Abstract
Understanding the situation distribution is a fundamental but important step in the emergency response to disaster. There are various emergency related spatial data available on Internet; however, it is still a big challenge in clustering the dynamic big spatial data. In this study, we provide a dynamic spatial clustering (DSC) to efficiently load and cluster the spatial big data based on a hierarchical-partition model (HPM). We have modeled the DSC to understand the distribution of emergency (e.g. Kumamoto earthquake in May 2016) from spatial data in tweets. The major contributions in the HPM-based DSC include loading dynamic big spatial data with optimal utilization of external memory, and rapid clustering to detect the dense regions of targeted emergency.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
