High performance integrated spatial big data analytics

Abstract

The growth of spatial big data has been explosive thanks to cost-effective and ubiquitous positioning technologies, and the generation of data from multiple sources in multi-forms. Such emerging spatial data has high potential to create new insights and values for our life through spatial analytics. However, spatial data analytics faces two major challenges. First, spatial data is both data-and compute-intensive due to the massive amounts of data and the multi-dimensional nature, which requires high performance spatial computing infrastructure and methods. Second, spatial big data sources are often isolated, for example, OpenStreetMap, census data and Twitter tweets are independent data sources. This leads to incompleteness of information and sometimes limited data accuracy, thus limited values from the data. Integrating spatial big data analytics by consolidating multiple data sources provides significant potential for data quality improvement in terms of completeness and accuracy, and much increased values derived from the data. In this paper, we present our vision of a high performance integrated spatial big data analytics framework. We provide a scalable spatial query based data integration engine with MapReduce, and demonstrate integrated spatial data analytics through a few use cases in our preliminary work. We then present our future plan on integrated spatial big data analytics for improving public health research and applications.