The Visual Display of Quantitative Information

Knowledge management: theory and practice

Being newly immersed in the upstream part of the oil business, I just recently had my first work session with data in ARC–GIS®. The project involves subsurface geographical modeling. Obviously I had considerable interest in discovering if the methodology in this book would enhance my modeling capabilities. The book is for social scientists, but I had no difficulty imagining my own important oil exploration application within the framework of geographically weighted regression (GWR). The first chapter nicely explains what is unique in this book. A standard regression model using geographically oriented data (the example is housing prices across all of England) is a global representation of a spatial relationship, an average that does not account for any local differences. In y = f (x), imagine a whole family of f ’s that are indexed by spatial location. That is the focus of this book. It is about one form of local spatial modeling, which is GWR. A more general resource for this topic is the earlier book by Fotheringham and Wegener (2000), which escaped the notice of Technometrics. Imagine a display of model parameters in a geographical information system (GIS) and you will understand the focus for this book. The authors note, “only where there is no significant spatial variation in a measured relationship can global models be accepted” (p. 10). The second chapter develops the basis of GWR. It analyzes the housing sales prices versus the 33 boroughs in London and begins by fitting a conventional multiple regression model versus housing characteristics. The GWR is motivated by differences in the regression models fitted separately by borough. The GWR is a spatial moving-window approach with all data distances weighted versus a specific data point using a weighting function and a bandwidth. A GIS can then be used to evaluate the spatial dependency of the parameters. As in kriging, local standard errors also are calculated. The chapter also provides all the math. Chapter 3 comprises several further considerations: parameters that are globally constant, outliers, and spatial heteroscedasticity. The first issue leads to hypothesis tests for model comparison using an Akaike information criterion (AIC). Local outliers are hard to detect. Studentized (deletion) residuals are recommended. The outliers can be plotted geographically. Robust regression is suggested as a less computationally intensive alternative. Hetereoscedasticity is harder to handle. Chapter 4 adds statistical inference to the capabilities of GWR: both a confidence interval approach using local likelihood and an AIC method. Four additional methodology chapters present various extensions of GWR. Chapter 5 considers the relationship between GWR and spatial autocorrelation, and includes a combined version of GWR and spatial regression using some complex hybrid models. Chapter 6 examines the relationship of scale and zoning problems in spatial analysis to GWR. Chapter 7 introduces the use of initial exploratory data analysis using geographically weighted statistics, which are based on the idea of using a kernel around each data point to create weights. Univariate statistics and correlation coefficients are defined for exploring local patterns in data. A final set of extensions in Chapter 8 discusses regression models with non-Gaussian errors, logistic regression, local principal components analysis, and local probability density estimation. The methods all use some kind of distributional model. The million-dollar question for me is always, “What about software?” The authors have a stand-alone program, GWR 3, available in CD–ROM by contacting the authors. Basically the drill with GWR 3 is to gather your data, use Excel to transform and reformat the data for GWR 3, use GWR 3 to produce a set of coefficients, and feed those coefficients to your favorite GIS to produce your maps. Forty pages of discussion about using the software are provided. A final epilogue chapter also discusses embedding GWR in R or Matlab and includes some references to people who have done that type of work. I probably would not have read this book if I had not happened to have had it in my briefcase on a visit with the exploration technologists. Though inclusive of appropriate mathematical development, this material is readily approachable because of the many illustrations and the pages and pages of GIS displays. The authors unabashedly present much of the material as their developmental work, so GWR offers a lot of opportunity for research and further development through novel applications and extensions.

Geographically Weighted Regression

Examples are discussed to show the differences among discriminant analysis, logistic regression, and multiple regression. Chapter 6, “Multivariate Analysis of Variance,” presents advantages of multivariate analysis of variance (MANOVA) over univariate analysis of variance (ANOVA), discusses assumptions of MANOVA, and assesses validations of MANOVA assumptions and model estimation. The authors also discuss post hoc tests of MANOVA and multivariate analysis of covariance. Chapter 7, “Conjoint Analysis,” explains what conjoint analysis does and how it is different from other multivariate techniques. Guidelines of selecting attributes, models, and methods of data collection are presented. Chapter 8, “Cluster Analysis,” studies objectives, roles, and limitations of cluster analysis. Two basic concepts: similarity and distance are discussed. The authors also discuss details of five most popular hierarchical algorithms (singlelinkage, complete-linkage, average-linkage, centroid method, Ward’s method) and three nonhierarchical algorithms (the sequential threshold method, the parallel threshold method, and the optimizing procedure). Profiles of clusters and guidelines for cluster validation are studied as well. Chapter 9, “Multidimensional Scaling and Correspondence Analysis,” introduces two interdependence techniques to display the relationships in the data. The book describes clearly and intuitively the differences between the two techniques and how these two techniques are performed. Chapters 10–12 cover topics in SEM. Chapter 10, “Structural Equation Modeling: An Introduction,” introduces SEM and related concepts such as exogenous, endogenous constructs, and so on, points out the differences between SEM and other multivariate techniques, overviews the decision process of SEM. Chapter 11, “Confirmatory Factor Analysis,” explains the differences between exploratory and confirmatory factor analysis, discusses how to construct, validate, and assess the goodness of fit of a measurement model in SEM by confirmatory factor analysis. Chapter 12, “Testing a Structural Model,” presents some methods of SEM in examining the relationships between latent constructs. The book is an excellent book for people in management and marketing. For the Technometrics audience, this book does not have much flavor of physical, chemical, and engineering sciences. For example, partial least squares, a very popular method in Chemometrics, is discussed but not as detailed as other techniques in the book. Furthermore, due to the amount of materials covered in the book, it might be inappropriate for someone who is new to multivariate analysis.

