1
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Lakew N, Jonsson J, Lindner P. Towards an Active Role of Financial Institutions in Preventing Problem Gambling: A Proposed Conceptual Framework and Taxonomy of Financial Wellbeing Indicators. J Gambl Stud 2024:10.1007/s10899-024-10312-8. [PMID: 38767773 DOI: 10.1007/s10899-024-10312-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2024] [Indexed: 05/22/2024]
Abstract
The transformation of gambling into a largely digital commodity has created a need for online payment technologies to facilitate online gambling, thereby also raising the question of what role these actors can play in the promotion of Responsible Gambling (RG). With the means and access they maintain, financial institutions are in a unique position to alleviate financial pitfalls, yet their role in the gambling context has thus far received little scrutiny. The objective of this study was to conduct an extant literature review to develop an initial set of financial indicators tailored for financial institutions, enabling them to engage in the RG initiatives. We conducted a two-step narrative literature review to identify both general Financial Well-Being (FWB) indicators across financial research disciplines, and one specific to gambling. A literature search over the past 20 years was performed across the following academic databases: Medline (Ovid), Sociological Abstracts (ProQuest), Web of Science (Clarivate), and PsycInfo (EBSCO). Manifest content analysis was used in step one to review general financial well-being, yielding a general FWB conceptual framework. In step two, we applied latent content analysis to the gambling-specific literature, linking essential concepts of gambling-related financial harms to the broader FWB literature. This resulted in a tentative taxonomy of indicators applicable to financial institutions with gambling customers. In tandem with the FWB conceptual framework, the preliminary taxonomy could provide a foundation for financial institutions catering to gambling customers to engage in the duty of care agenda, potentially broadening player protection beyond the current operator-focused RG measures.
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Affiliation(s)
- Nathan Lakew
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
| | - Jakob Jonsson
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Philip Lindner
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
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2
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Berke A, Calacci D, Mahari R, Yabe T, Larson K, Pentland S. Open e-commerce 1.0, five years of crowdsourced U.S. Amazon purchase histories with user demographics. Sci Data 2024; 11:491. [PMID: 38740768 DOI: 10.1038/s41597-024-03329-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/29/2024] [Indexed: 05/16/2024] Open
Abstract
This is a first-of-its-kind dataset containing detailed purchase histories from 5027 U.S. Amazon.com consumers, spanning 2018 through 2022, with more than 1.8 million purchases. Consumer spending data are customarily collected through government surveys to produce public datasets and statistics, which serve public agencies and researchers. Companies now collect similar data through consumers' use of digital platforms at rates superseding data collection by public agencies. We published this dataset in an effort towards democratizing access to rich data sources routinely used by companies. The data were crowdsourced through an online survey and shared with participants' informed consent. Data columns include order date, product code, title, price, quantity, and shipping address state. Each purchase history is linked to survey data with information about participants' demographics, lifestyle, and health. We validate the dataset by showing expenditure correlates with public Amazon sales data (Pearson r = 0.978, p < 0.001) and conduct analyses of specific product categories, demonstrating expected seasonal trends and strong relationships to other public datasets.
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Affiliation(s)
- Alex Berke
- MIT Media Lab, Cambridge, MA, 02139, USA.
