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3. Pilot studies and Theme Reports 

The three themes agreed by the project: C&C, E&I and Imagery are all based on classification tasks with the exception of Imputation. For Editing it is a classification of records into two classes, the ‘Change’ class where the data are inconsistent, missing or suspicious looking and the ‘No-Change’ class, where the data do not need any further attention and are deemed to be correct or consistent. The sentiment analysis of web based data is included in the C&C theme as it is a classification task to classify the data into the chosen sentiment categories. Imagery, an example of big data and alternative data sources classifies satellite or aerial images or their components into classes like ‘Urban’ and ‘Non-Urban’.

All pilot studies used supervised ML, this is where the algorithm ‘learns’ from training data that have been labelled, e.g. where the correct code has been assigned manually. This can be an occupation code assigned to a data record with an occupational text description, or the type of object visible on a satellite image. These labelled training data allow the algorithm during the learning phase to recognise rules or patterns in the data without having to explicitly formulating those rules. New data that have not been labelled can then be fed into the algorithm for it to categorise and recognise these data.

The participants of this project have submitted reports on their respective pilot studies. These were then summarised for each of the three themes into Theme Report. Given references to these reports are to the statswiki.unece.org web site. All reports will be accessible to the public. Further information can be obtained by contacting unece.org or the authors of the reports. This summary report uses information provided in the pilot study reports as well as the 3 theme reports.