# Automated Recommendation of Aggregate Visualizations for Crowdfunding Data
## 3 System and Results  
We have conducted an extensive exploratory analysis of the Kiva dataset using our proposed LoanVis system. In this section, we present some of the visualizations recommended by our system LoanVis, together with some of the insights driven from those visualizations. The presented visualizations are the ones that received the highest utility score, according to the employed data-driven, deviation-based metric (please see Section 2.2). Figure 4 shows a screenshot of LoanVis, which enables analysts to explore the Kiva dataset and recommends to the analysts visualizations that suit their exploratory analysis and are based on the deviation-based metric. LoanVis is developed using _Python 3.7_ under the _PyCharm_ IDE. Our user interface is developed using the _Dash_ package, which allows for the creation of an interactive web-based data application. Finally, the _Plotly_ library is utilized for generating dynamic data visualizations. As Figure 4 shows, LoanVis enables two forms of visual data exploration: (1) manual exploratory search and (2) automated recommendation-based exploration. Particularly, as shown in the top part of the interface, LoanVis allows analysts to specify parameters to manually construct different visualizations of the Kiva dataset across its different dimensions and measures. Alternatively, and as shown in the lower part of the interface, analysts can rely on LoanVis to automatically recommend insightful visualizations based on their selection of the explored aspects (e.g., sector, gender, etc.). In the rest of this section, we focus on the automated recommendations generated by LoanVis. Notice that in this work we focus on the effectiveness of LoansVis. That is, the interesting insights discovered by LoanVis, whereas efficiency issues (i.e., query execution time) are beyond the scope of this work. Meanwhile, techniques for optimizing the query processing time of visualization recommendation systems have been proposed in some of our related work (e.g., [23; 24; 25]) and are directly applicable to our LoanVis system. Table 2 presents a summary of all the results presented in this section (i.e., the recommended visualizations). For each result, the table shows the aspect of the Kiva dataset explored by the analyst, together with the different elements that constitute the corresponding visualizations recommended by LoanVis. Particularly, for each recommended visualization, the table lists the following: (1) the aspect explored by the analyst (e.g., sector, country, etc. ), (2) the particular value along that aspect recommended by LoanVis (e.g., _sector = entertainment_), (3) the dimension, measure, and aggregate function employed in the visualization recommended by LoanVis, and (4) the utility value of that recommended visualization. Notice that the maximum possible utility value for any visualization under the Euclidean distance measure is \(\sqrt{2}\)[24]. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Explored** & **Recommended** & **Recommended** & **Recommended** & **Recommended** & **Recommended** & **Figures** & **Deviation** \\
**Aspect** & **Selection** & **Dimension** & **Measure** & **Aggregation** & & **Figures** & **Deviation** \\ \hline Sector & Entertainment & Country & Loan Amount & SUMO & Figures 3 and 5 & 0.5761 \\ \hline Sector & Wholesale & Gender & Funded Amount & SUMO & Figure 6a & 0.428 \\ Sector & Construction & Gender & Lender Count & SUMO & Figure 6b & 0.4204 \\ \hline Country & Namibia & Repayment & & & & \\  & & Interval & Funded Amount & SUMO & Figure 7a & 1.12 \\ \hline Country & Congo & Gender & Loans & Count() & Figure 7b & 1.083 \\ \hline Year & 2016 & Country & Funded Amount & AVG0 & Figure 8 & 0.4705 \\ \hline Year & 2016 & Country & Lender Count & AVG0 & Figure 9 & 0.4903 \\ \hline Gender & Male &
\begin{tabular}{l} Repayment \\ Interval \\ \end{tabular} & Loans & Count() & Figure 10 & 0.3046 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the LoanVis Recommendations.