Socioeconomic characterization of regions through the lens of individual financial transactions
Name
journal.pone.0187031.pdf
Size
3.04 MB
Format
Adobe PDF
Checksum (MD5)
3f6688d9f198f7a01ca1d8f2acd51e94
Author(s)
Hashemian, Behrooz
Massaro, Emanuele
Bojic, Iva
Murillo Arias, Juan
Sobolevsky, Stanislav
Ratti, Carlo
Date Issued
November 2017
Journal
PLOS ONE
Publisher
Public Library of Science
Citation
Hashemian, Behrooz et al. “Socioeconomic Characterization of Regions through the Lens of Individual Financial Transactions.” Edited by Renaud Lambiotte. PLOS ONE 12, 11 (November 2017): e0187031 © 2017 Hashemian et al
Version
Final published version
Abstract
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pone.0187031
