About the Project

The project is aimed at providing flexible and efficient multilevel visualization of big spatial data. It is a platform designed for effective visualization of vector data irrespective of the format, shape and the type of visualization. This website not only provides some state of art visualization that we have worked with, but also invites people to try out the visualization with their own data for which we provide some super easy tutorials, with very simple setups.


The paramount growth of data in recent years is undeniable, out of which approximately 60% is spatial data. Additioanlly most of these data have billions of records, making them difficult to process. The need of inerteractive visualization of these spatial data is hence unavaoidable.
A popular approach for interactive big spatial data visualization is through multilevel image indexes which organizes fixed-sized tiles in a pyramidal structure to produce multilevel visualization. This leads to an exponential growth in the the number of tiles with growing levels. For example, a webmap with 18 zoom levels can have 90 billion tiles. Another common approach is to create data index like R-tree, R+ tree and generate images from them only on user requests. This method however fails to serve big data in real time.
When a user visualizes these maps, they execute visualization query by clicking on a position of the map that they want to visualize or by zooming in or out of a particular area. The visualization server is repsonsible for generating these images in real time so that the user enjoys absolute interactivity. The indexes discussed above are responsible for providing the user with the desired interactivity.
The image indexes with all pregenerated image tiles are highly interactive, but has a high preprocessing overhead. On the contrary, the data indexes have no such downside. But they are designed to respond only to range queries, only when the range is small enough to processed on-the-fly.
Our design meets the two design midway and provides an index that is capable of having both data and image components in the same index at the same time. To top it, we also have a controlling parameter which decides the type of each of the nodes (data or image) in an index. In our first paper, this parameter is the size of the individual nodes.


To be able to visualize, analyze or infer any information from any spatial or spatio-temporal datsets in real time, irrespective of the size or type of input data. We wish to make it a to-go platform for the scientific community, for visualizing any kind of spatio-temporal datasets.