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Understanding Heavy Drinking at Night through Smartphone Sensing and Active Human Engagement

Authors:

Thanh-Trung Phan,

Florian Labhart,

Skanda Muralidhar,

Daniel Gatica-PerezAuthors Info & Claims

PervasiveHealth '20: Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare

Pages 211 - 222

https://doi.org/10.1145/3421937.3421992

Published: 02 February 2021 Publication History

Abstract

Heavy alcohol consumption can lead to many severe consequences. In this paper, we study the phenomenon of heavy drinking at night (4+ drinks for women or 5+ for men on a single evening), using a smartphone sensing dataset depicting about nightlife and drinking behaviors for 240 young adult participants. Our work has three contributions. First, we segment nights into moving and static episodes as anchors to aggregate mobile sensing features. Second, we show that young adults tend to be more mobile, have more activities, and attend more crowded areas outside home on heavy drinking nights compared to other nights. Third, we develop a machine learning framework to classify a given weekend night as involving heavy or non-heavy drinking, comparing automatically captured sensor features versus manually contributed contextual cues and images provided over the course of the night. Results show that a fully automatic approach with phone sensors results in an accuracy of 71%. In contrast, manual input of context of drinking events results in an accuracy of 70%; and visual features of manually contributed images produce an accuracy of 72%. This suggests that automatic sensing is a competitive approach.

References

[1]

2019. Machine Learning in Python. Retrieved October 28, 2019 from https://scikit-learn.org/stable/

[2]

2020. Centers for Disease Control and Prevention. https://www.cdc.gov/alcohol/faqs.htm Accessed: 2020-01-13.

[3]

Ian Anderson, Julie Maitland, Scott Sherwood, Louise Barkhuus, Matthew Chalmers, Malcolm Hall, Barry Brown, and Henk Muller. 2007. Shakra: tracking and sharing daily activity levels with unaugmented mobile phones. Mobile networks and applications 12, 2-3 (2007), 185--199.

[4]

Zachary Arnold, Danielle Larose, and Emmanuel Agu. 2015. Smartphone inference of alcohol consumption levels from gait. In Healthcare Informatics (ICHI), 2015 International Conference on. IEEE, 417--426.

Digital Library

[5]

Sangwon Bae, Tammy Chung, Denzil Ferreira, Anind K Dey, and Brian Suffoletto. 2018. Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions. Addictive behaviors 83 (2018), 42--47.

[6]

Sangwon Bae, Denzil Ferreira, Brian Suffoletto, Juan C Puyana, Ryan Kurtz, Tammy Chung, and Anind K Dey. 2017. Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 2 (2017), 5.

Digital Library

[7]

Ling Bao and Stephen S Intille. 2004. Activity recognition from user-annotated acceleration data. In International conference on pervasive computing. Springer, 1--17.

[8]

Luke Bermingham and Ickjai Lee. 2019. Mining place-matching patterns from spatio-temporal trajectories using complex real-world places. Expert Systems with Applications 122 (2019), 334--350.

[9]

Tomas Brezmes, Juan-Luis Gorricho, and Josep Cotrina. 2009. Activity recognition from accelerometer data on a mobile phone. In International Work-Conference on Artificial Neural Networks. Springer, 796--799.

[10]

Ashlee C Carter, Karen Obremski Brandon, and Mark S Goldman. 2010. The college and noncollege experience: a review of the factors that influence drinking behavior in young adulthood. Journal of studies on alcohol and drugs 71, 5 (2010), 742--750.

[11]

John Chon and Hojung Cha. 2011. Lifemap: A smartphone-based context provider for location-based services. IEEE Pervasive Computing 10, 2 (2011), 58--67.

Digital Library

[12]

Matthew A Christensen, Laura Bettencourt, Leanne Kaye, Sai T Moturu, Kaylin T Nguyen, Jeffrey E Olgin, Mark J Pletcher, and Gregory M Marcus. 2016. Direct measurements of smartphone screen-time: relationships with demographics and sleep. PloS one 11, 11 (2016), e0165331.

