Using a cartographic modeling language to manipulate spectral satellite imagery

Land related information about the Earth's surface is commonIJ found in two forms: (1) map infornlation and (2) satellite image da ta. Satellite imagery provides a good visual picture of what is on the ground but complex image processing is required to interpret features in an image scene. Increasingly, methods are being sought to integrate the knowledge embodied in mop information into the interpretation task, or, alternatively, to bypass interpretation and perform biophysical modeling directly on derived data sources. A cartographic modeling language, as a generic map analysis package, is suggested as a means to integrate geographical knowledge and imagery in a process-oriented view of the Earth. Specialized cartographic models may be developed by users, which incorporate mapping information in performing land classification. In addition, a cartographic modeling language may be enhanced with operators suited to processing remotely sensed imagery. We demonstrate the usefulness of a cartographic modeling language for pre-processing satellite imagery, and define two nerv cartographic operators that evaluate image neighborhoods as post-processing operations to interpret thematic map values. The language and operators are demonstrated with an example image classification task.

How do practitioners use conceptual modeling in practice?

Much research has been devoted over the years to investigating and advancing the techniques and tools used by analysts when they model. As opposed to what academics, software providers and their resellers promote as should be happening, the aim of this research was to determine whether practitioners still embraced conceptual modeling seriously. In addition, what are the most popular techniques and tools used for conceptual modeling? What are the major purposes for which conceptual modeling is used? The study found that the top six most frequently used modeling techniques and methods were ER diagramming, data flow diagramming, systems flowcharting, workflow modeling, UML, and structured charts. Modeling technique use was found to decrease significantly from smaller to medium-sized organizations, but then to increase significantly in larger organizations (proxying for large, complex projects). Technique use was also found to significantly follow an inverted U-shaped curve, contrary to some prior explanations. Additionally, an important contribution of this study was the identification of the factors that uniquely influence the decision of analysts to continue to use modeling, viz., communication (using diagrams) to/from stakeholders, internal knowledge (lack of) of techniques, user expectations management, understanding models' integration into the business, and tool/software deficiencies. The highest ranked purposes for which modeling was undertaken were database design and management, business process documentation, business process improvement, and software development. (c) 2005 Elsevier B.V. All rights reserved.

Understanding conceptual schemas: exploring the role of application and IS domain knowledge

Although information systems (IS) problem solving involves knowledge of both the IS and application domains, little attention has been paid to the role of application domain knowledge. In this study, which is set in the context of conceptual modeling, we examine the effects of both IS and application domain knowledge on different types of schema understanding tasks: syntactic and semantic comprehension tasks and schema-based problem-solving tasks. Our thesis was that while IS domain knowledge is important in solving all such tasks, the role of application domain knowledge is contingent upon the type of understanding task under investigation. We use the theory of cognitive fit to establish theoretical differences in the role of application domain knowledge among the different types of schema understanding tasks. We hypothesize that application domain knowledge does not influence the solution of syntactic and semantic comprehension tasks for which cognitive fit exists, but does influence the solution of schema-based problem-solving tasks for which cognitive fit does not exist. To assess performance on different types of conceptual schema understanding tasks, we conducted a laboratory experiment in which participants with high- and low-IS domain knowledge responded to two equivalent conceptual schemas that represented high and low levels of application knowledge (familiar and unfamiliar application domains). As expected, we found that IS domain knowledge is important in the solution of all types of conceptual schema understanding tasks in both familiar and unfamiliar applications domains, and that the effect of application domain knowledge is contingent on task type. Our findings for the EER model were similar to those for the ER model. Given the differential effects of application domain knowledge on different types of tasks, this study highlights the importance of considering more than one application domain in designing future studies on conceptual modeling.

