A dendrogram that visualizes a clustering hierarchy is often integrated with a re-orderable matrix for pattern identification. The method is widely used in many research fields including biology, geography, statistics, and data mining. However, most dendrograms do not scale up well, particularly with respect to problems of graphical and cognitive information overload. This research proposes a strategy that links an overview dendrogram and a detail-view dendrogram, each integrated with a re-orderable matrix. The overview displays only a user-controlled, limited number of nodes that represent the ldquoskeletonrdquo of a hierarchy. The detail view displays the sub-tree represented by a selected meta-node in the overview. The research presented here focuses on constructing a concise overview dendrogram and its coordination with a detail view. The proposed method has the following benefits: dramatic alleviation of information overload, enhanced scalability and data abstraction quality on the dendrogram, and the support of data exploration at arbitrary levels of detail. The contribution of the paper includes a new metric to measure the ldquoimportancerdquo of nodes in a dendrogram; the method to construct the concise overview dendrogram from the dynamically-identified, important nodes; and measure for evaluating the data abstraction quality for dendrograms. We evaluate and compare the proposed method to some related existing methods, and demonstrating how the proposed method can help users find interesting patterns through a case study on county-level U.S. cervical cancer mortality and demographic data.
Spatial analysis and social network analysis typically consider social processes in their own specific contexts, either geographical or network space. Both approaches demonstrate strong conceptual overlaps. For example, actors close to each other tend to have greater similarity than those far apart; this phenomenon has different labels in geography (spatial autocorrelation) and in network science (homophily). In spite of those conceptual and observed overlaps, the integration of geography and social network context has not received the attention needed in order to develop a comprehensive understanding of their interaction or their impact on outcomes of interest, such as population health behaviors, information dissemination, or human behavior in a crisis. In order to address this gap, this paper discusses the integration of geographic with social network perspectives applied to understanding social processes in place from two levels: the theoretical level and the methodological level. At the theoretical level, this paper argues that the concepts of nearness and relationship in terms of a possible extension of the First Law of Geography are a matter of both geographical and social network distance, relationship, and interaction. At the methodological level, the integration of geography and social network contexts are framed within a new interdisciplinary field:~visual analytics, in which three major application-oriented subfields (data exploration, decision-making, and predictive analysis) are used to organize discussion. In each subfield, this paper presents a theoretical framework first, and then reviews what has been achieved regarding geo-social visual analytics in order to identify potential future research.
Geographic visualization, sometimes called cartographic visualization, is a form of information visualization in which principles from cartography, geographic information systems (GIS), exploratory data analysis (EDA), and information visualization more generally are integrated in the development and assessment of visual methods that facilitate the exploration, analysis, synthesis, and presentation of georeferenced information. The authors report on development and use of one component of a prototype GVis environment designed to facilitate exploration, by domain experts, of time series multivariate georeferenced health statistics. Emphasis is on how manipulable dynamic GVis tools may facilitate visual thinking, pattern noticing, and hypothesis generation. The prototype facilitates the highlighting of data extremes, examination of change in geographic patterns over time, and exploration of similarity among georeferenced variables. A qualitative exploratory analysis of verbal protocols and transaction logs is used to characterize system use. Evidence produced through the characterization highlights differences among experts in data analysis strategies (particularly in relation to the use of attribute "focusing" combined with time series animation) and corresponding differences in success at noticing spatiotemporal patterns.
A fundamental challenge that must be met to achieve a usable conversational interface to geographic information system (GIS) is how to enable a more natural interaction between the user and the system. This paper presents a design of an agent-based computational model, PlanGraph, and implementation of this model in a software agent, GeoDialogue, as a dialogue manager for a conversational GIS. This dialogue agent enables intelligent collaborative human-GIS dialogues. The capabilities of this agent are demonstrated through its performance in a conversational GIS, Dave_G.
Cartography, as an academic field (and as a profession) should be at the centre of the dramatic increase of place in every facet of our lives – but it is not. What happened? One answer is that the ...
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