Visualization

Connecting the Dots, Tensor Representations of Activitypub Networks

Connecting the Dots, Tensor Representations of Activitypub Networks

Reading Time: 4 min.
What are ActivityPub Networks? ActivityPub is a technical specification towards decentralized (more precisely, federated) social networking (termed the Fediverse) based upon the exchange of ActivityStreams messages that follow the Activity Vocabulary. The ActivityPub proposal has been standardized and published by the W3C and has motivated the design of several federated social networking systems. There are presently several concrete ActivityPub compliant implementations and the protocol sees meaningful adoption, primarily in the domain of federated social networks.
Input-Output Models as Graph Networks

Input-Output Models as Graph Networks

We discuss the relation of economic input-output models with graph theory and networks

Reading Time: 15 min.
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.
Connecting the Dots: Concentration, diversity, inequality and sparsity in economic networks

Connecting the Dots: Concentration, diversity, inequality and sparsity in economic networks

In this second Open Risk White Paper on "Connecting the Dots" we examine measures of concentration, diversity, inequality and sparsity in the context of economic systems represented as network (graph) structures.

Reading Time: 6 min.
Concentration, diversity, inequality and sparsity in the context of economic networks In this second Open Risk White Paper on Connecting the Dots we examine measures of concentration, diversity, inequality and sparsity in the context of economic systems represented as network (graph) structures. We adopt a stylized description of economies as property graphs and illustrate how relevant concepts can represent in this language. We explore in some detail data types representing economic network data and their statistical nature which is critical in their use in concentration analysis.
9 Ways Graphs Show Up in Data Science

9 Ways Graphs Show Up in Data Science

We explore a variety of distinct uses of graph structures in data science. We review various important graph types and sketch their linkages and relationships. The review provides an operational guide towards a better overall understanding of those powerful tools

Reading Time: 17 min.
Graphs seem to be everywhere in modern data science Graphs (and the related concept of Networks) have emerged from a relative mathematical and physics niche to an ubiquitous model for describing and interpreting various phenomena. While the scholarly account of how this came about would probably need a dedicated book, there is no doubt that one of the key factors that increased the visibility of the graph concept is the near universal adoption of digital social networks.
Visual Overview of Built-In Python Data Types

Visual Overview of Built-In Python Data Types

We discuss the Python language built-in data types and a visualization that organizes them according to key attributes

Reading Time: 4 min.
Data Types are a fundamental building block of data science Data science is about data, but data are not simple and tame beasts. They have character and attitude, which can cause a lot of friction between them and the data scientist. There is a lot of sweat and tears involved when confronting data, but data scientists can do worse than know how to handle in particular Data Type quirks. Namely, a good fraction of data science involves not modelling data, not transforming data, not even cleaning data but simply goading data around the right containers, providing them with the right stage that fits their character.
Visualization of a Planet in Lockdown

Visualization of a Planet in Lockdown

We visualize global mobility patterns over a full year of pandemic induced lockdowns

Reading Time: 5 min.
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.
A Global Mobility Index

A Global Mobility Index

We introduce a global mobility index that averages Google mobility data across all available countries (weighting by population) to provide an overall view of how the pandemic has influenced human mobility

Reading Time: 6 min.
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.
Is Global Debt Truly Astronomical?

Is Global Debt Truly Astronomical?

Is Global Debt Truly Astronomical?

Reading Time: 11 min.
Is the size of global debt truly “astronomical”? The notion of astronomical numbers and figures is quite frequently seeping in everyday language when large quantities of something are encountered in “normal” life. The strict definition of astronomical is obviously something of, or relating to, astronomy and astronomical observations but in common usage it also denotes something enormously or inconceivably large. This is, of course, because astronomical figures are inconceivably large!
Logarithmic Sankey Visualization of Credit Migrations

Logarithmic Sankey Visualization of Credit Migrations

Sankey diagrams are very useful for the visualization of flows, especially when there is a conserved quantity. They can be tricky when some of the flows are much smaller than others. In the latest release of transitionMatrix we include an example of a log-scale version of Sankey

Reading Time: 5 min.
Using Sankey Diagrams Sankey Diagrams are a type of flow diagram composed of interconnected arrows. The width of the arrows is proportional to the flow rate. Sankey diagrams are often used in physical sciences (physics, chemistry, biology) and engineering but also in economics. They can be used to represent the relative role and significance of various inputs and outputs in a given process. Sankey diagrams emphasize the major transfers within a system.
21 Ways to Visualize a Timeseries

21 Ways to Visualize a Timeseries

We explore a variety of distinct ways to visualize the same simple dataset. The post is an excursion into the fundamentals of visualization - a partial deconstruction of the process that highlights some common techniques and associated issues.