Ecological-economic modeling for biodiversity management: Potential, pitfalls, and prospects

Ecologists and economists both use models to help develop strategies for biodiversity management. The practical use of disciplinary models, however, can be limited because ecological models tend not to address the socioeconomic dimension of biodiversity management, whereas economic models tend to neglect the ecological dimension. Given these shortcomings of disciplinary models, there is a necessity to combine ecological and economic knowledge into ecological-economic models. It is insufficient if scientists work separately in their own disciplines and combine their knowledge only when it comes to formulating management recommendations. Such an approach does not capture feedback loops between the ecological and the socioeconomic systems. Furthermore, each discipline poses the management problem in its own way and comes up with its own most appropriate solution. These disciplinary solutions, however are likely to be so different that a combined solution considering aspects of both disciplines cannot be found. Preconditions for a successful model-based integration of ecology and economics include (1) an in-depth knowledge of the two disciplines, (2) the adequate identification and framing of the problem to be investigated, and (3) a common understanding between economists and ecologists of modeling and scale. To further advance ecological-economic modeling the development of common benchmarks, quality controls, and refereeing standards for ecological-economic models is desirable.

Modeling and exploiting behavior patterns in dynamic environments

This paper presents a new approach to improving the effectiveness of autonomous systems that deal with dynamic environments. The basis of the approach is to find repeating patterns of behavior in the dynamic elements of the system, and then to use predictions of the repeating elements to better plan goal directed behavior. It is a layered approach involving classifying, modeling, predicting and exploiting. Classifying involves using observations to place the moving elements into previously defined classes. Modeling involves recording features of the behavior on a coarse grained grid. Exploitation is achieved by integrating predictions from the model into the behavior selection module to improve the utility of the robot's actions. This is in contrast to typical approaches that use the model to select between different strategies or plays. Three methods of adaptation to the dynamic features of the environment are explored. The effectiveness of each method is determined using statistical tests over a number of repeated experiments. The work is presented in the context of predicting opponent behavior in the highly dynamic and multi-agent robot soccer domain (RoboCup)

Patterns in complex systems modeling

The design, development, and use of complex systems models raises a unique class of challenges and potential pitfalls, many of which are commonly recurring problems. Over time, researchers gain experience in this form of modeling, choosing algorithms, techniques, and frameworks that improve the quality, confidence level, and speed of development of their models. This increasing collective experience of complex systems modellers is a resource that should be captured. Fields such as software engineering and architecture have benefited from the development of generic solutions to recurring problems, called patterns. Using pattern development techniques from these fields, insights from communities such as learning and information processing, data mining, bioinformatics, and agent-based modeling can be identified and captured. Collections of such 'pattern languages' would allow knowledge gained through experience to be readily accessible to less-experienced practitioners and to other domains. This paper proposes a methodology for capturing the wisdom of computational modelers by introducing example visualization patterns, and a pattern classification system for analyzing the relationship between micro and macro behaviour in complex systems models. We anticipate that a new field of complex systems patterns will provide an invaluable resource for both practicing and future generations of modelers.

Research, knowledge and design

The discussion about relations between research and design has a number of strands, and presumably motivations. Putting aside the question whether or not design or “creative endeavour” should be counted as research, for reasons to do with institutional recognition or reward, the question remains how, if at all, is design research? This question is unlikely to have attracted much interest but for matters external to Architecture within the modern university. But Architecture as a discipline now needs to understand research much better than in the past when ‘research’ was whatever went on in building science, history or people/environment studies. In this paper, I begin with some common assumptions about design, considered in relation to research, and suggest how the former can constitute or be a mode of the latter. Central to this consideration is an understanding of research as the production of publicly available knowledge. The method is that of conceptual analysis which is much more fruitful than is usually appreciated. This work is part of a larger project in philosophy of design, in roughly the analytical tradition.

Modeling control objectives for business process compliance

Business process design is primarily driven by process improvement objectives. However, the role of control objectives stemming from regulations and standards is becoming increasingly important for businesses in light of recent events that led to some of the largest scandals in corporate history. As organizations strive to meet compliance agendas, there is an evident need to provide systematic approaches that assist in the understanding of the interplay between (often conflicting) business and control objectives during business process design. In this paper, our objective is twofold. We will firstly present a research agenda in the space of business process compliance, identifying major technical and organizational challenges. We then tackle a part of the overall problem space, which deals with the effective modeling of control objectives and subsequently their propagation onto business process models. Control objective modeling is proposed through a specialized modal logic based on normative systems theory, and the visualization of control objectives on business process models is achieved procedurally. The proposed approach is demonstrated in the context of a purchase-to-pay scenario.

