Interactive Visual Exploration of a Large Spatio-temporal Dataset: Reflections on a Geovisualization Mashup.

TLDR

Exploratory visual analysis is essential for preliminary investigation of large, structured, multifaceted spatio‑temporal datasets, requiring selection, aggregation, transformation, and flexible visual encodings and interactions. The authors propose a mashup‑inspired approach that loosely combines freely available functionality and data using de facto exchange standards. In a case study, they integrate MySQL, PHP, and LandSerf GIS to enable Google Earth visual synthesis and interaction via KML, introducing novel encodings such as spatial tag clouds, tag maps, data dials, and multi‑scale density surfaces. Preliminary findings indicate that the mashup approach and developed techniques are beneficial for others and can be broadly applied to gain insights into complex spatio‑temporal data.

Abstract

Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the dataset, the specific tools used and the 'mashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here.