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There have been many changes in Geographical Information Systems (GIS) technology which are beneficial for analysis and visualisation of Geographical Information (GI) but these have failed to be applied to non GIS users. The development of a computer based working environment for using GI within generic decision-making situations is a necessity if more use is to be made of GI by non GIS specialists. Problems are outlined for the incorporation of GI into existing systems : GIS and Spatial Decision Support Systems (SDSS). Issues of usability, integration of software/information and functionality have slowed the wider acceptance of GIS. 'These issues have lead to the development of a conceptual functional model of a Geographical Operating Environment (GOE). The GOE is proposed as a solution to the use of GI within a generic computer based decision-making environment for non GIS users. The GOE integrates the use of GI and non GI to allow the user to view and analyse information within a decision-making context. A series of prototypes have been developed and tested, mostly in conjunction with commercial organisations. The research has shown that there is much benefit in facilitating access to GI and non GI by decision-makers and that alternate strategies can be visualised and analysed. However, there are still problems for the mainstream use of GI within generic decision-making using existing computer systems from both a technological and sociological perspective. Decision-makers would benefit from the .additional analytical and visualisation techniques that can be applied to GI through the use of a GI based working environment such as the GOE.

Thesis (M.S.)--California Polytechnic State University, 2009. / Title from PDF title page; viewed on January 11, 2010. Major professor: Franz Kurfess, Ph.D. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Computer Science." "December 2009." Includes bibliographical references (p. 67-72).

An increasing amount of freely available Geographic Information System (GIS) data on the Internet has stimulated recent research into Spatial Information Retrieval (SIR). Typically, SIR looks at the problem of retrieving spatial data on a dataset by dataset basis. However in practice, GIS datasets are generally not analysed in isolation. More often than not multiple datasets are required to create a map for a particular analysis task. To do this using the current SIR techniques, each dataset is retrieved one by one using traditional retrieval methods and manually added to the map. To automate map creation the traditional SIR paradigm of matching a query to a single dataset type must be extended to include discovering relationships between different dataset types. This thesis presents a Bayesian inference retrieval framework that will incorporate expert knowledge in order to retrieve all relevant datasets and automatically create a map given an initial user query. The framework consists of a Bayesian network that utilises causal relationships between GIS datasets. A series of Bayesian learning algorithms are presented that automatically discover these causal linkages from historic expert knowledge about GIS datasets. This new retrieval model improves support for complex and vague queries through the discovered dataset relationships. In addition, the framework will learn which datasets are best suited for particular query input through feedback supplied by the user. This thesis evaluates the new Bayesian Framework for SIR. This was achieved by utilising a test set of queries and responses and measuring the performance of the respective new algorithms against conventional algorithms. This contribution will increase the performance and efficiency of knowledge extraction from GIS by allowing users to focus on interpreting data, instead of focusing on finding which data is relevant to their analysis. In addition, they will allow GIS to reach non-technical people.

No description available.

This thesis documents a research project that has led to the design and prototype implementation of several data storage schemes suited to the efficient multi-scale representation of integrated spatial data. Spatial information systems will benefit from having data models which allow for data to be viewed and analysed at various levels of detail, while the integration of data from different sources will lead to a more accurate representation of reality. The work has addressed two specific problems. The first concerns the design of an integrated multi-scale data model suited for use within Geographical Information Systems. This has led to the development of two data models, each of which allow for the integration of terrain data and topographic data at multiple levels of detail. The models are based on a combination of adapted versions of three previous data structures, namely, the constrained Delaunay pyramid, the line generalisation tree and the fixed grid. The second specific problem addressed in this thesis has been the development of an integrated multi-scale 3-D geological data model, for use within a Geoscientific Information System. This has resulted in a data storage scheme which enables the integration of terrain data, geological outcrop data and borehole data at various levels of detail. The thesis also presents details of prototype database implementations of each of the new data storage schemes. These implementations have served to demonstrate the feasibility and benefits of an integrated multi-scale approach. The research has also brought to light some areas that will need further research before fully functional systems are produced. The final chapter contains, in addition to conclusions made as a result of the research to date, a summary of some of these areas that require future work.

