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Erschienen in:
2017 | OriginalPaper | Buchkapitel
CyberGIS-Enabled Urban Sensing from Volunteered Citizen Participation Using Mobile Devices
verfasst von : Junjun Yin, Yizhao Gao, Shaowen Wang
Erschienen in: Seeing Cities Through Big Data
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Abstract
Environmental pollution has significant impact on citizens’ health and wellbeing in urban settings. While a variety of sensors have been integrated into today’s urban environments for measuring various pollution factors such as air quality and noise, to set up sensor networks or employ surveyors to collect urban pollution datasets remains costly and may involve legal implications. An alternative approach is based on the notion of volunteered citizens as sensors for collecting, updating and disseminating urban environmental measurements using mobile devices. A Big Data scenario emerges as large-scale crowdsourcing activities tend to generate sizable and unstructured datasets with near real-time updates. Conventional computational infrastructures are inadequate for handling such Big Data, for example, designing a “one-fits-all” database schema to accommodate diverse measurements, or dynamically generating pollution maps based on visual analytical workflows.
This paper describes a CyberGIS-enabled urban sensing framework to facilitate the volunteered participation of citizens in sensing environmental pollutions using mobile devices. Since CyberGIS is based on advanced cyberinfrastructure and characterized as high performance, distributed, and collaborative GIS, the framework enables interactive visual analytics for big urban data. Specifically, this framework integrates a MongoDB cluster for data management (without requiring a predefined schema), a MapReduce approach to extracting and aggregating sensor measurements, and a scalable kernel smoothing algorithm using a graphics processing unit (GPU) for rapid pollution map generation. We demonstrate the functionality of this framework though a use case scenario of mapping noise levels, where an implemented mobile application is used for capturing geo-tagged and time-stamped noise level measurements as engaged users move around in urban settings.