MNO-MINDS WP3

This work package aims at: 

• Developing methodologies for processing and integrating MNO and non-MNO data, according to a well-defined total error framework, considering different level of data aggregation and data configuration and different uses of the MNO data; it will take into account quality aspects connected with the processing and integration of MNO and non-MNO data; 

• Implementing the methodologies in documented open-source tools and testing the methods with synthetic data; 

• Providing guidelines and recommendations for processing and integration of MNO and non-MNO data, including quality elements.

Methods and guidelines proposed by this work package are strictly related and anchored (but not limited) to the non[1]MNO data sources that are identified as best candidates by the landscaping analysis of WP2. In addition, they are inter[1]connected to the ad-hoc survey in WP4, since the metholodogy will help in guiding the requirements for the ad-hoc survey, and at the same time they will benefit from its results.

T3.1 "Developing methodologies for processing and integration of MNO and non-MNO data" 

We will develop methodologies under the quality reference frame described in Section 2.1, where e.g. both the input, throughput and output quality will be considered under the total error framework. 

The methodologies can be grouped under three themes:

I) The situations where MNO measures are treated as the target outcomes, whether or not these may be subjected to non-negligible measurement errors. 

II) The situations where MNO data are used as auxiliary information in combination with the measures available from non-MNO sources.

III) Validation methods for statistics where MNO measures are used either as target outcomes (as-is) or proxy outcomes (subjected to non-negligible measurement errors).

 The SPRINT (T2.2) will reveal the most relevant non-MNO sources for selected target statistics. Total error analysis (according to the quality reference frame described in Section 2.1) of these target statistics will identify the relevant data configurations and the associated error types. The methodologies developed in T3.1 will cover, but are not limited to, all the situations identified in this way. 

SSB (leader), Istat, STAT, DESTATIS, INE, CBS, INS, INE-PT

T3.2 "Developing open-source tools for implementing methodologies for processing and integration of MNO and nonMNO data, to be released publicly along with reference synthetic data"

Open-source software development will be closely linked with the investigations and achieved methods outlined in T3.1. Simulations will be used for demonstration and appraisal of the various methods. A legacy synthetic population dataset will be designed and generated for future methodological development and evaluation.

INS (leader), Istat, STAT, DESTATIS, INE, CBS, SSB

T3.3 "Developing guidelines for processing and integration of MNO and non-MNO data"

Guidelines will be prepared based on the results obtained in T3.1 and T3.2. As in previous ESSnet projects, where quality guidelines are produced, the focus will be the quality framework, the recommended practices, the definition of relevant quality indicators and measures. The quality reference frame (cf. Section 2.1), possibly improved and enhanced during this project, will provide a basis on which the input quality, throughput quality and output quality will be distinguished in the context of integrating MNO and non-MNO data. 

SSB (leader), Istat, STAT, DESTATIS, INE, CBS, INS, INE-PT