Possible research: Spatial computational resource allocation
see also: CyberGIS’16 panel
Data structures are fundamental to the efficiency of algorithms pertaining to transfer and storage, computation, and visualization. Parallel and distributed computing comes in many implementations whose purposes vary greatly. Using centralized computing networks, new resources are available to more institutions, however the bridge between onsite spatial data collection and offsite computing is uncertain, even in terms of data structuring. The changes in resolution and computational needs have brought bitmap and vector closer than ever, however the software resources rely on centralized resources, for which there are few designed for LiDAR terrain mapping.
1: Study data structures to store spatial information. Do aspects of existing structures resolve any problems faced by users?
2: Study whether spatial data compression could be implemented to improve computability and
3: Study methods for data browsing and distributed storage solutions. Big data systems may limit the filesizes remote end users can personally compute with, however some data must be represented by the remote end user.