A Geographic Information System (GIS) is a computer system for capturing, storing, checking, and displaying data related to positions on Earth’s surface. GIS can show many different kinds of data on one map, such as streets, buildings, and vegetation. This enables people to more easily see, analyze, and understand patterns and relationships.
With the birth of the Web, we have seen immense value and broad applicability of GIS and the endless possibilities that can be achieved when integrating GIS systems into flexible Web architectures for use with modern IT infrastructure.
Traditional WebGIS systems has limited capability in handling large-scale geospatial data. This project integrates a GPU-accelerated spatial database backend with Google Maps API to support visualizing and querying large-scale geospatial data in a web environment.
The large data set used in this project is the global biodiversity data on species distributions. This data contains more than 1.6 million species, collected over three centuries of natural history exploration and including current observations from citizen scientists, researchers and automated monitoring programmes.
The size and nature of this data makes this project a good case-study to take advantage of the massive capabilities of the GPU, we hope to achieve significant performance speedup compared to traditional Web GIS applications run on CPU.