| | - Dan Calacci
- MIT Media Lab, Cambridge, MA, 02139, USA
- Princeton University, Princeton, NJ, 08544, USA
| | - Robert Mahari
- MIT Media Lab, Cambridge, MA, 02139, USA
- Harvard Law School, Cambridge, MA, 02138, USA
| | - Takahiro Yabe
- MIT Institute of Data, Systems, and Society (IDSS), Cambridge, MA, 02139, USA
- New York University Center for Urban Science and Progress, Brooklyn, NY, 11201, USA
| | | | - Sandy Pentland
- MIT Media Lab, Cambridge, MA, 02139, USA
- MIT Connection Science, Cambridge, MA, 02139, USA
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3
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Bahrami M, Boz HA, Suhara Y, Balcisoy S, Bozkaya B, Pentland A. Predicting merchant future performance using privacy-safe network-based features. Sci Rep 2023; 13:10073. [PMID: 37344502 DOI: 10.1038/s41598-023-36624-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 06/07/2023] [Indexed: 06/23/2023] Open
Abstract
Small and Medium-sized Enterprises play a significant role in most economies by contributing to job creation and economic growth. A majority of such merchants rely on business financing, and thus, financial institutions and investors need to assess their performance before making decisions on business loans. However, current methods of predicting merchants' future performance involve their private internal information, such as revenue and customer base, which cannot be shared without potentially exposing critical information. To address this problem, we first propose a novel approach to predicting merchants' future performance using credit card transaction data. Specifically, we construct a merchant network, regarding customers as bridges between merchants, and extract features from the constructed network structure for prediction purposes. Our study results demonstrate that the performance of machine learning models with features extracted from our proposed network is comparable to those with conventional revenue- and customer-based features, while maintaining higher privacy levels when shared with third-party organizations. Our approach offers a practical solution to privacy concerns over data and information required for merchants' performance prediction, enabling safe data-sharing among financial institutions and investors, helping them make more informed decisions on allocating their financial resources while ensuring that merchants' sensitive information is kept confidential.
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Affiliation(s)
- Mohsen Bahrami
- MIT Connection Science, Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Hasan Alp Boz
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Yoshihiko Suhara
- MIT Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Selim Balcisoy
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Burcin Bozkaya
- Sabanci Business School, Sabanci University, Istanbul, Turkey
| | - Alex Pentland
- MIT Connection Science, Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- MIT Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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4
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Luca M, Campedelli GM, Centellegher S, Tizzoni M, Lepri B. Crime, inequality and public health: a survey of emerging trends in urban data science. Front Big Data 2023; 6:1124526. [PMID: 37303974 PMCID: PMC10248183 DOI: 10.3389/fdata.2023.1124526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/10/2023] [Indexed: 06/13/2023] Open
Abstract
Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.
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Affiliation(s)
- Massimiliano Luca
- Mobile and Social Computing Lab, Bruno Kessler Foundation, Trento, Italy
- Faculty of Computer Science, Free University of Bolzano, Bolzano, Italy
| | | | | | - Michele Tizzoni
- Department of Sociology and Social Research, University of Trento, Trento, Italy
| | - Bruno Lepri
- Mobile and Social Computing Lab, Bruno Kessler Foundation, Trento, Italy
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5
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Suhara Y, Bahrami M, Bozkaya B, Pentland A'S. Validating Gravity-Based Market Share Models Using Large-Scale Transactional Data. BIG DATA 2021; 9:188-202. [PMID: 33739875 DOI: 10.1089/big.2020.0161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Customer patronage behavior has been widely studied in market share modeling contexts, which is an essential step toward estimating retail sales and finding new store locations in a competitive setting. Existing studies have conducted surveys to estimate merchants' market share and factors of attractiveness to use in various proposed mathematical models. Recent trends in Big Data analysis allow us to better understand human behavior and decision making, potentially leading to location models with more realistic assumptions. In this article, we propose a novel approach for validating the Huff gravity market share model, using a large-scale transactional dataset that describes customer patronage behavior at a regional level. Although the Huff model has been well studied and widely used in the context of sales estimation, competitive facility location, and demand allocation, this article is the first in validating the Huff model with a real dataset. Our approach helps to easily apply the model in different regions and with different merchant categories. Experimental results show that the Huff model fits well when modeling customer shopping behavior for a number of shopping categories, including grocery stores, clothing stores, gas stations, and restaurants. We also conduct regression analysis to show that certain features such as gender diversity and marital status diversity lead to stronger validation of the Huff model. We believe we provide strong evidence, with the help of real-world data, that gravity-based market share models are viable assumptions for retail sales estimation and competitive facility location models.
