Voronoi representation for areal data processing in data-rich environments

Abstract

Privacy and data explosion issues are major concerns in intelligence and security informatics. An areal data representation is a popular way to overcome these two issues. As data grows at an unprecedented rate, there still needs an improvement in area data representations to be more scalable. This paper determines the potential for an alternative areal representation that can offer performance benefits over traditional methods for use within data-rich environments. From the experiments performed, improvements are promising, especially within time-critical applications that need to consider large amounts of data quickly.