Socioeconomic characterization of regions through the lens of individual financial transactions.
People are increasingly leaving digital traces of their daily activities through interacting with their digital environment. Among these traces, financial transactions are of paramount interest since they provide a panoramic view of human life through the lens of purchases, from food and clothes to...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5708635?pdf=render |
| _version_ | 1818303888410804224 |
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| author | Behrooz Hashemian Emanuele Massaro Iva Bojic Juan Murillo Arias Stanislav Sobolevsky Carlo Ratti |
| author_facet | Behrooz Hashemian Emanuele Massaro Iva Bojic Juan Murillo Arias Stanislav Sobolevsky Carlo Ratti |
| author_sort | Behrooz Hashemian |
| collection | DOAJ |
| description | People are increasingly leaving digital traces of their daily activities through interacting with their digital environment. Among these traces, financial transactions are of paramount interest since they provide a panoramic view of human life through the lens of purchases, from food and clothes to sport and travel. Although many analyses have been done to study the individual preferences based on credit card transaction, characterizing human behavior at larger scales remains largely unexplored. This is mainly due to the lack of models that can relate individual transactions to macro-socioeconomic indicators. Building these models, not only can we obtain a nearly real-time information about socioeconomic characteristics of regions, usually available yearly or quarterly through official statistics, but also it can reveal hidden social and economic structures that cannot be captured by official indicators. In this paper, we aim to elucidate how macro-socioeconomic patterns could be understood based on individual financial decisions. To this end, we reveal the underlying interconnection of the network of spending leveraging anonymized individual credit/debit card transactions data, craft micro-socioeconomic indices that consists of various social and economic aspects of human life, and propose a machine learning framework to predict macro-socioeconomic indicators. |
| first_indexed | 2024-12-13T06:01:57Z |
| format | Article |
| id | doaj.art-3fd55d1c09fb41bf8be41cd1cc70b79b |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T06:01:57Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-3fd55d1c09fb41bf8be41cd1cc70b79b2022-12-21T23:57:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-011211e018703110.1371/journal.pone.0187031Socioeconomic characterization of regions through the lens of individual financial transactions.Behrooz HashemianEmanuele MassaroIva BojicJuan Murillo AriasStanislav SobolevskyCarlo RattiPeople are increasingly leaving digital traces of their daily activities through interacting with their digital environment. Among these traces, financial transactions are of paramount interest since they provide a panoramic view of human life through the lens of purchases, from food and clothes to sport and travel. Although many analyses have been done to study the individual preferences based on credit card transaction, characterizing human behavior at larger scales remains largely unexplored. This is mainly due to the lack of models that can relate individual transactions to macro-socioeconomic indicators. Building these models, not only can we obtain a nearly real-time information about socioeconomic characteristics of regions, usually available yearly or quarterly through official statistics, but also it can reveal hidden social and economic structures that cannot be captured by official indicators. In this paper, we aim to elucidate how macro-socioeconomic patterns could be understood based on individual financial decisions. To this end, we reveal the underlying interconnection of the network of spending leveraging anonymized individual credit/debit card transactions data, craft micro-socioeconomic indices that consists of various social and economic aspects of human life, and propose a machine learning framework to predict macro-socioeconomic indicators.http://europepmc.org/articles/PMC5708635?pdf=render |
| spellingShingle | Behrooz Hashemian Emanuele Massaro Iva Bojic Juan Murillo Arias Stanislav Sobolevsky Carlo Ratti Socioeconomic characterization of regions through the lens of individual financial transactions. PLoS ONE |
| title | Socioeconomic characterization of regions through the lens of individual financial transactions. |
| title_full | Socioeconomic characterization of regions through the lens of individual financial transactions. |
| title_fullStr | Socioeconomic characterization of regions through the lens of individual financial transactions. |
| title_full_unstemmed | Socioeconomic characterization of regions through the lens of individual financial transactions. |
| title_short | Socioeconomic characterization of regions through the lens of individual financial transactions. |
| title_sort | socioeconomic characterization of regions through the lens of individual financial transactions |
| url | http://europepmc.org/articles/PMC5708635?pdf=render |
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