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Affiliation(s)
- Yoshihiko Suhara
- The Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Mohsen Bahrami
- The Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Sabanci Business School, Sabanci University, Istanbul, Turkey
| | - Burcin Bozkaya
- Sabanci Business School, Sabanci University, Istanbul, Turkey
- New College of Florida, Sarasota, Florida, USA
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6
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Abstract
Food safety continues to threaten public health. Machine learning holds potential in leveraging large, emerging data sets to improve the safety of the food supply and mitigate the impact of food safety incidents. Foodborne pathogen genomes and novel data streams, including text, transactional, and trade data, have seen emerging applications enabled by a machine learning approach, such as prediction of antibiotic resistance, source attribution of pathogens, and foodborne outbreak detection and risk assessment. In this article, we provide a gentle introduction to machine learning in the context of food safety and an overview of recent developments and applications. With many of these applications still in their nascence, general and domain-specific pitfalls and challenges associated with machine learning have begun to be recognized and addressed, which are critical to prospective use and future deployment of large data sets and their associated machine learning models for food safety applications.
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Affiliation(s)
- Xiangyu Deng
- Center for Food Safety, University of Georgia, Griffin, Georgia 30223, USA;
| | - Shuhao Cao
- Department of Mathematics and Statistics, Washington University, St. Louis, Missouri 63105, USA;
| | - Abigail L Horn
- Department of Preventive Medicine, University of Southern California, Los Angeles, California 90032, USA;
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7
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NADAL: A Neighbor-Aware Deep Learning Approach for Inferring Interpersonal Trust Using Smartphone Data. COMPUTERS 2020. [DOI: 10.3390/computers10010003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Interpersonal trust mediates multiple socio-technical systems and has implications for personal and societal well-being. Consequently, it is crucial to devise novel machine learning methods to infer interpersonal trust automatically using mobile sensor-based behavioral data. Considering that social relationships are often affected by neighboring relationships within the same network, this work proposes using a novel neighbor-aware deep learning architecture (NADAL) to enhance the inference of interpersonal trust scores. Based on analysis of call, SMS, and Bluetooth interaction data from a one-year field study involving 130 participants, we report that: (1) adding information about neighboring relationships improves trust score prediction in both shallow and deep learning approaches; and (2) a custom-designed neighbor-aware deep learning architecture outperforms a baseline feature concatenation based deep learning approach. The results obtained at interpersonal trust prediction are promising and have multiple implications for trust-aware applications in the emerging social internet of things.
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8
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Choi D, Shah C, Singh V. Investigating information seeking in physical and online environments with escape room and web search. J Inf Sci 2020. [DOI: 10.1177/0165551520972285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Searching and interacting with information is one of the most fundamental behaviours of human beings – something that takes place in both online and physical environments. Yet, most studies of information interaction have focused on only one of these sides. This work aims to connect them by investigating one’s information interaction behaviours in different physical and online contexts as well as different types of tasks. During Web search (online searching) and Escape Room (physical searching), 31 participants’ behavioural data during web search (online searching) and escape room (physical searching) were collected through eye-tracker, web browser logs, and wearable video recorder. Analysis of the behavioural data suggests that individuals have a preferred search strategy that they adopt across different tasks and environments. The behavioural pattern, however, was found to be affected by the task type (e.g. problem searching vs exploratory search) and the way information is structured within the environments.
