<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.</p></abstract>

The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.

Small Area Model-Based Estimators Using Big Data Sources

Department of Economics and Management – University of Pisa, Via Ridolfi 10, 56124 Pisa, Italy

Department of Economics and Management - University of Pisa, Via Ridolfi 10, 56124 Pisa, Italy.

Department of Economics and Management - University of Pisa, Via Ridolfi 10, 56124 Pisa, Italy

KDD Lab – ISTI – National Research Council, Via G. Moruzzi 1, 56124 Pisa, Italy.

Department of Computer Science – University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy

KDD Lab - ISTI - National Research Council, Via G. Moruzzi 1, 56124 Pisa, Italy

Department of Information Engineering – University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy.

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Small Area Model-Based Estimators Using Big Data Sources"}

The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for...

Small Area Model-Based Estimators Using Big Data Sources

Published Online: Jun 27, 2015

Page range: 263 - 281

Received: Jul 01, 2013

Accepted: Feb 01, 2015

DOI: https://doi.org/10.1515/jos-2015-0017

Keywords
Social mining, auxiliary information, poverty measures

© by Stefano Marchetti

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Small Area Model-Based Estimators Using Big Data Sources

Published Online: Jun 27, 2015

Page range: 263 - 281

Received: Jul 01, 2013

Accepted: Feb 01, 2015

DOI: https://doi.org/10.1515/jos-2015-0017

KeywordsSocial mining, auxiliary information, poverty measures

© by Stefano Marchetti

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Social mining, auxiliary information, poverty measures