Visualizing Multiple Variables Across Scale and Geography

Abstract

Comparing multiple variables to select those that effectively characterize complex entities is important in a wide variety of domains - geodemographics for example. Identifying variables that correlate is a common practice to remove redundancy, but correlation varies across space, with scale and over time, and the frequently used global statistics hide potentially important differentiating local variation. For more comprehensive and robust insights into multivariate relations, these local correlations need to be assessed through various means of defining locality. We explore the geography of this issue, and use novel interactive visualization to identify interdependencies in multivariate data sets to support geographically informed multivariate analysis. We offer terminology for considering scale and locality, visual techniques for establishing the effects of scale on correlation and a theoretical framework through which variation in geographic correlation with scale and locality are addressed explicitly. Prototype software demonstrates how these contributions act together. These techniques enable multiple variables and their geographic characteristics to be considered concurrently as we extend visual parameter space analysis (vPSA) to the spatial domain. We find variable correlations to be sensitive to scale and geography to varying degrees in the context of energy-based geodemographics. This sensitivity depends upon the calculation of locality as well as the geographical and statistical structure of the variable.

Mapping Parallels between Outdoor Urban Environments and Indoor Manufacturing Environments

“…Therefore, most manufacturing companies collect data from manufacturing processes with explicit and implicit spatial-temporal references to enable data analysis and visualization. By applying methods of geo-visual analytics, humans can be supported in identifying patterns within the given data [29,30]. In addition, near real-time visualization of manufacturing data is of interest due to the emergence of wearable devices for employees and managers [31].…”

Visualizing the Impact of Geographical Variations on Multivariate Clustering

“…Dykes and Brunsdon [DB07] introduced geographically weighted interactive graphics for exploring and hypothesizing the spatial relationships under different scale‐based variations. Goodwin et al [GDST16] developed a suite of novel interactive visualization methods to identify interdependencies in multivariate data coupled with a series of correlation matrix views. While Goodwin et al focus primarily on spatial extents of pairwise correlations, our work explores spatial extents in the multivariate clustering space and enables exploratory analysis between clustering differences.…”

“…These systems have been deployed for a variety of different application domain areas and utilize a number of different analytical algorithms. For example, work by von Landesberger et al [vLBA*12] presented an approach for classifying spatiotemporal categorical data supported by algorithms for the selection of globally and focally representative time steps based on categorical changes, and Goodwin et al [GDST16] developed a suite of novel interactive visualization methods to identify interdependencies in multivariate data coupled with a series of correlation matrix views.…”

Visual Encoding of Dissimilarity Data via Topology-Preserving Map Deformation

“…The effects associated with the inclusion of individual variables may be either global or geographically focussed. We are working to provide visualization support to assess such effects of geography and scale for geodemographic variable selection [18]. We focus on the energy domain, where consumer profiles may help to understand energy use and perhaps influence behavior.…”