Scholarly record
AN OPEN DATA CROWDSOURCING APPROACH FOR ENVIRONMENTAL NOISE POLLUTION MAPPING
Abstract
Open data is undoubtedly one of the most exciting innovations of the last decade. Data that can be (re)used freely and without restrictions is a key driver for achieving many environmental goals. Through the use of open crowdsourcing tools, noise monitoring is enabled by the active participation of citizens who measure noise and thus contribute to the creation of a dynamic noise map. This is consistent with the lifecycle of open data - the design of the process and practices for handling data from its creation, to the provision of open data, to its use by various parties. By participating in the crowdsourcing processes, users act as both consumers and providers of data; with enhanced capabilities of data users, such as commenting, rating, processing, or customizing to their specific needs; and then publishing new versions of the same or their own new datasets. In this way, better communication and collaboration between data users and data providers creates another root for a sustainable ecosystem by closing information and evaluation loops by supporting a broader range of use cases in services and applications on ecological issues.
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References11
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