Megatrend & Intervention Impact Analyzer for Jobs
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README.md

Megatrend & Intervention Impact Analyzer for Jobs

Team NSI Estonia submission for EUBD Hackathlon 2017

Team

  • Innar Liiv
  • Rain Öpik
  • Toomas Kirt

Application prototype

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.

Click here to open the prototype application

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).

Graph description

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:

  • Composite

    • 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 [1].
  • Demand + Supply

    • This is basically the same visualization as Composite, except the Megatrend markers (yellow halo) are not drawn.
  • Highlight MegaTrend

    • Node is colored red when the job is affected by the Megatrend. Non-affected jobs are colored white.
  • Highlight Supply

    • 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.
  • Highlight Demand

    • 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.

Data processing

Processing pipeline

.----------------.     .----------------.     .----------------.     .--------------.     .---------------.
| 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

1. Datasets

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.

from_oc_key to_oc_key skill_in_from_oc skill_in_to_oc
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 manoeuvre bus 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 N/A park vehicles
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. bus driver)
  • Skill that is only required for the second occupation (eg. private chauffeur)
  • 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:

from_oc_key to_oc_key skill_match_pct comment
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 [1] originally has SOC occupation codes. Mapping ISCO to SOC [2] 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 0.98 and 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:

g_node

esco_oc_key preflabel_en ox_max_prob all_jv all_cvdes at_jv at_cvdes be_jv be_cvdes ...
00cee175-1376-43fb-9f02-ba3d7a910a58 bus driver 0.89 53936 535 1426 1925 5
45037d43-a8f5-4f46-b332-b2935bc305f4 cargo vehicle driver 0.79 666061 1729 13305 14 35475 15
...

Explanation of columns in g_node table:

  • 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.

The final table of similar occupations (g_link) was exported to file data/exp_link.psv and list of all nodes in the graph (g_node) was saved to file data/exp_node.psv.

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.

The script preprocess/convert_data.py prepares data files for the Visualizer. It reads data/exp_node.psv and data/exp_link.psv and produces data/vis_node.csv and data/vis_link.csv files.

5. Visualizer UI

Visualizer is built with d3.js, which renders a zoomable and scrollable SVG document for browsing the graph. See js/vis.js for details.


License

This project is licensed under the MIT License - see the LICENSE file for details.

References