[13]

John D Clapp, Danielle R Madden, Douglas D Mooney, and Kristin E Dahlquist. 2017. Examining the social ecology of a bar-crawl: An exploratory pilot study. PLoS one 12, 9 (2017), e0185238.

[14]

Jacob Cohen. 2013. Statistical power analysis for the behavioral sciences. Routledge.

[15]

Kelly E Courtney and John Polich. 2009. Binge drinking in young adults: Data, definitions, and determinants. Psychological bulletin 135, 1 (2009), 142.

[16]

Ashlee Curtis, Nicolas Droste, Kerri Coomber, Belinda Guadagno, Richelle Mayshak, Shannon Hyder, Alexa Hayley, and Peter Miller. 2019. Off the rails---Evaluating the nightlife impact of Melbourne, Australia's 24-h public transport trial. International Journal of Drug Policy 63 (2019), 39--46.

[17]

Paul M Dietze, Michael Livingston, Sarah Callinan, and Robin Room. 2014. The big night out: What happens on the most recent heavy drinking occasion among young V ictorian risky drinkers? Drug and alcohol review 33, 4 (2014), 346--353.

[18]

Trinh Minh Tri Do, Jan Blom, and Daniel Gatica-Perez. 2011. Smartphone usage in the wild: a large-scale analysis of applications and context. In Proceedings of the 13th international conference on multimodal interfaces. ACM, 353--360.

[19]

Trinh Minh Tri Do and Daniel Gatica-Perez. 2011. Groupus: Smartphone proximity data and human interaction type mining. In 2011 15th Annual International Symposium on Wearable Computers. IEEE, 21--28.

[20]

Trinh Minh Tri Do and Daniel Gatica-Perez. 2013. The places of our lives: Visiting patterns and automatic labeling from longitudinal smartphone data. IEEE Transactions on Mobile Computing 13, 3 (2013), 638--648.

[21]

Nathan Eagle and Alex Pentland. 2005. Social serendipity: Mobilizing social software. IEEE Pervasive Computing 4, 2 (2005), 28--34.

Digital Library

[22]

Gerhard Gmel, Jacques Gaume, Mohamed Faouzi, Jean-Pierre Kulling, and Jean-Bernard Daeppen. 2008. Who drinks most of the total alcohol in young men---risky single occasion drinking as normative behaviour. Alcohol & Alcoholism 43, 6 (2008), 692--697.

[23]

Gerhard Gmel and Jürgen Rehm. 2004. Measuring alcohol consumption. Contemporary Drug Problems 31, 3 (2004), 467--540.

[24]

Gerhard Gmel, Jürgen Rehm, Emmanuel Kuntsche, et al. 2003. Binge drinking in Europe: definitions, epidemiology, and consequences. Sucht 49, 2 (2003), 105--116.

[25]

Karen Hughes, Zara Anderson, Michela Morleo, and Mark A Bellis. 2008. Alcohol, nightlife and violence: the relative contributions of drinking before and during nights out to negative health and criminal justice outcomes. Addiction 103, 1 (2008), 60--65.

[26]

Mark Jayne, Sarah L Holloway, and Gill Valentine. 2006. Drunk and disorderly: alcohol, urban life and public space. Progress in human geography 30, 4 (2006), 451--468.

[27]

Dean M Karantonis, Michael R Narayanan, Merryn Mathie, Nigel H Lovell, and Branko G Celler. 2006. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE transactions on information technology in biomedicine 10, 1 (2006), 156--167.

Digital Library

[28]

Donna M Kazemi, Brian Borsari, Maureen J Levine, Shaoyu Li, Katie A Lamberson, and Laura A Matta. 2017. A systematic review of the mHealth interventions to prevent alcohol and substance abuse. Journal of health communication 22, 5 (2017), 413--432.

[29]

Daniel Kelly, Barry Smyth, and Brian Caulfield. 2013. Uncovering measurements of social and demographic behavior from smartphone location data. IEEE Transactions on Human-Machine Systems 43, 2 (2013), 188--198.

