High Performance Spatiotemporal Visual Analytics Technologies and Its Applications in Big Socioeconomic Data Analysis

The boom in publicly accessible big data sources, such as social networks and the internet of things, is creating new opportunities as well as new technologies and applications to study and understand socioeconomic activities. Visual analytics is promising social sensing approach that helps users effectively identify interesting events and discover hidden patterns, anomalies, and relations from datasets. Big data however also represents a challenge to current data processing and analytical technologies, given the heterogeneity of multi-source data, extremely large data volume and intensive computation impede the efficiency and feasibility of visual analysis. In this chapter, we introduce cutting-edge data storage, computing, and visualization technologies that can tackle these challenges. Data storage and index mechanism optimize query and reduce response time for multi-dimensional big data access; high Performance Computing (HPC) technology accelerates big data preprocessing and mining; web-based interactive visualization technologies allow users to synthesize and manipulate data in a dynamic and user-friendly manner. Taking big enterprise registration data as an example, we demonstrate how to apply these new technologies to build a web-based visual analytics framework that supports studies in economic geography. The necessity and value of these technologies for big socioeconomic data processing and visualization is analyzed and discussed. We conclude, by analyzing and discussing the versatility and portability of these technologies in other application scenarios such as in the humanities and social sciences.