Megatrend & Intervention Impact Analyzer for Jobs
Team NSI Estonia submission for EUBD Hackathlon 2017
- Innar Liiv
- Rain Öpik
- Toomas Kirt
The prototype application can be viewed in a web browser. We recommend to use the latest version of Google Chrome.
Note: Internet Explorer and Safari are not supported. The latest version of Firefox opens the app, but render performance is suboptimal.
How to use
Moving around and getting information
The application has two modes:
Move & zoom mode - click and drag mouse to move the graph. Scroll mouse wheel to zoom in and out.
Query mode - hover mouse over a node to display a small tooltip with demand and supply numbers. Hovering also highlights connected jobs and fades out the rest of the graph.
Click Right Mouse Button to switch between Move and Query modes.
If the display does not show anything or you get stuck, please reload the page in browser (F5 or ⌘ + R).
A node in the graph denotes a job. A job is linked to other jobs based on similarity - for each job we found top 3 jobs that have the largest number of overlapping skills.
Nodes are colored to highlight the amount of job vacancies and jobseekers.
The visualizer supports several layers:
- Left half of a node is colored by the number of vacancies available for that job (demand). White means no vacancies, light pink low and red denoting high demand.
- Right half is colored by number of job seekers who have listed this job in their desired job list. Color gradation is similar to the left half.
- Node is marked with a yellow halo when this job is affected by the Megatrend, i.e. job is in the list of jobs suspectible for automation / computerization .
Demand + Supply
- This is basically the same visualization as Composite, except the Megatrend markers (yellow halo) are not drawn.
- Node is colored red when the job is affected by the Megatrend. Non-affected jobs are colored white.
- Node is colored red when at least one job seeker has listed this job in their desired job list. White nodes denote jobs that no-one desires.
- Node is colored red when this job is listed in at least one job vacancy. White nodes denote jobs with no demand.
Demand & supply imbalance:
Colors values for the left and right half (demand and supply) are normalized separately due to huge imbalance in EURES data. Some countries have no job seekers in EURES while showing lots of vacancies and vice versa.
The default mode (Show imbalance unchecked) will help to identify most demanded jobs - look for nodes with a bright red left half. Similarly, jobs with the largest supply of seekers have a bright right half.
Tick the checkbox Show imbalance to normalize the colors to the same scale. This helps to visualize imbalance - when the left half of the node is brighter red compared to the right, this job has unsatisfied demand. Conversely, a brighter right half marks jobs with oversupply of job seekers.
Note: the EURES data contains huge discrepancies between supply and demand across different countries. Some countries have no job seekers in EURES while showing lots of vacancies and vice versa. Therefore the Show imbalance mode may reveal only the extremities.
.----------------. .----------------. .----------------. .--------------. .---------------. | 1. Hackathlon | | 2. Occupation | | 3. Export data | | 4. Calculate | | 5. Visualizer | | datasets in | --> | graph data in | --> | to PSV files | --> | graph layout | --> | UI | | PostgreSQL and | | PostgreSQL | | '----------------' | '--------------' | '---------------' | Apache Hive | '----------------' | | | '----------------' | | | + eubd.vis.g_node + exp_node.psv + vis_node.csv + eubd.vis.g_link + exp_link.psv + vis_link.csv
- EURES CV and job vacancy dataset
- ESCO RDF, converted to relational structure suitable for SQL
- List of Jobs Susceptible for Automation / Computerization (Oxford, 2017)
- Occupation classifications mapping table from Occupation classifications crosswalks - from O*NET-SOC to ISCO (2016)
2. Occupation graph
A graph is defined by two entities:
- Node - denotes an ESCO occupation. Each occupation may have additional data attributes attached to it.
- Link - two nodes (occupations) are connected when they are similar to each other.
Therefore the occupation graph is based similarity between ESCO occupations. We decided to define similarity based on skill information in the ESCO RDF classifier.
g_link - linking similar occupations together
For a given ESCO occupation, we queried all skills that this occupation requires (relation type essentialSkill in RDF). Then we matched all ESCO occupations that require the same skills. This produces a mapping ESCO occupation --> ESCO occupation with a similarity measure that describes the ratio of shared skills between two occupations to number of all skills required by the first occupation.
Let's take two occupations:
00cee175-1376-43fb-9f02-ba3d7a910a58 - bus driver and
e75305db-9011-4ee0-ab62-8d41a98f807e - private chauffeur and
enumerate all skills that are essential for both occupations.
|00cee175-1376-43fb-9f02-ba3d7a910a58||e75305db-9011-4ee0-ab62-8d41a98f807e||provide first aid||N/A|
|00cee175-1376-43fb-9f02-ba3d7a910a58||e75305db-9011-4ee0-ab62-8d41a98f807e||N/A||maintain personal hygiene standards|
|00cee175-1376-43fb-9f02-ba3d7a910a58||e75305db-9011-4ee0-ab62-8d41a98f807e||drive in urban areas||drive in urban areas|
|00cee175-1376-43fb-9f02-ba3d7a910a58||e75305db-9011-4ee0-ab62-8d41a98f807e||keep time accurately||keep time accurately|
|00cee175-1376-43fb-9f02-ba3d7a910a58||e75305db-9011-4ee0-ab62-8d41a98f807e||provide information to passengers||provide information to passengers|
The skills in this table can be divided into three groups:
- Skill that is only required for the first occupation (eg.