[30]

Mikkel Baun Kjærgaard and Petteri Nurmi. 2012. Challenges for social sensing using wifi signals. In Proceedings of the 1st ACM workshop on Mobile systems for computational social science. ACM, 17--21.

Digital Library

[31]

Emmanuel Kuntsche, Ronald Knibbe, Gerhard Gmel, and Rutger Engels. 2005. Why do young people drink? A review of drinking motives. Clinical psychology review 25, 7 (2005), 841--861.

[32]

Emmanuel Kuntsche, Sandra Kuntsche, Johannes Thrul, and Gerhard Gmel. 2017. Binge drinking: Health impact, prevalence, correlates and interventions. Psychology & health 32, 8 (2017), 976--1017.

[33]

Emmanuel Kuntsche and Florian Labhart. 2012. Investigating the drinking patterns of young people over the course of the evening at weekends. Drug and alcohol dependence 124, 3 (2012), 319--324.

[34]

Emmanuel Kuntsche, Roy Otten, and Florian Labhart. 2015. Identifying risky drinking patterns over the course of Saturday evenings: An event-level study. Psychology of addictive behaviors 29, 3 (2015), 744.

[35]

Florian Labhart, Rutger Engels, and Emmanuel Kuntsche. 2018. What reminds young people that they drank more than intended on weekend nights: an event-level study. Journal of studies on alcohol and drugs 79, 4 (2018), 644--648.

[36]

Florian Labhart, Kathryn Graham, Samantha Wells, and Emmanuel Kuntsche. 2013. Drinking before going to licensed premises: An event-level analysis of predrinking, alcohol consumption, and adverse outcomes. Alcoholism: Clinical and Experimental Research 37, 2 (2013), 284--291.

[37]

Florian Labhart, Michael Livingston, Rutger Engels, and Emmanuel Kuntsche. 2018. After how many drinks does someone experience acute consequences---determining thresholds for binge drinking based on two event-level studies. Addiction 113, 12 (2018), 2235--2244.

[38]

Florian Labhart, Darshan Santani, Jasmine Truong, Flavio Tarsetti, Olivier Bornet, Sara Landolt, Daniel Gatica-Perez, and Emmanuel Kuntsche. 2017. Development of the Geographical Proportional-to-size Street-Intercept Sampling (GPSIS) method for recruiting urban nightlife-goers in an entire city. International Journal of Social Research Methodology 20, 6 (2017), 721--736.

[39]

Florian Labhart, Flavio Tarsetti, Olivier Bornet, Darshan Santani, Jasmine Truong, Sara Landolt, Daniel Gatica-Perez, and Emmanuel Kuntsche. 2019. Capturing drinking and nightlife behaviours and their social and physical context with a smartphone application-investigation of users' experience and reactivity. Addiction Research & Theory (2019), 1--14.

[40]

Florian Labhart, Samantha Wells, Kathryn Graham, and Emmanuel Kuntsche. 2014. Do individual and situational factors explain the link between predrinking and heavier alcohol consumption? An event-level study of types of beverage consumed and social context. Alcohol and Alcoholism 49, 3 (2014), 327--335.

[41]

Dong Kyu Lee. 2016. Alternatives to P value: confidence interval and effect size. Korean journal of anesthesiology 69, 6 (2016), 555--562.

[42]

Robert LiKamWa, Yunxin Liu, Nicholas D Lane, and Lin Zhong. 2013. Moodscope: Building a mood sensor from smartphone usage patterns. In Proceeding of the 11th annual international conference on Mobile systems, applications, and services. ACM, 389--402.

[43]

MJ Mathie, Branko G Celler, Nigel H Lovell, and ACF Coster. 2004. Classification of basic daily movements using a triaxial accelerometer. Medical and Biological Engineering and Computing 42, 5 (2004), 679--687.

