasets. Thereafter, we present our current approaches to those issues.

Spatial Crowdsourcing (SC) is a novel and transformative platform that engages individuals, groups and communities in the act of collecting, analyzing, and disseminating environmental, social and other spatio-temporal information. SC outsources a set of spatio-temporal tasks to a set of workers, i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations of interest. Protecting location privacy is an important concern in SC, as an adversary with access to individual whereabouts can infer sensitive details about a person (e.g., health status, political views). Due to the challenging nature of protecting worker privacy in SC, solutions for this problem are quite complex, and require tuning of several parameters to obtain satisfactory results. In this paper, we propose PrivGeoCrowd, a toolbox for interactive visualization and tuning of SC private task assignment methods. This toolbox is useful for several real-world entities that are involved in SC, such as: mobile phone operators that want to sanitize datasets with worker locations, spatial task requesters, and SC-service providers that match workers to tasks.