Spatial data often embedded with geographic references are important to numerous scientific domains (e.g., ecology, geography and spatial sciences, geosciences, and social sciences, to name just a few), and also beneficial to solving many critical societal problems (e.g., environmental and urban sustainability). In recent years, however, this type of data has exploded to massive size and significant complexity as increasingly sophisticated location-based sensors and devices (e.g., social networks, smartphones, and environmental sensors) are widely deployed and used. The big spatial data collected from numerous sources are extensively used to instrument our natural, human and social systems at unprecedented scales while providing us with tremendous opportunities to gain dynamic insight into complex phenomena. However, to synthesize various spatial data – a foundational process of various scientific problem-solving practices – has become increasingly difficult and is not scalable to the significant size, complexity, and diversity of spatial data. Therefore, the overarching goal of this project is to establish fundamental and scalable capabilities for spatial data synthesis through integration with cyberGIS (geographic information systems based on advanced cyberinfrastructure (CI)) and novel cloud computing strategies to enable cutting-edge data-intensive research and education across multiple scientific communities.