# Automated Recommendation of Aggregate Visualizations for Crowdfunding Data
## 3 System and Results
### Automated Recommendations for the Sector Aspect  
When exploring the _Sector_ aspect of the Kiva dataset, the top-1 visualization recommended by LoanVis according to the employing deviation metric is the one based on the _Entertainment_ sector, which has been discussed in the previous section and presented in Figure 3. Recall that the recommended visualization shows the significant discrepancy associated with the entertainment sector, where for most countries, loans provided for that sector are minimal, except for the USA.  
In addition to Figure 3, that insight could be further understood by examining Figure 5. Particularly, Figure 5 shows the normalized distributions of the amount of loans for all sectors (i.e., comparison view) vs. the normalized distribution of loans directed to the entertainment sector (i.e., target view).  
As expected, and as shown in the figure, the sum of all the normalized values in each of the target and comparison views adds up to 1.0 (please see Equation (1)). Further, the figure clearly emphasizes and clarifies the discrepancy highlighted earlier in Figure 3. For instance, while a country such as the Philippines receives almost 10% of the total kiva loans distributed worldwide, its share of the entertainment loans does not exceed 1%. In comparison, projects in the USA receive less than 6% of the total distributed loans, whereas its share of the loans directed to the entertainment sector is the highest among all countries at almost 10%. Similarly, as Figure 5 shows, Israel receives only 0.12% of the total loans worldwide but receives 3.12% of the entertainment loans.  
While both Figures 3 and 5 deliver the same insights, Figure 3 utilizes two scales for plotting the absolute values on the Y-axis, whereas Figure 5 uses normalized values derived based on Equation (1). For the sake of simplicity and to enhance readability, all of the remaining visualizations are presented using absolute values, similar to Figure 3.  
Figure 6a shows the top-2 visualization recommended by LoanVis along the _Sector_ aspect. In contrast to the top-1 visualization, which is based on the _Country_ dimension (Figure 3), this top-2 recommendation is based on the _Gender_ dimension (Figure 6a). Particularly, Figure 6a shows the total amount of loans funded for projects in all sectors per gender (i.e., comparison view) vs. the loans funded in the specific Wholesale sector (i.e., target view). From Figure 6a, it is interesting to notice that, in general, female-led projects receive most of the funding (about $200 M vs. $60 M for male-led projects), as shown in the comparison view. However, for projects in the particular Wholesale sector, male-led projects seem to receive the higher share (about $300 k for female-led wholesale projects vs. $400 k for male-led ones).  
Figure 5: Normalized probability distribution in loans disbursed for all sectors (comparison view in blue) vs. loans for the entertainment sector (target view in green).  
Interestingly, a similar discrepancy applies to projects in the Construction sector, which was the top-3 visualization recommended by LoanVis, and is shown in Figure 6b. In fact, during our analysis, we initially thought that male-led projects would receive the highest amount of loans in the Construction sector. However, as automatically discovered by LoanVis, that discrepancy is more pronounced in the Wholesale sector (Figure 6a), which scored a deviation value of 0.428, whereas the Construction sector (Figure 6b) came next with a deviation value of 0.4204.