Going Beyond Citizen Data Collection with Mapster: A Mobile+Cloud Real-Time Citizen Science Experiment

摘要

Citizens have always played an important role in emergency management such as urban flooding response. New information and communication technologies such as smart phones and computer-based social networks have great potential to transform the roles of citizens in emergency management. However, current digital citizen science projects are usually limited in three areas: 1) limited one-way citizen participation, 2) no processing and integration of citizens' reports with other existing infrastructure sensing data, 3) no personalized near-real-time spatiotemporal visualization tools for citizens to instantly view aggregated data to gain updated situational awareness. We developed a Mapster application that specifically addresses these issues. First, we leveraged Twitter's geo-referenced tweets functionality to design a customized smart phone application for citizens to report a set of events that have been identified in past urban flooding situations such as "basement flooding" and "powerline down" etc. Second, a Cloud-based semantic streaming data harvesting and processing tool was developed to fetch and process both the Twitter feeds and other infrastructure sensing data such as US National Weather Service's radar data. Third, a user can instantly explore the heterogeneous data processed and provided by the Cloud service through a map-based spatiotemporal animation tool on the smart phone to see how all the events evolve before, during, and after a storm. Such a two-way information flow significantly improves citizen participation and their sense of situational awareness. We present our architecture, implementation, and discussion of issues on citizen science data collection platforms, integration of heterogeneous data sources and future work plan.