Public policy in knowledge-based economies: Foundations and frameworks

Knowledge is a product of human social systems and, therefore, the foundations of the knowledge-based economy are social and cultural. Communication is central to knowledge creation and diffusion, and Public Policy in Knowledge-Based Economies highlights specific social and cultural conditions that can enhance the communication, use and creation of knowledge in a society.The purpose of this book is to illustrate how these social and cultural conditions are identified and analysed through new conceptual frameworks. Such frameworks are necessary to penetrate the surface features of knowledge-based economies - science and technology - and disclose what drives such economies.This book will provide policymakers, analysts and academics with the fundamental tools needed for the development of policy in this little understood and emerging area.

Knowledge and willingness to pay for the conservation of wildlife species: Experimental results evaluating Australian tropical species

The nature of an experiment involving 204 residents is outlined and the results are reported and analysed. Two consecutive surveys of the respondents provide data about their stated knowledge of 23 wildlife species present in tropical Australia, most of which exclusively occur there. In addition, these surveys provide data about the willingness of respondents to pay for the conservation of those species belonging to three taxa; reptiles, mammals, and birds. Thus it is possible to compare the respondents’ stated knowledge of the species with their willingness to pay for their conservation, and to draw relevant inferences from this. From the initial survey and these associations, interesting relationships can be observed between those variables (knowledge and willingness to pay). The second survey was completed after the respondents’ knowledge of the species was experimentally increased and became more balanced. This is shown to result in increased dispersion (greater discrimination) in willingness to contribute to conservation of the different species in the set of wildlife species considered. Both theoretical and policy conclusions are drawn from the results.

Knowledge of birds and willingness to pay for their conservation: An Australian case study

This case study concentrates on the extent of knowledge of members of the Australian public of Australia’s tropical bird species and their willingness to pay for their conservation. In order to place this issue in context, it first provides background information on the status of Australian bird species, focusing attention on species that occur in tropical Australia. Then, using survey results, this study considers the hypothesis that the public’s relative support for the conservation of different bird species depends on its comparative knowledge of their existence and status. Based on experimental results from a sample of residents of Brisbane, Queensland (Australia), it is found that their knowledge of bird species that occur exclusively in the Australian tropics (including tropical Queensland) is very poor compared to those that also occur in the Brisbane area and are relatively common. Experimental results indicate that when respondents in the sample had an option to allocate $1,000 between ten bird species listed in the survey, it resulted in a greater allocation of funds to the better known and more common species than when they were provided with balanced information about all the selected species. With balanced information the average allocation to bird species confined mostly to the Australian tropics, particularly those threatened or endangered, increased. The general consequences of this for policies for the conservation of birds are discussed.

Western Australian primary school teachers knowledge about childhood asthma and its management

A total of 164 primary school teachers from Perth, Western Australia anonymously completed a survey of their knowledge and attitudes about asthma. These teachers were active in assisting children with asthma management but most (91.5%) felt that they did not know enough about asthma. Attitudes toward children with asthma were positive; 97% agreed that such children should be encouraged to participate in sporting activities. Specific knowledge about asthma management and medications was, however, poor. This large sample of Western Australian teachers knew more than their European counterparts but asthma training is needed and should be targeted at improving knowledge of both regular and emergency treatments for asthma.

Young children, adolescents and alcohol - Part I: Exploring knowledge and awareness of alcohol and related issues

This article explores young children's and adolescents' views pertaining to: knowledge and awareness of alcohol and alcohol related issues; social situations in which. alcohol use is present; orientation to alcohol risk; perceived and actual alcohol use; social image and reputation; and short and long term health beliefs in relation to alcohol. Forty focus groups were conducted with 240 primary school students (118 males and 122 females) and 24 focus groups were conducted with 192 high school students (90 males and 102 females); the total being 64 focus groups comprising 432 school students. Participants ages ranged from five years three months to 16 years 10 months. The videotaped discussions revealed that approximately 75% of the primary school-aged children and almost all of the high school students reported that they had tasted alcohol. Parents were primarily responsible for providing the alcohol. Virtually all participants recognised and were able to correctly name a selection of alcoholic and non-alcoholic beverages, and levels of knowledge and awareness of the health and safety aspects of alcohol were relatively mixed. Presentation of bottles and cans was reported as being important in attracting young persons. These data suggest there is an urgent need for research addressed to the development of prevention/intervention education curriculum materials for use with primary school-aged children.