@article{Meier, author = {Meier, Sebastian}, title = {The Marker Cluster}, series = {International Journal of Agricultural and Environmental Information Systems}, volume = {7}, journal = {International Journal of Agricultural and Environmental Information Systems}, number = {1}, publisher = {IGI Global}, address = {Hershey}, issn = {1947-3206}, doi = {10.4018/IJAEIS.2016010102}, pages = {28 -- 43}, abstract = {The growing amount of gathered, stored and available data is creating a need for useful mass-data visualizations in many domains. The mapping of large spatial data sets is not only of interest for experts anymore, but, with regard to the latest advances in web cartography, also moves into the domain of public cartographic applications. One interactive web-based cartographic interface design pattern that helps with visualizing and interacting with large, high density data sets is the marker cluster; a functionality already in use in many web-based products and solutions. In this article, the author will present their ongoing research on the problem of "too many markers." They will present an empirical evaluation and comparison of marker cluster techniques and similar approaches, including heatmaps and tiled heatmaps. They conclude with a first concept for overcoming some of the obstacles that they were able to identify in their study and thereby introduce a new direction for further research.}, subject = {Big Data}, language = {en} } @article{BeechamRooneyMeieretal., author = {Beecham, R. and Rooney, C. and Meier, S. and Dykes, J. and Slingsby, A and Turkay, C. and Wood, J. and Wood, B. L. and Wong, W.}, title = {Faceted Views of Varying Emphasis (FaVVEs)}, series = {Computer Graphics Forum}, volume = {35}, journal = {Computer Graphics Forum}, number = {3}, publisher = {Wiley-Blackwell}, address = {Oxford}, issn = {1467-8659}, doi = {10.1111/cgf.12900}, pages = {241 -- 249}, abstract = {Many datasets have multiple perspectives - for example space, time and description - and often analysts are required to study these multiple perspectives concurrently. This concurrent analysis becomes difficult when data are grouped and split into small multiples for comparison. A design challenge is thus to provide representations that enable multiple perspectives, split into small multiples, to be viewed simultaneously in ways that neither clutter nor overload. We present a design framework that allows us to do this. We claim that multi-perspective comparison across small multiples may be possible by superimposing perspectives on one another rather than juxtaposing those perspectives side-by-side. This approach defies conventional wisdom and likely results in visual and informational clutter. For this reason we propose designs at three levels of abstraction for each perspective. By flexibly varying the abstraction level, certain perspectives can be brought into, or out of, focus. We evaluate our framework through laboratory-style user tests. We find that superimposing, rather than juxtaposing, perspective views has little effect on performance of a low-level comparison task. We reflect on the user study and its design to further identify analysis situations for which our framework may be desirable. Although the user study findings were insufficiently discriminating, we believe our framework opens up a new design space for multi-perspective visual analysis.}, subject = {Visualisierung}, language = {en} }