Application of Spatial Concepts to Genome Data

This project will investigate the application of geographic information science concepts and methods to the modeling and analysis of genome data. The primary objective of the research is to develop a data model for genomes that supports the graphical exploration of the higher order spatial arrangement of genome features through spatial queries and spatial data analysis tools. The spatial genome model formalizes topological and order relationships among genome features (before, after, overlap), uses metric properties to refine spatial topologies, and includes representations of features that have uncertain metric properties. The genome spatial model enhances the integrative and comparative potential of genome data by providing the foundation for more powerful spatial reasoning and inferences than can be achieved by data models that incorporate only a small subset of possible temporal-spatial relationships among genome features (e.g. order and distance). The research represents a logical extension from current feature by feature analytical approaches of genome studies to one that allows biologists to ask questions about the contextual and organizational significance of the spatial arrangement of genome features. These functional capabilities should, in turn, aid in the automation of repetitive analytical tasks associated with the mapping of genome features and drive the discovery of biologically significant aspects of genome organization and function

Detection and Generalization of Spatio-temporal Trajectories for Motion Imagery

In today\u27s world of vast information availability users often confront large unorganized amounts of data with limited tools for managing them. Motion imagery datasets have become increasingly popular means for exposing and disseminating information. Commonly, moving objects are of primary interest in modeling such datasets. Users may require different levels of detail mainly for visualization and further processing purposes according to the application at hand. In this thesis we exploit the geometric attributes of objects for dataset summarization by using a series of image processing and neural network tools. In order to form data summaries we select representative time instances through the segmentation of an object\u27s spatio-temporal trajectory lines. High movement variation instances are selected through a new hybrid self-organizing map (SOM) technique to describe a single spatio-temporal trajectory. Multiple objects move in diverse yet classifiable patterns. In order to group corresponding trajectories we utilize an abstraction mechanism that investigates a vague moving relevance between the data in space and time. Thus, we introduce the spatio-temporal neighborhood unit as a variable generalization surface. By altering the unit\u27s dimensions, scaled generalization is accomplished. Common complications in tracking applications that include occlusion, noise, information gaps and unconnected segments of data sequences are addressed through the hybrid-SOM analysis. Nevertheless, entangled data sequences where no information on which data entry belongs to each corresponding trajectory are frequently evident. A multidimensional classification technique that combines geometric and backpropagation neural network implementation is used to distinguish between trajectory data. Further more, modeling and summarization of two-dimensional phenomena evolving in time brings forward the novel concept of spatio-temporal helixes as compact event representations. The phenomena models are comprised of SOM movement nodes (spines) and cardinality shape-change descriptors (prongs). While we focus on the analysis of MI datasets, the framework can be generalized to function with other types of spatio-temporal datasets. Multiple scale generalization is allowed in a dynamic significance-based scale rather than a constant one. The constructed summaries are not just a visualization product but they support further processing for metadata creation, indexing, and querying. Experimentation, comparisons and error estimations for each technique support the analyses discussed

Modeling Multiple Granularities of Spatial Objects

People conceptualize objects in an information space over different levels of details or granularities and shift among these granularities as necessary for the task at hand. Shifting among granularities is fundamental for understanding and reasoning about an information space. In general, shifting to a coarser granularity can improve one\u27s understanding of a complex information space, whereas shifting to a more detailed granularity reveals information that is otherwise unknown. To arrive at a coarser granularity. objects must be generalized. There are multiple ways to perform generalization. Several generalization methods have been adopted from the abstraction processes that are intuitively carried out by people. Although, people seem to be able to carry out abstractions and generalize objects with ease. formalizing these generalization and shifts between them in an information system, such as geographic information system, still offers many challenges. A set of rules capturing multiple granularities of objects and the use of these granularities for enhanced reasoning and browsing is yet to be well researched. This thesis pursues an approach for arriving at multiple granularities of spatial objects based on the concept of coarsening. Coarsening refers to the process of transforming a representation of objects into a less detailed representation. The focus of this thesis is to develop a set of coarsening operators that are based on the objects\u27 attributes, attribute values and relations with other objects, such as containment, connectivity, and nearness. for arriving at coarser or amalgamated objects. As a result. a set of four coarsening operators—group, group, compose, coexist, and filter are defined. A framework, called a granularity graph. is presented for modeling the application of coarsening operators iteratively to form amalgamated objects. A granularity graph can be used to browse through objects at different granularities, to retrieve objects that are at different granularities, and to examine how the granularities are related to each other. There can occur long sequences of operators between objects in the graph, which need to be simplified. Compositions of coarsening operators are derived to collapse or simplify the chain of operators. The semantics associated with objects amalgamations enable to determine correct results of the compositions of coarsening operators. The composition of operators enables to determine all the possible ways for arriving at a coarser granularity of objects from a set of objects. Capturing these different ways facilitates enhanced reasoning of how objects at multiple granularities are related to each other

Modelling the spatial-temporal movement of tourists

Tourism is one of the most rapidly developing industries in the world. The study of spatio-temporal movement models of tourists are undertaken in variety of disciplines such as tourism, geography, mathematics, economics and artificial intelligence. Knowledge from these different fields has been difficult to integrate because tourist movement research has been conducted at different spatial and temporal scales. This thesis establishes a methodology for modelling the spatial-temporal movement of tourists and defines the spatial-temporal movement of tourists at both the macro and micro level. At the macro level, the sequence of tourist movements is modelled and the trend for tourist movements is predicted based on Markov Chain theory (MC). Log-linear models are then adopted to test the significance of the movement patterns of tourists. Tourism market segmentation based on the significant movement patterns of tourists is implemented using the EM (Expectation-Maximisation) algorithm. At the micro level, this thesis investigates the wayfinding decision-making processes of tourists. Four wayfinding models are developed and the relationships between the roles of landmarks and wayfinding decision-making are also discussed for each type of the wayfinding processes. The transition of a tourist movement between the macro and micro levels was examined based on the spatio-temporal zooming theory. A case study of Phillip Island, Victoria, Australia is undertaken to implement and evaluate the tourist movement models established in this thesis. Two surveys were conducted on Phillip Island to collect the macro and micro level movement data of tourists. As results show particular groups of tourists travelling with the same movement patterns have unique characteristics such as age and travel behaviours such as mode of transport. Effective tour packages can be designed based on significant movement patterns and the corresponding target markets. Tourists with various age groups, residency, gender and different levels of familiarity with physical environment have different wayfinding behaviours. The results of this study have been applied to tourism management on Phillip Island and the novel methods developed in this thesis have proved to be useful in improving park facilities and services provided to tourists, in designing tour packages for tourism market promotion and in understanding tourist wayfinding behaviours

Shifts in Detail Through Temporal Zooming

Spatio-temporal knowledge representation often requires shifting from one level of detail to another so that users can carry out a desired task. Geographic information systems typically treat such alterations in detail with respect to the geometric properties of objects. In this paper, we extend the theories based on geometrical considerations only to an approach that focuses on shifts between levels of temporal detail. The approach is based on a model of change to identifiable objects and describes temporal zooming that involves expanding or collapsing the transitions between identity states of objects. This work offers promising new directions for spatio-temporal query languages. 1.Introduction People view the world at different levels of detail, abstracting from the world only those things that serve their present interests [1-4]. These changes in detail enable people to translate the complexities of the real world into simpler representations. With geographic information systems ..