Automated Recommendation of Aggregate Visualizations for Crowdfunding Data

Abstract

Analyzing crowdfunding data has been the focus of many research efforts, where analysts typically explore this data to identify the main factors and characteristics of the lending process as well as to discover unique patterns and anomalies in loan distributions. However, the manual exploration and visualization of such data is clearly an ad hoc, time-consuming, and labor-intensive process. Hence, in this work, we propose LoanVis, which is an automated solution for discovering and recommending those valuable and insightful visualizations. LoanVis is a data-driven system that utilizes objective metrics to quantify the “interestingness” of a visualization and employs such metrics in the recommendation process. We demonstrate the effectiveness of LoanVis in analyzing and exploring different aspects of the Kiva crowdfunding dataset.

Keywords

• data exploration
• visual analytics
• aggregate views