# Automated Recommendation of Aggregate Visualizations for Crowdfunding Data
## 2 Materials and Methods
### The Kiva Dataset  
The Kiva dataset has attracted the attention of multiple research studies in the area of data science and analytics (e.g., [2; 6; 9; 35]). The overarching goal of such research is to utilize the Kiva data set for extracting knowledge, gaining valuable insights into the crowdfunding model, and understanding its loan funding characteristics. In this paper, we expand on such research efforts and present our _LoanVis_ system, which automates the process of exploring the Kiva crowdfunding dataset and provides analysts with fast data-driven recommendations of insightful visualizations.  
There are mainly four types of participants in Kiva, namely: loans, borrowers, lenders, and field partners. The loans are in the form of fund-raising campaigns posted by field partners on behalf of the borrowers. Particularly, field partners are typically local non-profit organizations that act as the link between borrowers and Kiva. Lenders participate in a donation-based crowdfunding model (i.e., donors receive back their principal investment without profits).  
Kiva provides open public access to its data through daily snapshots and an Application Programming Interface (API). The Kiva data contains a set of heterogeneous information (i.e., data attributes) about the loans, lenders, borrowers, and field partners [34]. In particular, the main \"objects\" in the Kiva dataset are the _borrover_, the _lender_, and the _partner_, which are all connected to the _loan_ object [1]. That is, the dataset is centered around the loan data object, namely the \"_Loans_\" table. Each loan listing would include information regarding the key details about that loan, such as the industry for which the loan is intended, together with information about the borrowers, as well as the loan financial information (e.g., amount, term, repayment interval, etc.). Table 1 provides a summary of the main attributes of the _Loans_ dataset.  
The utilized Kiva dataset contains information about more than 671,000 loans displayed in 87 countries. A quick and simple exploration of the Kiva dataset can reveal some basic and interesting insights. For instance, out of the 15 funded sectors, projects related to the _Food_ and _Agriculture_ sectors receive the most loans. In terms of countries, borrowers from the Philippines and Kenya top the list of 87 countries for the total number of funded loans. Gender-wise, the data indicates that women make up the majority of borrowers, with 64% of borrowers being female.  
Manually exploring the Kiva dataset and discovering some basic insights, similar to the ones mentioned above, has been the focus of multiple works (e.g., [2; 29; 30; 33]). For instance, the work in [29] explores the Kiva dataset to understand the impact of the project sector on the distribution of loan amount (i.e., the relationship between attributes _Funded Amount_ and _Sector_ in Table 1). Similarly, the work in [30] attempts to understand the relationship between lending activity and features that characterize the loan, including the country of the loan, the loan sector, and the gender of the borrowers (i.e., attributes listed in Table 1 as _Country_, _Borrower Genders_, and _Sector_). In fact, Kiva provides its own data visualization and analytics dashboard [36], which allows users to explore the underlying crowdfunding data and facilitates conducting studies similar to the ones listed above. However, in this work, our goal goes beyond providing such basic statistics, and our focus is on leveraging visual analytics to automatically recommend data-driven, insightful visualization of the Kiva dataset, as described in the next sections.  
Figure 1: Ranking and Recommending Visualizations in LoanVis.