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The overall growing amount of data is a driving force behind new fields of research in mass data visualization. One of many examples for recent research conducted in this area is the field of network visualizations, like e.g. the massive data plots by Hochman and Manovich (Hochman & Manovich, 2013). One might argue that mass data visualizations are not new. What might be new are the web-based or mobile technologies and communication channels that are available for developing visualizations on the production side as well as for the audiences on the reception side. The field of visualization is moving away from being an expert-only field towards being a field that is relevant for a broad audience. Thus, data and its representations are being made accessible and usable for scholars from many different fields as well as for a broad public audience.
The same applies for data visualization in cartography. Experts in the field have been working with mass data and geovisualization techniques since the introduction of the first Geo Information Systems (GIS) in the late 1960s (Coppock & Rhind, 2001). But especially with the advances in web cartography and the growing popularity of modern web mapping applications like Google maps (Google Inc., 2014) or Bing (Microsoft Corporation, 2014), we observe that the public and academic fields are increasingly involved in the usage of modern web-based cartographic applications for private, professional and academic purposes. At the same time, we experience a growing number of services that collect or create large amounts of spatial data, for example services like Yelp (Yelp, 2014) or Foursquare (Foursquare, 2014). This combination of trends nourishes the need for new spatial mass data visualization methods for web-based cartographic applications.
In this paper, we analyze the marker cluster; a visualization method that is already being used by many web-based applications that integrate visualizations of large spatial datasets. From a scientific point of view, most of the research on marker clusters focuses on the more technical elements, like e.g. the algorithms generating the clusters (Bär & Hurni, 2011; Delort, 2010a; Kefaloukos, Vaz Salles, & Zachariasen, 2012; Stefanakis, 2005). In contrast to this related research, we investigate the functionality of the marker cluster method and take the human perception into account. Abstract representations of spatial data each require interpretation of the respective cartographic communication model. That is why also the marker cluster, just like every other layer of abstraction, will conceivably introduce errors in the user’s interpretation.
In order to better understand the user’s perception of the marker cluster, we have conducted a series of experiments. They were aimed at testing the marker cluster’s performance and precision, but also the accuracy in the users’ interpretation, in other words their level of understanding the marker cluster as an abstract representation.
With our approach, we follow a trend towards more functional cartographic representations, away from purely design and artistic map creation (MacEachren, 2004), that has emerged over the last few decades. One of the earliest and most cited publications that have informed this trend towards more objective guidelines for cartographic representations is Arthur H. Robinson’s 1952 book the Look of maps, based on his dissertation, and his book Elements of Cartography (Robinson, 1952; Robinson et al, 1984). Since then, we have seen a whole body of research evolve that has been directed towards the analysis of perception of cartographic representations. Howard’s (Howard, 1980) model for analyzing cartographic symbolization separates three distinct levels of cartographic representation: lexical, functional and cognitive. With our series of experiments, we mostly focussed on the latter: the individual perception of cartographic representation. Nonetheless, we also gained insights into the two more systemic aspects of Howard’s model through the qualitative part of our analysis.