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Deliverable 3.2 - Guidelines for best practices implementation for transferring methodology

Deliverable 3.2 - Guidelines for best practices implementation for transferring methodology
Deliverable 3.2 - Guidelines for best practices implementation for transferring methodology
Today, big data is a buzz word. Although there have been attempts to properly define the term, a really universally accepted definition has not yet been given. Accordingly, many different types of data may be classified as big data or new data. These range from scanner data collected at retail outlets, through remote sensing data to mobile phone data. As the availability of such data increases, researchers try to make use of them by incorporating them into existing methods and developing new methods. These developments are also highly relevant for the estimation of well-being indicators, a core focus of the MAKSWELL project. The combination of new data sources and new or modified methods are promising especially where the estimation of well-being at a fine spatial resolution is concerned. While a comprehensive survey of the related literature and available data sets is out of the scope of this project, this deliverable collects a few (experimental) applications that shed a light on the potential benefits of these new approaches. Some drawbacks and practical implementation problems are addressed as well. Taken as a whole, the presented set of applications points to future research needs in the area and allows the derivation of some general best practice guidelines that can also inform other subject matter areas beside the measurement of poverty and well-being.
Makswell
Zimmermann, Thomas
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Polidoro, Federico
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di Leo, Federico
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Fedeli, Massimo
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Burger, Joep
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van den Brakel, Jan
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Pratesi, Monica
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Giusti, Caterina
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Marchetti, Stefano
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Biggeri, Luigi
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Bertarelli, Gaia
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Schirripa Spagnolo, Francesco
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Laureti, Tiziana
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Benedetti, Ilaria
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Smith, Paul A.
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Dawber, James
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Tzavidis, Nikolaos
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Luna Hernandez, Angela
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O'Donoghue, Jim
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Flower, Tanya
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Thomas, Heledd
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Würz, Nora
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Schmid, Timo
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Articus, Charlotte
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Burgard, Jan Pablo
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Caratiola, Christopher
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Dieckmann, Hanna
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Ertz, Florian
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Krause, Joscha
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Münnich, Ralf
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Wölwer, Anna-Lena
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Zimmermann, Thomas
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Polidoro, Federico
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di Leo, Federico
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Fedeli, Massimo
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Burger, Joep
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van den Brakel, Jan
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Pratesi, Monica
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Giusti, Caterina
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Marchetti, Stefano
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Biggeri, Luigi
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Bertarelli, Gaia
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Schirripa Spagnolo, Francesco
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Laureti, Tiziana
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Benedetti, Ilaria
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Smith, Paul A.
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Dawber, James
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Tzavidis, Nikolaos
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Luna Hernandez, Angela
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O'Donoghue, Jim
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Flower, Tanya
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Thomas, Heledd
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Würz, Nora
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Schmid, Timo
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Articus, Charlotte
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Burgard, Jan Pablo
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Caratiola, Christopher
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Dieckmann, Hanna
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Ertz, Florian
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Krause, Joscha
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Münnich, Ralf
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Wölwer, Anna-Lena
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Zimmermann, Thomas, Polidoro, Federico, di Leo, Federico, Fedeli, Massimo, Burger, Joep, van den Brakel, Jan, Pratesi, Monica, Giusti, Caterina, Marchetti, Stefano, Biggeri, Luigi, Bertarelli, Gaia, Schirripa Spagnolo, Francesco, Laureti, Tiziana, Benedetti, Ilaria, Smith, Paul A., Dawber, James, Tzavidis, Nikolaos, Luna Hernandez, Angela, O'Donoghue, Jim, Flower, Tanya, Thomas, Heledd, Würz, Nora, Schmid, Timo, Articus, Charlotte, Burgard, Jan Pablo, Caratiola, Christopher, Dieckmann, Hanna, Ertz, Florian, Krause, Joscha, Münnich, Ralf and Wölwer, Anna-Lena (2020) Deliverable 3.2 - Guidelines for best practices implementation for transferring methodology (Regional poverty measurement as a prototype for modern indicator methodology) Makswell 205pp.

Record type: Monograph (Project Report)

Abstract

Today, big data is a buzz word. Although there have been attempts to properly define the term, a really universally accepted definition has not yet been given. Accordingly, many different types of data may be classified as big data or new data. These range from scanner data collected at retail outlets, through remote sensing data to mobile phone data. As the availability of such data increases, researchers try to make use of them by incorporating them into existing methods and developing new methods. These developments are also highly relevant for the estimation of well-being indicators, a core focus of the MAKSWELL project. The combination of new data sources and new or modified methods are promising especially where the estimation of well-being at a fine spatial resolution is concerned. While a comprehensive survey of the related literature and available data sets is out of the scope of this project, this deliverable collects a few (experimental) applications that shed a light on the potential benefits of these new approaches. Some drawbacks and practical implementation problems are addressed as well. Taken as a whole, the presented set of applications points to future research needs in the area and allows the derivation of some general best practice guidelines that can also inform other subject matter areas beside the measurement of poverty and well-being.

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More information

Published date: October 2020

Identifiers

Local EPrints ID: 444697
URI: http://eprints.soton.ac.uk/id/eprint/444697
PURE UUID: b6e4c875-0f12-4292-84dd-4ab1744c1113
ORCID for Paul A. Smith: ORCID iD orcid.org/0000-0001-5337-2746
ORCID for Nikolaos Tzavidis: ORCID iD orcid.org/0000-0002-8413-8095
ORCID for Angela Luna Hernandez: ORCID iD orcid.org/0000-0001-8629-1918

Catalogue record

Date deposited: 30 Oct 2020 17:30
Last modified: 17 Mar 2024 03:36

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Contributors

Author: Thomas Zimmermann
Author: Federico Polidoro
Author: Federico di Leo
Author: Massimo Fedeli
Author: Joep Burger
Author: Jan van den Brakel
Author: Monica Pratesi
Author: Caterina Giusti
Author: Stefano Marchetti
Author: Luigi Biggeri
Author: Gaia Bertarelli
Author: Francesco Schirripa Spagnolo
Author: Tiziana Laureti
Author: Ilaria Benedetti
Author: Paul A. Smith ORCID iD
Author: James Dawber
Author: Jim O'Donoghue
Author: Tanya Flower
Author: Heledd Thomas
Author: Nora Würz
Author: Timo Schmid
Author: Charlotte Articus
Author: Jan Pablo Burgard
Author: Christopher Caratiola
Author: Hanna Dieckmann
Author: Florian Ertz
Author: Joscha Krause
Author: Ralf Münnich
Author: Anna-Lena Wölwer

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