Reading Time: 29 min.
What this blog post is about (and what it isn’t) With the ever more widespread adoption of Data Science tools (defined loosely as the intensive use of data in decision-making), there is a renewed interest in Visualization as an effective channel for humans to understand information 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:
Comparing Google Community Mobility Reports Across Countries

Comparing Google Community Mobility Reports Across Countries

Reading Time: 5 min.
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.
Exploring Community Mobility Reports Using OpenCPM

Exploring Community Mobility Reports Using OpenCPM

Reading Time: 7 min.
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.
New Open Risk Academy Course: Introduction to Geojson

New Open Risk Academy Course: Introduction to Geojson

Reading Time: 2 min.
Course Content This course is a CrashProgram (short course) introducing the GeoJSON specification for the encoding of geospatial features. The course is at an introductory technical level. It requires some familiarity with data specifications such as JSON and a very basic knowledge of Python Who Is This Course For The course is useful to: Any developer or data scientist that wants to work with geospatial features encoded in the geojson format How Does The Course Help Mastering the course content provides background knowledge towards the following activities:
New Open Risk Academy Course: Simulation of Credit Contagion

New Open Risk Academy Course: Simulation of Credit Contagion

Reading Time: 2 min.
Course Content This course is an introduction to the concept of credit contagion. It covers the following topics: Contagion Risk Overview and Definition Various Contagion Types and Modelling Challenges The Simple Contagion Model by Davis and Lo Supply Chains Contagion Sovereign Contagion Who Is This Course For The course is useful to: Risk Analysts across the financial industry and beyond Risk Management students Quantitative Risk Managers developing or validating risk models How Does The Course Help Mastering the course content provides background knowledge towards the following activities:
Connecting the Dots: Economic Networks as Property Graphs

Connecting the Dots: Economic Networks as Property Graphs

Reading Time: 0 min.
Connecting the Dots: Economic Networks as Property Graphs We develop a quantitative framework that approaches economic networks from the point of view of contractual relationships between agents (and the interdependencies those generate). The representation of agent properties, transactions and contracts is done in the context of a property graph. A typical use case for the proposed framework is the study of credit networks. You can find the white paper here: (OpenRiskWP08_131219)
A new logo for the Open Risk Manual

A new logo for the Open Risk Manual

Reading Time: 0 min.
A new logo for the Open Risk Manual We have updated the logo for the Open Risk Manual. The new logo aims to make more explicit both the inspiration that the Open Risk Manual project draws from the trail-blazing Wikipedia initiative (and increasing collection of associated Wikimedia projects) and the reliance on the open source ecosystem of software and tools, including the mediawiki software and the important semantic mediawiki extension.
Visualization of large scale economic data sets

Visualization of large scale economic data sets

Reading Time: 3 min.
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.
Python versus R Language: A side by side comparison for quantitative risk modeling

Python versus R Language: A side by side comparison for quantitative risk modeling

Reading Time: 3 min.
Motivation for the comparison A large component of risk management relies on data processing and quantitative tools. In turn, such information processing pipelines and numerical algorithms must be implemented in computer systems. Computing systems come in an extraordinary large variety but in recent years open source software finds increased adoption for diverse applications (machine learning, data science, artificial intelligence). In particular cloud computing environments are primarily based on open source projects at the systems level.
Machine learning approaches to synthetic credit data

Machine learning approaches to synthetic credit data

Reading Time: 9 min.
The challenge with historical credit data Historical credit data are vital for a host of credit portfolio management activities: Starting with assessment of the performance of different types of credits and all the way to the construction of sophisticated credit risk models. Such is the importance of data inputs that for risk models impacting significant decision-making / external reporting there are even prescribed minimum requirements for the type and quality of necessary historical credit data.
Stressing Transition Matrices

Stressing Transition Matrices

Reading Time: 1 min.
Release of version 0.4.1 of the transitionMatrix package focuses on stressing transition matrices Further building the open source OpenCPM toolkit this realease of transitionMatrix features: Feature: Added functionality for conditioning multi-period transition matrices Training: Example calculation and visualization of conditional matrices Datasets: State space description and CGS mappings for top-6 credit rating agencies Conditional Transition Probabilities The calculation of conditional transition probabilities given an empirical transition matrix is a highly non-trivial task involving many modelling assumptions.