Beyond the Mirror: Personal Analytics through Visual Juxtaposition with Other People's Data

TL;DR

This paper introduces CALTREND, a visual analytics system that compares personal online schedule data with others' to enhance interpretation and insight, demonstrated through expert studies.

Contribution

The paper presents a novel system for juxtaposing individual and anonymized peer data to improve contextual understanding of personal analytics.

Findings

Juxtaposition with others' data enriches interpretation.

Domain-specific mental models influence data understanding.

Comparative views facilitate diverse insights.

Abstract

An individual's data can reveal facets of behavior and identity, but its interpretation is context dependent. We can easily identify various self-tracking applications that help people reflect on their lives. However, self-tracking confined to one person's data source may fall short in terms of objectiveness, and insights coming from various perspectives. To address this, we examine how those interpretations about a person's data can be augmented when the data are juxtaposed with that of others using anonymized online calendar logs from a schedule management app. We develop CALTREND, a visual analytics system that compares an individuals anonymized online schedule logs with using those from other people. Using CALTREND as a probe, we conduct a study with two domain experts, one in information technology and one in Korean herbal medicine. We report our observations on how comparative views help enrich the characterization of an individual based on the experts' comments. We find that juxtaposing personal data with others' can potentially lead to diverse interpretations of one dataset shaped by domain-specific mental models.