While many large, multivariate datasets carry geographic and temporal references, our ability to analyze these datasets lags behind our ability to collect them because of the challenges posed by complexity and scalability issues. This research aims to develop a visual analytics approach that integrates visual, computational and cartographic methods and couples them with human knowledge and judgment to support the exploratory analysis of spatio-temporal, high dimensional, large datasets. By combining both human and machine strengths, the proposed research has a better chance to discover novel, relevant and potentially useful information than can be achieved by applying any of the methods in isolation. The effectiveness of the proposed approach will be tested via case analysis on a variety of systematically selected sample datasets.

www.elsevier.com/locate/isprsjprs To support their analytical processes, today's organizations deploy data warehouses and client tools such as OLAP (On-Line Analytical Processing) to access, visualize, and analyze their integrated, aggregated and summarized data. Since a large part of these data have a spatial component, better client tools are required to take full advantage of the geometry of the spatial phenomena or objects being analyzed. With this regard, Spatial OLAP (SOLAP) technology offers promising possibilities. A SOLAP tool can be defined as “a type of software that allows rapid and easy navigation within spatial databases and that offers many levels of information granularity, many themes, many epochs and many display modes synchronized or not: maps, tables and diagrams” [Bédard, Y., Proulx, M.J., Rivest, S., 2005. Enrichissement du OLAP pour l'analyse géographique: exemples de réalisation et

New analysis and modeling approaches often call for reconsideration of methodological and philosophical stances if they are to be used appropriately. This is particularly true of Agent-Based Models (ABM), which can play a wide range of roles in scientific investigations, from characterizing emergent properties of data through prediction of future states, to positing explanations of possible causal mechanisms. In the biological simulation community, a similar recognition is summed up by Peck (2005) thus: "Philosophers and practitioners of science are recognizing that simulation models are a new kind of tool that defies the categories, uses and restrictions found in the historical use of mathematical models”. Such models do not sit easily with the traditional view of models and their roles in geographical analysis (e.g. Chorley, 1964). While we do not agree that simulation is a ‘new kind of science ’ we do believe that its application and interpretation, and legitimate roles and limitations, are not yet well understood. Equally, real-world experiments are not practical for many kinds of broad-scale, geographical inquiry, and simulation models allow exploration of alternative realities and responses to change. Thus, we must learn to use simulation technology in effective and defensible ways. How do we validate such complex models? There is a danger that simulation models can