# Automated Recommendation of Aggregate Visualizations for Crowdfunding Data
## 1 Introduction
For instance, an exploratory query, which applies filters such as: WHERE country = 'Kenya' AND borrower-gender = 'Female'; is considered interesting if it maximizes some quantifiable metric (e.g., skewness, surprisingness, diversity, or deviation from an expected distribution [19; 26; 27]). In exploring crowdfunding datasets, analysts are typically interested in identifying unique patterns, anomalies, and discrepancies across the different aspects of such data (e.g., borrower-gender, country, project-activity, etc.) [29; 30; 31]. For instance, the data visualization tool Tableau features a visualization-based case study of the Kiva dataset called Kiva Loan Story [32]. Furthermore, in an attempt to gain insights from crowdfunding data, Kiva deploys its own data visualization and analytics dashboard [30]. However, such a dashboard mainly supports basic data filtering operations and rudimentary visualizations based on the user's search parameters. That is, it is expected that the user knows exactly in advance the insights they are looking for! However, such insights become clear only in \"hindsight\" after spending a long time exploring the data. Hence, in this work, we present _LoansVis_, our visual analytics platform, which employs well-studied utility metrics (e.g., [19; 21; 24; 26; 27]) to automatically recommend interesting data visualizations that reveal hidden insights (e.g., discrepancies and anomalies in the distribution of loans across different countries or by gender). Accordingly, we have designed our proposed LoanVis to automatically provide recommended visualizations in two forms: (1) _Value-based_ recommendation: in which LoanVis recommends high-utility visualizations based on some specific values provided by the analyst (e.g., recommends aggregate visualizations based on the analyst manual choice of: country = 'Kenya'), and (2) _Aspect-based_ recommendation: in which the analyst's manual interaction is further minimized and they only have to specify their aspect of analysis (e.g., country or borrower-gender), then LoanVis automatically recommends both: (i) a specific value for the user-specified aspect (e.g., country = 'Congo'), and (ii) high-utility insightful visualizations based on that recommended value (e.g., a visualization of loan distribution across gender in the country of Congo). Our extensive experimental evaluation demonstrates LoanVis' ability to automatically detect and recommend interesting visualizations based on the Kiva crowdfunding dataset. Particularly in this work, we present some of the recommended visualizations that show unique and often unexpected patterns in the distribution of loans across different aspects of analysis (e.g., country, gender, sector, etc.). Our findings complement and expand on existing related works that aim at studying the discrepancy and factors affecting crowdfunding (e.g., [2; 29; 30; 33]). For instance, the work in [33] investigates how the gender, country, and type of borrower's business affect the lenders' lending decisions. The research in [2] examines the impact of forming groups on receiving fast funds for loan requests. Both of the works in [6; 34] focus on the loan characteristics, either through examining the factors that motivate lenders to contribute to donations or that lead to the success or failure of crowdfunding. However, while those studies relied on time-consuming manual data exploration and visualization, our proposed LoanVis facilitates understanding the characteristics of crowdfunding by automatically recommending visualizations that reveal unique patterns and discrepancies. The rest of this paper is organized as follows. In Section 2, we present our methodology, which is based on a discussion of the Kiva dataset (Section 2.1), followed by the details of our proposed LoanVis system (Section 2.2). In Section 3, we present our results based on the visualizations recommended by our LoanVis system. We conclude in Section 4.