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November 2017

Tracing the roots of TCdata360 datasets: an interactive network graph

Reg Onglao's picture

When doing data analysis, it's common for indicators to take the spotlight whereas datasets usually take the backseat as an attribution footnote or as a metadata popup.

However, we often forget how intertwined dataset sources are and how this affects data analysis. For instance, we can never assume that indicators from different datasets are mutually exclusive – it's possible for them to be the same indicator or to have an influence on the other as a component weight in an index, if the other dataset were used as a source for the other.

In this blog, we're interested to see if this applies to TCdata360 by taking a deeper look at its "dataset genealogy" and answer questions such as – Is it safe to do cross-dataset analysis using TCdata360 datasets? Are there interesting patterns in the relationships between TCdata360 datasets?

Quick introduction to network graphs

We call a dataset which serves as a data source for another dataset as "source", and a dataset which pulls indicator data from another as "target". Collectively, all of these are called "nodes".

To see the relationships between TCdata360 datasets, we mapped these in a directed network graph wherein each dataset is a node. By directed, we mean that source nodes are connected to their target nodes through an arrow, since direction is important to identify source from target nodes. For the purposes of this blog, we restricted the network graph to contain datasets within TCdata360 only; thus, all data sources and targets external to TCdata360 will not be included in our analysis.

Here's how the network graph looks like.

Each dataset is represented by a circle (aka "node") and is grouped and color-coded by data owner or institution. The direction from any source to target node is clearer in the interactive version, wherein there's a small arrow on the connecting line which shows the direction from target to source.

Are South Asian countries sinking into a debt trap?

Bidisha Das's picture
 

This blog is part of a series based on International Debt Statistics 2018.

The 2018 edition of International Debt Statistics (IDS 2018) which presents statistics and analysis on financial flows (debt and equity) for 123 low-and middle-income countries has just been released. One of the key observations of IDS 2018 is that net financial flows in 2016 to all developing countries witnessed a more than threefold increase over their 2015 level. This was driven entirely by net debt flows, which increased by $542 billion in 2016. Consequently, total external debt outstanding of all developing countries went up to $6.9 trillion, an increase of 4.1 percent over 2015. Interestingly, South Asia seems to deviate from this norm of IDS 2018.

External debt outstanding of South Asia contracted in 2016

South Asia is the only region that has shown a contraction in the total external debt outstanding in 2016. The total external debt stock of South Asia contracted by almost 2 percent as net debt flows into the region turned negative ($-7.7) for the first time in a decade. More specifically, this is the result of net long-term external debt flows turning negative (-$12.5 billion) implying that principal repayments by South Asia, on long-term external debt far exceeded disbursements.

Interactive product export streamgraphs with data360r (now in CRAN!)

Reg Onglao's picture

Building beautiful, interactive charts is becoming easier nowadays in R, especially with open source packages such as plot.ly, ggplot2 and leaflet. But behind the scenes, there is an often untold, gruesome part of creating data visualizations -- downloading, cleaning, and processing data into the correct format.

Making data access and download easier is one of the reasons we developed data360r, recently available on CRAN and the newest addition to the TCdata360 Data Science Corner.

Data360r is a nifty R wrapper for the TCdata360 API, where R users ranging from beginners to experts can easily download trade and competitiveness data, metadata, and resources found in TCdata360 using single-line R functions.

In an earlier blog, we outlined some benefits of using data360r. In this blog, we’ll show you how to make an interactive streamgraph using the data360r and streamgraph packages in just a few lines of code! For more usecases and tips, go to https://tcdata360.worldbank.org/tools/data360r.

Five years of investments in open data

Tim Herzog's picture
Also available in: 中文
 
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This year marks the fifth anniversary of the World Bank’s efforts to help countries launch their own open data initiatives, and harness the power of open data to benefit their citizens. A new report provides insights into how open data is benefitting countries, what strategies are working well, what could be improved.

The report provides the most comprehensive snapshot of Bank-funded open data activities to date. In the last five years, the Bank has provided technical assistance and funding for open data activities in over 50 countries, conservatively estimated at more than $50 million from a variety of sources. In many cases Bank funding has leveraged support from other partners or co-sponsorship by countries and other institutions. Within the Bank, the Trust Fund for Statistical Capacity Building (TFSCB) has been the most significant source of funding for open data. The TFSCB has financed over 20 projects in 16 countries, as well as 6 grants for regional and global activities.

Supporting over 45 countries with national and sector-specific open data

Support for open data has taken a variety of forms. To date, 45 Open Data Readiness Assessments (ODRAs) have been completed at national and sub-national levels, which have helped raise awareness and catalyze public and private efforts to advance open data within countries. There are now sector-specific ODRA tools for business, energy, and transport. The Bank has invested in a range of open data learning and knowledge products, including data literacy courses and the Open Data Toolkit, and collaborated with its global partners to support academic research, a series of regional conferences, and open data implementation. The report also found that these initial efforts have catalyzed longer-term project investments, i.e., IBRD loans and IDA credits, with open data implementation components in at least 14 countries.

Hoping for a cloudy future for Caribbean statistics

Michael M. Lokshin's picture
Photo Credit: Lou Gold

Hurricanes Irma and Maria recently devastated the Caribbean region. Infrastructure in Dominica was severely damaged and the country suffered a total loss of its annual agricultural production. The entire population of Barbuda had to be evacuated to Antigua and other islands. Estimates by the World Bank indicate that Irma caused damages equivalent to 14 percent of GDP for Antigua and Barbuda, and up to 200 percent of GDP for Dominica. The increasing frequency of hurricanes poses a threat to the economic development and wellbeing of 40 million people living in the region.

The World Bank and other development institutions acted quickly by offering support to assess damages and losses, respond to the disaster, and assist with recovery by delivering financial packages and supporting emergency operations. However, in the longer term, the focus is on building the resilience of these small island states to natural disasters.

Data: critical for responding to disasters, but also vulnerable to them

Systems of national statistics can provide critical information about the extent of a disaster, help guide recovery operations, and assess the preparedness of countries to future shocks.  At the same time, the reliance of National Statistical Offices (NSOs) on local IT infrastructure makes them highly vulnerable to natural disasters. Computers, servers, and networks cannot operate without power; flooding and high humidity destroys hardware and storage media; looting and breaking into abandoned buildings puts sensitive information at the risk of falling into the wrong hands. Fortifying NSO buildings to withstand Category 5 hurricanes and enabling the offices to continue functioning afterwards is prohibitively expensive. Even if such structures were built, staffing would remain an issue, particularly if the entire population of the country was evacuated (as in case of Barbuda).

Cloud computing provides a very effective way to resolve that problem at a small fraction of the cost.