Recap of Previous Posts Part 1 - Overview of the Public Procurement TED dataset Part 2 - Identification of Entities involved in procurement Part 3 - Attribution of GHG Emissions using the CPV classification In the earlier parts of this series we motivated and defined the scope of our exploration of Public Procurement data, we dug deeper into constructing economic representations of the public procurement process. We also linked procurement entities to private sector sellers.
Recap of Previous Posts Part 1 - Overview of the Public Procurement TED dataset Part 2 - Identification of Entities involved in procurement In the first part of this series we motivated and defined the scope of a study explores Public Procurement data. In the second instalment we dug deeper into an important facet of the data, with the aim of constructing a meaningful economic representation of the public procurement process.
Recap of Previous Post Part 1 - Overview In the first part of this series we motivated and defined the scope of a study that explores Public Procurement data. We discussed the meaning of the main relevant terms (Open Data, Open Source, Green Public Procurement) and briefly reviewed the current state and challenges of the latter in EU context. Further, we took a first look into the EU’s TED Database (which is the main source of data) and highlighted some key statistics which bring to light information such as: size of the dataset, overall structure and some data quality aspects.
Introduction In a series of posts we will explore the role of Open Data and Open Source in enabling and accelerating the broad based effort towards Green Public Procurement (GPP). There are several important (and possibly obscure or “buzzwordy”) terms in the above sentence, so the first order of business will be to unpack them. Let us start with the term Public Procurement which will be the main domain of interest in this study.
Motivation Fig 1. An economic network as a graph. The economy is a complex tangle of various agents that interact via transactions (sales and purchases) and contracts (lending, investing). In recent times more and more techniques from graph theory and network science are brought to bear on economic analysis. On the other hand, ever since the seminal contributions of Leontief, Input-Output Models (IO) have been widely used to describe economic relationships between economic actors (e.
The frontpage graphic is adapted from Steffen et al. “Planetary Boundaries: Guiding human development on a changing planet". Science (2015). The Planetary Boundaries concept was proposed in 2009 by this group of Earth system and environmental scientists. The group suggested that finding a “safe operating space for humanity” is a precondition for sustainable development. The framework is based on scientific evidence that human actions since the Industrial Revolution have become the main driver of global environmental change.
Visualizing a year in lockdowns and restricted mobility As we move into February 2021 the world will be experiencing almost a year under pandemic conditions. This has markedly changed behavioral patterns of human mobility across the board. One major difference with previous pandemics is that through the use of a variety of digital technologies and new data collection channels we know have an unprecedented view of those changing mobility patterns.
Constructing a Global Mobility Index (GMI): In previous posts (here, and here) we introduced new Open Risk Dashboard functionalities that integrate COVID-19 community mobility data (currently focusing on the datasets provided by Google). As a reminder, these reports chart over time human mobility trends collected from mobile geolocation data. The granularity is by geography and across different categories of places / activities such as retail and recreation areas, groceries and pharmacies, parks, transit stations, workplaces, and residential areas.
What this blog post is about (and what it isn’t): With the ever more widespread adoption of Data Science, defined as the intensive use of data in various forms of decision making, there is a renewed interest in Visualization as an effective channel for humans to understand data at various stages of the data lifecycle. There is a large variety of data visualization tools which can produce an ever more bewildering variety of visualization types
The community mobility reports and OpenCPM: In a previous post we introduced new OpenCPM functionality that integrates COVID-19 community mobility data (currently from Google). The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. While these reports are unlikely to persist as open data sources in the long term, the current availability (as of May 2020) enables providing within OpenCPM a mobility data dashboard that can help draw insights through visualization and statistical analysis.
The community mobility reports and OpenCPM: As the COVID-19 pandemic unfolded technology providers (most notably Google and Apple) made available to the public aggregated and anonymized data about human mobility in the crisis period (on the basis of smartphone location data). These Community Mobility Reports provide insights into how mobility patterns changed in response both to pandemic news and policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of locations and activities, such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.
Making Open Risk Data easier: In an earlier blog post we discussed the promise of Open Risk Data and how the widespread availability of good information that is relevant for risk management can substantially help mitigate diverse risks. The list of Open Risk Data providers, particularly from public sector, keeps increasing and we are aiming to document all available datasets in the dedicated page of the Open Risk Manual.
The motivation for federated credit risk models: Federated learning is a machine learning technique that is receiving increased attention in diverse data driven application domains that have data privacy concerns. The essence of the concept is to train algorithms across decentralized servers, each holding their own local data samples, hence without the need to exchange potentially sensitive information. The construction of a common model is achieved through the exchange of derived data (gradients, parameters, weights etc).
Visualization of large scale economic data sets: Economic data are increasingly being aggregated and disseminated by Statistics Agencies and Central Banks using modern API’s (application programming interfaces) which enable unprecedented accessibility to wider audiences. In turn the availability of relevant information enables more informed decision making by a variety of actors in both public and private sectors. An excellent example of such a modern facility is the European Central Bank’s Statistical Data Warehouse (SDW), an online economic data repository that provides features to access, find, compare, download and share the ECB’s published statistical information.
Motivation for Building an open source database based on EBA’s Standardized NPL Templates In a recent insightful piece “Overcoming non-performing loan market failures with transaction platforms”, Fell et al. dug deeply into the market failures that help perpetuate the NPL problem. They highlight, in particular, information asymmetries and the attendant costs of valuing NPL portfolios as key obstacles. In the same wavelength, the European Banking Authority published standardized NPL data templates as a step towards reducing the obstacles that prevent the reduction of NPL’s.
There is a legend that every time a data set is released into the open, somewhere dies a black swan The Promise of Open Risk Data: Well, it is not a true legend. Legends take centuries of oral storytelling to form. In our frantic age, dominated by the daily news cycle and viral twitter storms, legends have been replaced by the rather more short-lived memes and #hashtags. Black Swans need no introductions The whole informal theory of black swans concerns improbable events (low likelihood events) that come as a nasty surprise and have large impact.
StatsNews: Aggregating Economic Open Data news: StatsNews Version 1.3 of the RegNews Aggregator includes a separate stream of economic opendata news releases. Regnews is a web app developed by Open Risk to assist with keeping abreast with diverse financial regulatory news releases and publications. The app data are directly derived from the published regulatory RSS sheets (NB some are not conforming to RSS standards). If in doubt please refer to the original feeds (links provided).
Regnews: Financial Regulatory News Aggregation Version 1.2: The #RegNews Aggregator is a web app developed by Open Riskto assist with keeping abreast with diverse financial regulatory news releases and publications. The app data are directly derived from the published regulatory RSS sheets (NB some are not conforming to RSS standards). If in doubt please refer to the original feeds (links provided). Copyright of the publications is with the respective authoring institutions.
Risk Management Internship: In finance, it’s the best of times, it’s the worst of times It is a special moment to start a career in financial services. We are walking amid the ruins of the previous financial order. Fallen banks, broken markets, negative interest rates, shell-shocked economies and discredited theoretical assumptions. We see the enormous cost and impact to the welfare of society of a less than perfect financial system which has not kept pace with the advancement of our general knowledge and technical capabilities in most other domains.
Correlation Radar added to the Dashboard: About the Correlation Radar: The EU Risk Dashboard is a web app developed by Open Risk to assist with the exploration and understanding of the large number of economic indicators published by the ECB in its Statistical Data Warehouse. The app data are derived from the timeseries available in the Warehouse. Most readings in the currently selected series are monthly or quarterly and are updated when those become available at the Warehouse.