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Affiliation(s)
- Dongho Choi
- Sony Interactive Entertainment, Cloud Gaming Engineering and Infrastructure (CGEI), Aliso Viejo, CA, USA
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9
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Xu Y, Belyi A, Santi P, Ratti C. Quantifying segregation in an integrated urban physical-social space. J R Soc Interface 2019; 16:20190536. [PMID: 31744420 DOI: 10.1098/rsif.2019.0536] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Our knowledge of how cities bring together different social classes is still limited. Much effort has been devoted to investigating residential segregation, mostly over well-defined social groups (e.g. race). Little is known of how mobility and human communications affect urban social integration. The dynamics of spatial and social-network segregation and individual variations along these two dimensions are largely untapped. In this article, we put forward a computational framework based on coupling large-scale information on human mobility, social-network connections and people's socio-economic status (SES), to provide a breakthrough in our understanding of the dynamics of spatio-temporal and social-network segregation in cities. Building on top of a social similarity measure, the framework can be used to depict segregation dynamics down to the individual level, and also provide aggregate measurements at the scale of places and cities, and their evolution over time. By applying the methodology in Singapore using large-scale mobile phone and socio-economic datasets, we find a relatively higher level of segregation among relatively wealthier classes, a finding that holds for both social and physical space. We also highlight the interplay between the effect of distance decay and homophily as forces that determine communication intensity, defining a notion of characteristic 'homophily distance' that can be used to measure social segregation across cities. The time-resolved analysis reveals the changing landscape of urban segregation and the time-varying roles of places. Segregations in physical and social space are weakly correlated at the individual level but highly correlated when grouped across at least hundreds of individuals. The methodology and analysis presented in this paper enable a deeper understanding of the dynamics of human segregation in social and physical space, which can assist social scientists, planners and city authorities in the design of more integrated cities.
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Affiliation(s)
- Yang Xu
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Alexander Belyi
- Singapore-MIT Alliance for Research and Technology, 1 Create Way, Singapore.,Faculty of Applied Mathematics and Computer Science, Belarusian State University, Minsk, Belarus
| | - Paolo Santi
- Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.,Istituto di Informatica e Telematica del CNR, Pisa, Italy
| | - Carlo Ratti
- Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
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10
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De Nadai M, Cardoso A, Lima A, Lepri B, Oliver N. Strategies and limitations in app usage and human mobility. Sci Rep 2019; 9:10935. [PMID: 31358830 PMCID: PMC6662905 DOI: 10.1038/s41598-019-47493-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 07/12/2019] [Indexed: 12/01/2022] Open
Abstract
Cognition has been found to constrain several aspects of human behaviour, such as the number of friends and the number of favourite places a person keeps stable over time. This limitation has been empirically defined in the physical and social spaces. But do people exhibit similar constraints in the digital space? We address this question through the analysis of pseudonymised mobility and mobile application (app) usage data of 400,000 individuals in a European country for six months. Despite the enormous heterogeneity of apps usage, we find that individuals exhibit a conserved capacity that limits the number of applications they regularly use. Moreover, we find that this capacity steadily decreases with age, as does the capacity in the physical space but with more complex dynamics. Even though people might have the same capacity, applications get added and removed over time. In this respect, we identify two profiles of individuals: app keepers and explorers, which differ in their stable (keepers) vs exploratory (explorers) behaviour regarding their use of mobile applications. Finally, we show that the capacity of applications predicts mobility capacity and vice-versa. By contrast, the behaviour of keepers and explorers may considerably vary across the two domains. Our empirical findings provide an intriguing picture linking human behaviour in the physical and digital worlds which bridges research studies from Computer Science, Social Physics and Computational Social Sciences.
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Affiliation(s)
- Marco De Nadai
- Vodafone Research, Paddington Central, London, W2 6BY, UK.
- Mobs Lab, Fondazione Bruno Kessler, Via Sommarive 18, 38123, Povo, TN, Italy.
