# Automated Recommendation of Aggregate Visualizations for Crowdfunding Data
## 3 System and Results
### Automated Recommendations for the Year Aspect  
In this experiment, we focus on automatically generating recommendations based on the _Year_ aspect. Figure 8 shows the top-1 visualization recommended by LoanVis for that aspect. Particularly, as the figure shows, LoanVis recommended the selection of Year = 2016 and also recommended a visualization in which the Y-axis is the _Average Loan Amount_ and the X-axis is the _Country_. Examining Figure 8, it shows the overall distribution of the average loan amount received by each country over all the recorded years (i.e., the comparison view, which is shown in black). However, the general overall distribution is significantly different in 2016, which has been recommended by LoanVis.  
Specifically, looking at the distribution of loans in 2016 (i.e., target view, which is shown in red color), we notice that while the distribution of the average loan amount of most countries followed the same general pattern as in the comparison view, some discrepancies stand out, namely: (1) few countries did not receive any loans in 2016 (e.g., Bhutan, Chile, Congo, and Iraq), and (2) the country of South Sudan received loans in an average amount much higher than the loans it received in the other years. Digging deeper into that discrepancy, we realized that in 2015/2016, Kiva agreed to allow a one-year grace period for loans disbursed in South Sudan and also agreed to restructure repayment plans [38].  
Interestingly, our observation was further emphasized by the top-2 visualization recommended by LoanVis, which is shown in Figure 9. In that recommended visualization, the comparison view shows the average number of lenders per project over the different years for all countries, whereas the target view shows the average number of lenders per project for all countries in 2016 (in red). Looking at the general distribution captured by the comparison view, we notice a skewed distribution, in which projects in Bhutan got the highest number of lenders per project (about 200 lenders per funded project), followed by Chile, Namibia, and Nigeria comes last in terms of average number of lenders per project. However, the distribution captured by the target view for 2016, while it still shows a skewed pattern, the details of that pattern are significantly different from the general one. Particularly, as Figure 9 shows, in 2016, the country with the maximum number of lenders per project was South Sudan, followed by Somalia, then Namibia. Noticing that projects in South Sudan received the highest number of lenders in 2016 (as shown in Figure 9) might provide a potential explanation for those projects receiving loans in high amount (as shown in Figure 8).  
Figure 7: LoanVis top recommendations for the _Country_ aspect. (**a**) In Namibia (target view in red), most loans are payed as bullet repayments at once in contrast to the other worldwide prevailing repayment methods (comparison view in black). (**b**) In Congo (target view in red), most projects are led by mixed-gender teams vs. worldwide (comparison view in black); most projects are female-led.