The Grammar of Graphics

The objective of this paper is to develop the relationships among the identified knowledge management (KM) barriers. Further, this paper is also helpful to understand mutual influences of barriers and to identify those barriers which support other barriers (driving barrier) and also those barriers which are most influenced by other barriers (dependent barriers). The interpretive structural modeling (ISM) methodology is used to evolve mutual relationships among these barriers. KM barriers have been classified, based on their driving power and dependence power. The objective behind this classification is to analyze the driving power and dependence power of these barriers.

Knowledge management barriers: An interpretive structural modeling approach

This paper describes the design of groupware for virtual organizations that share certain characteristics. First the concept of virtual organizations is introduced. Then our application partner, the "virtual" service company Sigma, and our empirical work at Sigma are described. The identified problems are categorized and discussed. Finally, we present the derived aspects for the design of groupware in the context of virtual organizations.

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Supporting cooperation in a virtual organization

Preserving and fostering knowledge is the vital interest of a network-like virtual organization. The decentralized and geographically distributed organizational structure inhibits knowledge flow. The particular conditions of knowledge management in virtual organizations are discussed by the example of an empirical study we carried out in a service company. We analyze how technical and organizational aspects influence knowledge sharing and transfer. Particular emphasis is placed on supporting knowledge transfer. It is shown how an existing knowledge sharing tradition had to be extended and which drawback it had concerning technology use. To improve knowledge flow within the organization a web-based knowledge base was introduced. This initiated a process of externalizing tacit knowledge and is meant to introduce new ways of knowledge transfer to the organizational culture. In fluid virtual organizations sustained knowledge management means to establish an organizational memory that is flexible and adaptive to changing requirements. This is best achieved by a strong organizational culture that emphasizes knowledge sharing by the use of various communication channels.

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Sustained knowledge management by organizational culture

This article enhances existing approaches to present-day asynchronous awareness concepts by providing the means to explicitly represent and mediate contextual information. The resulting concept of contextual awareness takes different notions of the term context into account. Following a human-centered approach, the proposed methods serve as mediators for context between persons rather than automatically detecting context. Based on this variant of awareness, the atmosphere framework is introduced to provide mechanisms to deal with the problem of workload in tandem with contextual information. Atmosphere provides a highly tailorable structure and interface to deal with a wide variance of user and organizational requirements. The article closes with the description of a partial implementation of the framework and its evaluation.

Atmosphere: A Framework for Contextual Awareness

Mutual awareness has been a focus point of research in Human–Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW) since the early 1990s. At its essence, mutual awareness refers to a fundamental quality of collaborative work, the ability of co-workers to perceive each others’ activities and expressions and relate them to a joint context. In this chapter, we explore the history of awareness concepts by analysing existing literature in order to identify trends, research questions, research approaches and classification schemes throughout different stages of research into awareness. We have adopted a historical angle in the hope that it will allow us to show how awareness research has progressed over time. We document this development using three different phases: (1) Early exploration of awareness (approximately 1990–1994), (2) Diversification and research prototypes (approximately 1995–1999) and (3) Extended models and specialisation (approximately 2000–now). While these phases are to some extent arbitrary and overlapping, they allow us to highlight differences in research focus at the time and understand research in context.

An Historical Reflection of Awareness in Collaboration

"The Cube" is a unique facility that combines 48 large multi-touch screens and very large-scale projection surfaces to form one of the world's largest interactive learning and engagement spaces. The Cube facility is part of the Queensland University of Technology's (QUT) newly established Science and Engineering Centre, designed to showcase QUT's teaching and research capabilities in the STEM (Science, Technology, Engineering, and Mathematics) disciplines. In this application paper we describe, the Cube, its technical capabilities, design rationale and practical day-to-day operations, supporting up to 70,000 visitors per week. Essential to the Cube's operation are five interactive applications designed and developed in tandem with the Cube's technical infrastructure. Each of the Cube's launch applications was designed and delivered by an independent team, while the overall vision of the Cube was shepherded by a small executive team. The diversity of design, implementation and integration approaches pursued by these five teams provides some insight into the challenges, and opportunities, presented when working with large distributed interaction technologies. We describe each of these applications in order to discuss the different challenges and user needs they address, which types of interactions they support and how they utilise the capabilities of the Cube facility.

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Markus Rittenbruch

Papers

Supporting cooperation in a virtual organization

Sustained knowledge management by organizational culture

Atmosphere: A Framework for Contextual Awareness

An Historical Reflection of Awareness in Collaboration

The cube: a very large-scale interactive engagement space