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 9I, 38123, Povo, TN, Italy.
| | - Angelo Cardoso
- Vodafone Research, Paddington Central, London, W2 6BY, UK
| | - Antonio Lima
- Vodafone Research, Paddington Central, London, W2 6BY, UK
| | - Bruno Lepri
- Mobs Lab, Fondazione Bruno Kessler, Via Sommarive 18, 38123, Povo, TN, Italy
| | - Nuria Oliver
- Vodafone Research, Paddington Central, London, W2 6BY, UK
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11
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The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2018.10.004] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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12
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Padmaja B, Prasad VVR, Sunitha KVN, Reddy NCS, Anil CH. DetectStress: A Novel Stress Detection System Based on Smartphone and Wireless Physical Activity Tracker. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2019. [DOI: 10.1007/978-981-13-1580-0_7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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13
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Di Clemente R, Luengo-Oroz M, Travizano M, Xu S, Vaitla B, González MC. Sequences of purchases in credit card data reveal lifestyles in urban populations. Nat Commun 2018; 9:3330. [PMID: 30127416 PMCID: PMC6102281 DOI: 10.1038/s41467-018-05690-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 07/06/2018] [Indexed: 11/09/2022] Open
Abstract
Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics, and social sciences. In human activities, Zipf's law describes, for example, the frequency of appearance of words in a text or the purchase types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchase sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted from their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.
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Affiliation(s)
- Riccardo Di Clemente
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,The Bartlett Centre for Advanced Spatial Analysis, University College London, London, WC1E 6BT, UK
| | - Miguel Luengo-Oroz
- United Nations Global Pulse, 46th Street and 1st Avenue, New York, NY, 10017, USA
| | - Matias Travizano
- GranData, 550 15th Street Suite 36C, San Francisco, CA, 94103, USA
| | - Sharon Xu
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Bapu Vaitla
- Department of Environmental Health, Harvard University, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Marta C González
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. .,Department of City and Regional Planning, Berkeley, CA, 94720-1820, USA. .,Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720-1820, USA.
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14
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Urkup C, Bozkaya B, Salman FS. Customer mobility signatures and financial indicators as predictors in product recommendation. PLoS One 2018; 13:e0201197. [PMID: 30052681 PMCID: PMC6063431 DOI: 10.1371/journal.pone.0201197] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Accepted: 07/10/2018] [Indexed: 11/19/2022] Open
Abstract
The rapid growth of mobile payment and geo-aware systems as well as the resulting emergence of Big Data present opportunities to explore individual consuming patterns across space and time. Here we analyze a one-year transaction dataset of a leading commercial bank to understand to what extent customer mobility behavior and financial indicators can predict the use of a target product, namely the Individual Consumer Loan product. After data preprocessing, we generate 13 datasets covering different time intervals and feature groups, and test combinations of 3 feature selection methods and 10 classification algorithms to determine, for each dataset, the best feature selection method and the most influential features, and the best classification algorithm. We observe the importance of spatio-temporal mobility features and financial features, in addition to demography, in predicting the use of this exemplary product with high accuracy (AUC = 0.942). Finally, we analyze the classification results and report on most interesting customer characteristics and product usage implications. Our findings can be used to potentially increase the success rates of product recommendation systems.
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Affiliation(s)
- Cagan Urkup
- Department of Industrial Engineering, Koç University, Istanbul, Turkey
| | - Burcin Bozkaya
- School of Management, Sabancı University, Istanbul, Turkey
| | - F. Sibel Salman
- Department of Industrial Engineering, Koç University, Istanbul, Turkey
- * E-mail:
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15
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Dong X, Suhara Y, Bozkaya B, Singh VK, Lepri B, Pentland A‘S. Social Bridges in Urban Purchase Behavior. ACM T INTEL SYST TEC 2018. [DOI: 10.1145/3149409] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The understanding and modeling of human purchase behavior in city environment can have important implications in the study of urban economy and in the design and organization of cities. In this article, we study human purchase behavior at the community level and argue that people who live in different communities but work at close-by locations could act as “social bridges” between the respective communities and that they are correlated with similarity in community purchase behavior. We provide empirical evidence by studying millions of credit card transaction records for tens of thousands of individuals in a city environment during a period of three months. More specifically, we show that the number of social bridges between communities is a much stronger indicator of similarity in their purchase behavior than traditionally considered factors such as income and sociodemographic variables. Our findings also suggest that such an effect varies across different merchant categories, that the presence of female customers in social bridges is a stronger indicator compared to that of their male counterparts, and that there seems to be a geographical constraint for this effect, all of which may have implications in the studies of urban economy and data-driven urban planning.