- Skill that is only required for the second occupation (eg.
- Skill that is required by both of these occupations.
When count the number of distinct skills that are required for both occupations (22 for this example) and divide it by the number of distinct skills required for the first occupation (35), we get a percentage of matching skills, which we can use as a similarity measure between these two occupations.
The following table shows an example of the graph for two occupations:
|00cee175-1376-43fb-9f02-ba3d7a910a58||3a15ec1b-9250-41a0-9344-feb2956481b7||0.80||bus driver -> trolley bus driver|
|00cee175-1376-43fb-9f02-ba3d7a910a58||03e02554-15d1-4697-960c-8909e7d36f7e||0.77||bus driver -> tram driver|
|00cee175-1376-43fb-9f02-ba3d7a910a58||e75305db-9011-4ee0-ab62-8d41a98f807e||0.63||bus driver -> private chauffeur|
|45037d43-a8f5-4f46-b332-b2935bc305f4||c5d779f4-345b-4918-872b-a1cbaeb1d9be||0.60||cargo vehicle driver -> dangerous goods driver|
|45037d43-a8f5-4f46-b332-b2935bc305f4||00cee175-1376-43fb-9f02-ba3d7a910a58||0.55||cargo vehicle driver -> bus driver|
|45037d43-a8f5-4f46-b332-b2935bc305f4||e75305db-9011-4ee0-ab62-8d41a98f807e||0.45||cargo vehicle driver -> private chauffeur|
The resulting matrix is very large, as contains occupation pairs that are loosely connected by a very generic skill.
For example, both
bus driver and
physiotherapy assistant have an
use different communication channels as essential skill, which connects them in the graph.
However when we calculate the skill match ratio, we get a modest 0.02. Also the connection between these occupations does not make sense in real life, as it is difficult
to imagine that a person skilled in operating heavy vehicles could easily apply for a position that requires medical skills.
Therefore we decided to prune the graph of weakly connected occupation pairs and take only 3 most similar occupations for every occupation.
g_node - annotating occupations with supply and demand data
Since each node in the occupation graph denotes ESCO occupation, we would like to know how this occupation will be affected by
automation or computerization. The list of Jobs Suspectible for Automation  originally has SOC occupation codes.
Mapping ISCO to SOC  is unfortunately one-to-many, which means that some ISCO occupations (eg.
8332 - Heavy truck and lorry drivers)
are assocaited with several SOC occupations (
53-1031 - Driver/Sales Workers and
53-3032 - Heavy and Tractor-Trailer Truck Drivers) that
may have differing probabilities for automation (respectively
0.79). To solve this ambiguity, we have calculated two probabilities, maximum and average.
After knowing, which jobs are going to impacted, we wanted to assess, how many people would be affected by this trend. Since we have based our tool on EURES CV and job vacancy dataset, we could handily calculate the number of vacancies and number of unique persons that have marked this occupation as their desired job.
However the amount of data in EURES dataset makes direct querying inefficient, therefore we decided to crete a special denormalized (crosstab) table for storing occupation-based supply/demand counts by country. We used Apache Hive to run queries against EURES datasets and imported the results back to SQL.
For example, based on EURES data, there are 1925 job vacancies for 'bus driver' in Austria and 5 job seekers have marked 'bus driver' as their desired occupation. See table below:
|45037d43-a8f5-4f46-b332-b2935bc305f4||cargo vehicle driver||0.79||666061||1729||13305||14||35475||15|
Explanation of columns in
- esco_oc_key - ESCO occupation code .
- preflabel_en - Preferred occupation label in English.
- ox_max_prob - Probability of automation for this occupation.
- all_jv - total number of vacancies for this occupation.
- at_jv - number of vacancies in Austria (AT).
- at_be - number of vacancies in Belgium (BE).
- all_cvdes - total number of unique job seekers who have listed this occupation as their desired job.
- at_cvdes - number of job seekers in Austria who desire this job.
- be_cvdes - number of job seekers in Belgium who desire this job.
3. Export data to PSV files
We designed the visualizer tool to run without server backend and online connection to database. This makes it easy to host the tool on a static website (like GitHub) without any running costs.
4. Calculate graph layout
Experience with d3.js have shown that real-time calculation of graph layout (the position of every node) may be slow for graphs with non-trivial structure. The occupation graph has 2950 nodes and 8838 links and after some experimentation we decided to pre-calculate the positions of graph nodes.
We used the SFDP layout algorithm from Python graph-tool for calculating the position of nodes and reindexing node identifiers to format that is suitable for visualizer.
5. Visualizer UI
This project is licensed under the MIT License - see the LICENSE file for details.
-  Carl Benedikt Frey, Michael A. Osborne, "The future of employment: How susceptible are jobs to computerisation?", Technological Forecasting and Social Change, Volume 114, January 2017, Pages 254-280
-  Wojciech Hardy, David Autor, Daron Acemoglu, "Occupation classifications crosswalks - from O*NET-SOC to ISCO", 2016 [Online]. Available at: http://ibs.org.pl/en/resources/occupation-classifications-crosswalks-from-onet-soc-to-isco/