[44]

Merryn J Mathie, Adelle CF Coster, Nigel H Lovell, Branko G Celler, Stephen R Lord, and Anne Tiedemann. 2004. A pilot study of long-term monitoring of human movements in the home using accelerometry. Journal of telemedicine and telecare 10, 3 (2004), 144--151.

[45]

Uwe Maurer, Asim Smailagic, Daniel P Siewiorek, and Michael Deisher. 2006. Activity recognition and monitoring using multiple sensors on different body positions. Technical Report. CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE.

[46]

Dennis McCarty. 1985. Environmental factors in substance abuse. In Determinants of substance abuse. Springer, 247--281.

[47]

Jun-Ki Min, Afsaneh Doryab, Jason Wiese, Shahriyar Amini, John Zimmerman, and Jason I Hong. 2014. Toss'n'turn: smartphone as sleep and sleep quality detector. In Proceedings of the SIGCHI conference on human factors in computing systems. ACM, 477--486.

Digital Library

[48]

Raul Montoliu, Jan Blom, and Daniel Gatica-Perez. 2013. Discovering places of interest in everyday life from smartphone data. Multimedia tools and applications 62, 1 (2013), 179--207.

[49]

Andrei Papliatseyeu and Oscar Mayora. 2009. Mobile habits: Inferring and predicting user activities with a location-aware smartphone. In 3rd Symposium of Ubiquitous Computing and Ambient Intelligence 2008. Springer, 343--352.

[50]

Thanh-Trung Phan, Skanda Muralidhar, and Daniel Gatica-Perez. 2019. # Drink Or# Drunk: Multimodal Signals and Drinking Practices on Instagram. In Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare. ACM, 71--80.

Digital Library

[51]

Thanh-Trung Phan, Skanda Muralidhar, and Daniel Gatica-Perez. 2019. Drinks & Crowds: Characterizing Alcohol Consumption through Crowdsensing and Social Media. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 2 (2019), 59.

Digital Library

[52]

Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, and Michael L Littman. 2005. Activity recognition from accelerometer data. In Aaai, Vol. 5. 1541--1546.

Digital Library

[53]

Damaris J Rohsenow. 1982. Social anxiety, daily moods, and alcohol use over time among heavy social drinking men. Addictive Behaviors 7, 3 (1982), 311--315.

[54]

Darshan Santani, Joan-Isaac Biel, Florian Labhart, Jasmine Truong, Sara Landolt, Emmanuel Kuntsche, and Daniel Gatica-Perez. 2016. The night is young: urban crowdsourcing of nightlife patterns. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 427--438.

Digital Library

[55]

Darshan Santani, Florian Labhart, Sara Landolt, Emmanuel Kuntsche, Daniel Gatica-Perez, et al. 2018. DrinkSense: Characterizing youth drinking behavior using smartphones. IEEE Transactions on Mobile Computing 17, 10 (2018), 2279--2292.

[56]

Piotr Sapiezynski, Arkadiusz Stopczynski, Radu Gatej, and Sune Lehmann. 2015. Tracking human mobility using wifi signals. PloS one 10, 7 (2015), e0130824.

[57]

Piotr Sapiezynski, Arkadiusz Stopczynski, David Kofoed Wind, Jure Leskovec, and Sune Lehmann. 2017. Inferring person-to-person proximity using WiFi signals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 2 (2017), 24.

Digital Library

[58]

Oliver Stanesby, Florian Labhart, Paul Dietze, Cassandra JC Wright, and Emmanuel Kuntsche. 2019. The contexts of heavy drinking: A systematic review of the combinations of context-related factors associated with heavy drinking occasions. PloS one 14, 7 (2019).

[59]

Abigail K Stevely, John Holmes, and Petra S Meier. 2019. Contextual characteristics of adults' drinking occasions and their association with levels of alcohol consumption and acute alcohol-related harm: A mapping review. Addiction (2019).

[60]

Xing Su, Hanghang Tong, and Ping Ji. 2014. Activity recognition with smartphone sensors. Tsinghua science and technology 19, 3 (2014), 235--249.