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Affiliation(s)
- Xiaowen Dong
- Massachusetts Institute of Technology, Cambridge, MA, USA
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16
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Agarwal RR, Lin CC, Chen KT, Singh VK. Predicting financial trouble using call data-On social capital, phone logs, and financial trouble. PLoS One 2018; 13:e0191863. [PMID: 29474411 PMCID: PMC5825009 DOI: 10.1371/journal.pone.0191863] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 01/13/2018] [Indexed: 12/02/2022] Open
Abstract
An ability to understand and predict financial wellbeing for individuals is of interest to economists, policy designers, financial institutions, and the individuals themselves. According to the Nilson reports, there were more than 3 billion credit cards in use in 2013, accounting for purchases exceeding US$ 2.2 trillion, and according to the Federal Reserve report, 39% of American households were carrying credit card debt from month to month. Prior literature has connected individual financial wellbeing with social capital. However, as yet, there is limited empirical evidence connecting social interaction behavior with financial outcomes. This work reports results from one of the largest known studies connecting financial outcomes and phone-based social behavior (180,000 individuals; 2 years' time frame; 82.2 million monthly bills, and 350 million call logs). Our methodology tackles highly imbalanced dataset, which is a pertinent problem with modelling credit risk behavior, and offers a novel hybrid method that yields improvements over, both, a traditional transaction data only approach, and an approach that uses only call data. The results pave way for better financial modelling of billions of unbanked and underbanked customers using non-traditional metrics like phone-based credit scoring.
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Affiliation(s)
| | - Chia-Ching Lin
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Kuan-Ta Chen
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Vivek Kumar Singh
- School of Communication and Information, Rutgers University, New Brunswick, New Jersey, United States of America
- Media Labs, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
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17
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D’Silva K, Noulas A, Musolesi M, Mascolo C, Sklar M. Predicting the temporal activity patterns of new venues. EPJ DATA SCIENCE 2018; 7:13. [PMID: 31008012 PMCID: PMC6448359 DOI: 10.1140/epjds/s13688-018-0142-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 05/10/2018] [Indexed: 05/06/2023]
Abstract
Estimating revenue and business demand of a newly opened venue is paramount as these early stages often involve critical decisions such as first rounds of staffing and resource allocation. Traditionally, this estimation has been performed through coarse-grained measures such as observing numbers in local venues or venues at similar places (e.g., coffee shops around another station in the same city). The advent of crowdsourced data from devices and services carried by individuals on a daily basis has opened up the possibility of performing better predictions of temporal visitation patterns for locations and venues. In this paper, using mobility data from Foursquare, a location-centric platform, we treat venue categories as proxies for urban activities and analyze how they become popular over time. The main contribution of this work is a prediction framework able to use characteristic temporal signatures of places together with k-nearest neighbor metrics capturing similarities among urban regions, to forecast weekly popularity dynamics of a new venue establishment in a city neighborhood. We further show how we are able to forecast the popularity of the new venue after one month following its opening by using locality and temporal similarity as features. For the evaluation of our approach we focus on London. We show that temporally similar areas of the city can be successfully used as inputs of predictions of the visit patterns of new venues, with an improvement of 41% compared to a random selection of wards as a training set for the prediction task. We apply these concepts of temporally similar areas and locality to the real-time predictions related to new venues and show that these features can effectively be used to predict the future trends of a venue. Our findings have the potential to impact the design of location-based technologies and decisions made by new business owners.
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Affiliation(s)
- Krittika D’Silva
- Department of Computer Science, University of Cambridge, Cambridge, UK
| | | | - Mirco Musolesi
- Department of Geography, University College London, London, UK
- The Alan Turing Institute, London, UK
| | - Cecilia Mascolo
- Department of Computer Science, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
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