Introduction

The Asian Development Bank (ADB) is a regional development bank with a mission to “achieve a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty.” ADB was established in 1966 and includes 68 member countries in the Asia-Pacific region in addition to Europe and the Americas. In achieving its mission, ADB strives to assist its members and promote economic growth and prosperity by providing loans, technical assistance, grants, and equity investments.

I came across ADB’s Sovereign Projects dataset and aimed to gain a better understanding of various development projects in the Asia-Pacific region. Inspired by the World Bank’s Data Blog, I was particularly keen on exploring ways to visualize large volumes of financial data into easy, digestible formats that do not require background knowledge on the topic.

Materials & Process

To conduct this exploration and analysis of development projects, I utilized both OpenRefine and Microsoft Excel for data cleansing and prepping while the visualizations were created in Tableau Public.

My process entailed the following steps:

1. Obtain Sovereign Projects dataset from the ADB Data Library.
2. Clean and organize data with OpenRefine. In particular, filling in null/empty cells and formatting dates into YYYY-MM-DD (Broman & Woo).
3. Finalize dataset in Microsoft Excel by removing extraneous columns.
4. Open finalized dataset in Tableau Public to conduct analysis and create visualizations.

Results & Analysis

To assist in my analysis and visualizations, I developed the following research questions:

1. What is the overall trend of financing on development projects, if any?
2. Which countries receive the most financing and/or the most projects?
3. Which sectors/projects receive the most financing?

In answering my first research question, I decided to create a bar graph to visualize the number of projects approved from 1998 to 2019 while also adding a secondary line to simultaneously show the trend in financing. The graph shows a steady increase in the number of approved projects, with what appears to be a slight decrease in recent years. The finance line generally mirrors the projects trend, with a few years trending in the opposite direction, such as in 2017. This suggests that while ADB may have taken on less projects, the approved projects were on a much larger scale that may affect nationwide efforts, thus, necessitating more financing.

I decided to create two visualizations for my second research question. I found a bar graph to be the most appropriate format to visually display quantitative information as users can easily see which countries received the most financing without having to examine the numbers. The map was a nice break from the graphs I have been using and also sought to serve as a visual cue about the region. I enabled the density feature to help draw attention to which countries were receiving the most projects. From the visualizations created, it was clear that China and India were the two countries receiving the most projects and financing, respectively

To address my third research question, I decided on the packed bubbles visualization as users can easily discern which sector is receiving the most financing without referring to numbers.

With China and India in mind, I wanted to further examine the finances and projects occurring in these countries and set out to create visualizations to represent which sectors are receiving the most funding in each country. While I originally created another set of packed bubbles visualizations to demonstrate the similarities China and India had with the overall sector financing, the resulting dashboard ended up looking repetitive.

Instead, I decided on listing the top five development projects receiving the most financing in each country to highlight the similarities. By specifically providing the project names, such as “railway project,” users have a better idea of what kind of projects are being developed rather than seeing a generic category, such as “transport.”

Reflection

While large datasets can be intimidating, it is comforting to know that there are many resources and tools to assist in the data cleansing and preparation process. In cleansing my dataset, I found that OpenRefine’s cluster and facet features were especially advantageous as it made editing large volumes of data a much easier process. However, I still found myself relying on Microsoft Excel for the finishing touches and as a cross reference for my analysis.

I found Tableau to be the perfect launching pad for research and analysis with its user-friendly interface and ability to generate instant visualizations. The research questions answered in this assignment serve as a starting point for a much more in-depth analysis into these development projects. As I would like to continue creating visualizations that can reach a general audience from all backgrounds, I find that further examination of the financing behind these projects would serve as the next best step as I can explore different methods of visualizing financial data that all can understand.

Final dashboard links:
https://public.tableau.com/views/Final4_15927116076000/Dashboard1?:language=en&:display_count=y&publish=yes&:origin=viz_share_link

https://public.tableau.com/views/dashboard2_15927971362050/Dashboard1?:language=en&:display_count=y&publish=yes&:origin=viz_share_link

Resources:

Asian Development Bank: Who We Are
https://www.adb.org/who-we-are/main

Asian Development Bank: Data Library
https://data.adb.org/

Broman K.W., and Woo K.H. 2017. “Data organization in spreadsheets.” PeerJ Preprints 5:e3183v1 https://doi.org/10.7287/peerj.preprints.3183v1