Attribute Signatures: Dynamic Visual Summaries for Analyzing Multivariate Geographical Data

Cagatay Turkay, Aidan Slingsby, Helwig Hauser, Jo Wood, Jason Dykes
2014 IEEE Transactions on Visualization and Computer Graphics  
Fig. 1. Attribute signatures (right) are dynamically created in response to an interactive geographic selection sequence (left) that follows the coastline from South Gloucestershire to St Ives on the north Cornwall coast where each output area is represented with an orange dot. The signatures show how the average values for 41 attributes vary as the selection moves. The trace of the brush sequence is linked to the signatures -the faded points and the vertical dashed lines on the signatures are
more » ... inked to the location highlighted on the map. A small holiday resort, Lynton (green rectangle), is characterized by the high proportion of population in the hotel and catering industry. Fishing & agriculture towns, such as Hartland (red markers), are characterized with low population densities where population is in mostly detached houses. Abstract-The visual analysis of geographically referenced datasets with a large number of attributes is challenging due to the fact that the characteristics of the attributes are highly dependent upon the locations at which they are focussed, and the scale and time at which they are measured. Specialized interactive visual methods are required to help analysts in understanding the characteristics of the attributes when these multiple aspects are considered concurrently. Here, we develop attribute signatures -interactively crafted graphics that show the geographic variability of statistics of attributes through which the extent of dependency between the attributes and geography can be visually explored. We compute a number of statistical measures, which can also account for variations in time and scale, and use them as a basis for our visualizations. We then employ different graphical configurations to show and compare both continuous and discrete variation of location and scale. Our methods allow variation in multiple statistical summaries of multiple attributes to be considered concurrently and geographically, as evidenced by examples in which the census geography of London and the wider UK are explored.
doi:10.1109/tvcg.2014.2346265 pmid:26356917 fatcat:rizam2fyq5akxemrtqmvxjnqs4