In the state of Virginia, population growth and the associated increases in municipal wastewater, along with the threat of EPA regulations, will increase the need for reductions in phosphorous (P) loads in surface waters in order to meet and maintain water quality standards for the Chesapeake Bay. Agriculture contributes 49% of P entering the Bay; therefore, it can be expected that agriculture will be targeted as a source of P reductions. Spatially variable physical and socioeconomic characteristics of a watershed and its occupant farms affect both the decisions made by farmers and the transport of nutrients. Evidence suggests that spatially variable characteristics should be considered when designing policies to control nonpoint sources of water pollution. However, spatial information can be expensive to collect and the evidence is not conclusive as to the level of information required to analyze specific pollution-control policies. The objective of this study was to estimate the accuracy of predicted compliance costs and changes in P deliveries resulting from mandatory buffer installation and mandatory nutrient management for three alternative levels of information, relative to the population of farms in a Virginia watershed. For each information case, an economic model, FARMPLAN, was used to determine the profit maximizing levels of inputs, outputs and gross margins. Selected crop rotations and P applications were used as inputs to the physical model, PDM, which estimated the levels of P delivered to the watershed outlet. The compliance cost and P reduction estimates for the three alternative cases were compared to those of the population to determine their accuracy. The inclusion of greater levels of spatial information will lead to more accurate estimates of compliance costs and pollution reductions. Estimates of livestock capacity are more important to making accurate predictions than are farm boundaries. Differences in estimates made using different levels of information will be greater when the farmers have greater flexibility in meeting the policy requirements. The implications are that additional spatial information does not aid in the selection of one policy over the other, but can be useful in when estimating costs for budgeting purposes, or when evaluating how farmers will respond to the policy. / Master of Science

New, often free, spatial information applications such as mapping tools, topological imaging, and geographic information systems are becoming increasingly available to the average computer user. These systems, which were once available only to government, scholastic, and corporate institutions with highly skilled operators, are driving a need for new and innovative ways for the average user to navigate and control spatial information intuitively, accurately, and efficiently. Gestures provide a method of control that is well suited to navigating the large datasets often associated with spatial information applications. Several different types of gestures and different applications that navigate spatial data are examined. This leads to the introduction of a system that uses a visual head tracking scheme for controlling of the most common navigation action in the most common type of spatial information application, panning a 2-D map. The proposed head tracking scheme uses head pointing to control the direction of panning. The head tracking control is evaluated against the traditional control methods of the mouse and touchpad, showing a significant performance increase over the touchpad and comparable performance to the mouse, despite limited practice with head tracking.

This paper examines the processing of visual information beyond the creation of the early representations. A fundamental requirement at this level is the capacity to establish visually abstract shape properties and spatial relations. This capacity plays a major role in object recognition, visually guided manipulation, and more abstract visual thinking. For the human visual system, the perception of spatial properties and relations that are complex from a computational standpoint, nevertheless often appears immediate and effortless. This apparent immediateness and ease of perceiving spatial relations is, however, deceiving. It conceals in fact a complex array of processes highly specialized for the task. The proficiency of the human system in analyzing spatial information far surpasses the capacities of current artificial systems. The study of the computations that underlie this competence may therefore lead to the development of new more efficient processors for the spatial analysis of visual information. It is suggested that the perception of spatial relations is achieved by the application to the base representations of visual routines that are composed of sequences of elemental operations. Routines for different properties and relations share elemental operations. Using a fixed set of basic operations, the visual system can assemble different routines to extract an unbounded variety of shape properties and spatial relations. At a more detailed level, a number of plausible basic operations are suggested, based primarily on their potential usefulness, and supported in part by empirical evidence. The operations discussed include shifting of the processing focus, indexing to an odd-man-out location, bounded activation, boundary tracing, and marking. The problem of assembling such elemental operations into meaningful visual routines is discussed briefly.

In the past decade efforts to develop spatial data infrastructures (SDIs) have migrated from the initial “top-down” national approaches to “bottom-up” and cross jurisdictional efforts at the sub-national level. Although national SDI developments are fundamental to building the SDI culture and policy, it is sub-national and local SDI development that will deliver the immediate benefits to citizens and the community. In countries which have highly decentralised federations of states such as Australia, United States and Canada, the challenge is how to co-ordinate the literally thousands of often small local government jurisdictions which are important contributors to state and local SDIs. In recent years, a number of co-operative spatial data sharing partnerships between local and state government have emerged in various countries around the world. These partnerships are relatively new initiatives that have been established to facilitate more effective sharing of spatial data between organisations, but also as a mechanism to contribute to SDI development. To maximise the benefits from these partnerships it is essential to understand the factors that contribute to their successful operation and sustainability. Therefore, the focus of this research is to understand these collaborative arrangements so that future data sharing initiatives can be improved and sustained.