Machine Learning for Official Statistics
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  • Under the HLG-MOS ML Project Work Package 1, a total of 21 studies were conducted with three broad themes: coding and classification, edit and imputation and imagery analysis
  • Work package 1 report provides executive summary of all three application areas.
  • Theme report provides overview of context, methods, practices and lessons learned from pilot studies under each theme.
  • Pilot study paper contains details about each study, please see Studies and Codes page for information about programming language and codes 
WP1Pilot Study ThemePilot Study Paper
Work Package (WP) 1 - Pilot Studies Executive Summary Report (to be updated)

Coding and Classification Theme Report

  1. Mexico - Occupation and Economic activity coding using natural language processing
  2. Canada - Industry and Occupation Coding
  3. Belgium Flanders - Sentiment Analysis of twitter data
  4. Serbia - Coding textually described data on economic activity collected from Labour Force Survey
  5. USA - Coding Workplace Injury and Illness
  6. Poland - Production description to ECOICOP
  7. IMF - Automated Coding using the IMF’s Catalog of Time Series
  8. Iceland - Automatic coding of occupation and industry in social statistical surveys
  9. Norway - Standard Industrial Code Classification by Using Machine Learning

Edit and Imputation Theme Report

  1. Italy - Imputation of the variable “Attained Level of Education” in Base Register of Individuals
  2. Poland - Imputation in the sample survey on participation of Polish residents in trips
  3. Germany - Machine learning for imputation 
  4. Belgium VITO - Early estimates of energy balance statistics using machine learning
  5. UK - Editing of Living Cost and Food Survey Income data
  6. Italy - Editing in the Italian Register of the Public Administration
  7. Italy - Machine Learning for Data Editing Cleaning in NSI : Some ideas and hints

Imagery Analysis Theme Report

  1. Australia - Address Register Automated Image Recognition (AIR) model
  2. Netherlands - Learning statistical information from images: a proof of concept
  3. Switzerland - Arealstatistik Deep Learning (ADELE)
  4. Mexico - Use of Landsat satellite data for the mapping of urban areas in non-census years 
  5. UNECE - Generic Pipeline for Production of Official Statistics Using Satellite Data and Machine Learning



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