GeoSpatial Decision-Making

I am involved with the GeoDec (Geospatial Decision Making) project that allows navigation through a 3-D model and enables users to ask queries and get information about the area in a convenient way. GeoDec is a part of the GeoRealism computing platform that "gives humans the ability to capture, model, integrate, and query static and dynamic data from the real world in real time at fidelities not currently available." This work has been featured in MSNBC. See the article and its video here.

Check out the following demo of me demonstrating Negaah, GeoRealism's visualization interface.

Moving Object Tracking and Querying on Google Earth

This is the first moving object tracking and querying interface based on Google Earth. This work is featured in Google Earth Blog. Read the article here. Check out a short demo of the interface below.

Location Privacy

Several location-based services and context-aware applications are emerging usually not paying enough attention to important privacy threats associated with such services. Although they usually support anonymous usage of their services, they can not do much about another important private information: User's location. Location privacy. According to Forrester Research,$15 billion in online revenue has been held back due to consumer concerns over privacy protection online in 2001. Current techniques usually try to blur user's location by spatial cloaking. However we believe there are better ways to do it which doesn't require users to trade privacy for quality of service. Read More.
 

Vector Data Compression

Vector data and in particular road networks are being queried, hosted, and processed by many application contexts such as mobile computing. However, many hosting/processing clients such as PDAs cannot afford this bulky data due to their storage and transmission limitations. In particular, the result of a typical spatial query such as window query is too huge for a transfer-and-store scenario. While several general/vector data compression schemes have been studied by different communities, we propose a novel approach in vector data compression which is easily integrated within a geospatial query processing system. It uses line aggregation to reduce the number of relevant tuples and Huffman compression to achieve a multi-resolution compressed representation of a road network database. Our empirical results verify that our approach exhibits high compression ratios and fast query processing.