ITR: Understanding Change in Spatiotemporal Data

University Of California-Riverside, Riverside CA

Investigators

Abstract

Spatiotemporal data appears in many real-life applications (global change, surveillance, transportation etc.) Together with regular attributes such data contains topological as well as temporal attributes. This combination creates novel interesting problems. Moreover, spatiotemporal data is usually presented in "streams" which drastically affects the data processing methods. We propose general exploratory techniques that will allow the user not only to verify specific hyphotheses, but more importantly, to understand the underlying process that controls the changes recorded in the spatiotemporal datasets. In particular: 1. We will first first address the problem of performing on-line analysis on spatiotemporal streams. This part of the project will provide the appropriate tools for the major effort of this proposal, i.e., understanding patterns of change. 2. We will then address the problem of understanding patterns of change in specific spatiotemporal applications, namely: (i) epidemiological, (ii) environmental and (iii) surveillance applications. In each application we will first define low-level analysis primitives. Such primitives are simpler to identify in each application area. We will then use combinations of such primitives to describe complex (high-level) patterns of change.