[61]

Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2818--2826.

[62]

Johannes Thrul and Emmanuel Kuntsche. 2015. The impact of friends on young adults' drinking over the course of the evening---an event-level analysis. Addiction 110, 4 (2015), 619--626.

[63]

Julia M Townshend and Theodora Duka. 2005. Binge drinking, cognitive performance and mood in a population of young social drinkers. Alcoholism: Clinical and Experimental Research 29, 3 (2005), 317--325.

[64]

Ilse Van Liempt, Irina Van Aalst, and Tim Schwanen. 2015. Introduction: Geographies of the urban night.

[65]

Peter H Veltink, HansB J Bussmann, Wiebe De Vries, WimL J Martens, Rob C Van Lummel, et al. 1996. Detection of static and dynamic activities using uniaxial accelerometers. IEEE Transactions on Rehabilitation Engineering 4, 4 (1996), 375--385.

[66]

Guillem Vich, Oriol Marquet, and Carme Miralles-Guasch. 2017. Suburban commuting and activity spaces: using smartphone tracking data to understand the spatial extent of travel behaviour. The Geographical Journal 183, 4 (2017), 426--439.

[67]

Jessica Weafer and Mark T Fillmore. 2013. Acute alcohol effects on attentional bias in heavy and moderate drinkers. Psychology of addictive behaviors 27, 1 (2013), 32.

[68]

Henry Wechsler and Toben F Nelson. 2001. Binge drinking and the American college students: What's five drinks? Psychology of Addictive Behaviors 15, 4 (2001), 287.

[69]

Cassandra Wright, Paul M Dietze, Paul A Agius, Emmanuel Kuntsche, Michael Livingston, Oliver C Black, Robin Room, Margaret Hellard, and Megan SC Lim. 2018. Mobile phone-based ecological momentary intervention to reduce young adults' alcohol use in the event: a three-armed randomized controlled trial. JMIR mHealth and uHealth 6, 7 (2018), e149.

[70]

Qiang Xu, Jeffrey Erman, Alexandre Gerber, Zhuoqing Mao, Jeffrey Pang, and Shobha Venkataraman. 2011. Identifying diverse usage behaviors of smartphone apps. In Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference. ACM, 329--344.

Digital Library

[71]

Zhixian Yan, Jun Yang, and Emmanuel Munguia Tapia. 2013. Smartphone bluetooth based social sensing. In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication. ACM, 95--98.

Digital Library

[72]

Koji Yatani. 2016. Effect sizes and power analysis in hci. In Modern Statistical Methods for HCI. Springer, 87--110.

[73]

Chuang-wen You, Kuo-Cheng Wang, Ming-Chyi Huang, Yen-Chang Chen, Cheng-Lin Lin, Po-Shiun Ho, Hao-Chuan Wang, Polly Huang, and Hao-Hua Chu. 2015. SoberDiary: A Phone-based Support System for Assisting Recovery from Alcohol Dependence. In Proceedings of CHI (CHI '15). ACM, New York, NY, USA, 3839--3848.

[74]

Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba. 2018. Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence 40, 6 (2018), 1452--1464.

Cited By

Lee HKim ABae SLee U(2024)S-ADL: Exploring Smartphone-based Activities of Daily Living to Detect Blood Alcohol Concentration in a Controlled EnvironmentProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642832(1-25)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642832
Jung YJung GJeong SKim CWoo WHong HLee U(2023)"Enjoy, but Moderately!": Designing a Social Companion Robot for Social Engagement and Behavior Moderation in Solitary Drinking ContextProceedings of the ACM on Human-Computer Interaction10.1145/36100287:CSCW2(1-24)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610028
Assi KMeegahapola LDroz WKun PDe Götzen ABidoglia MStares SGaskell GChagnaa AGanbold AZundui TCaprini CMiorandi DZarza JHume ACernuzzi LBison IRodas Britez MBusso MChenu-Abente RGiunchiglia FGatica-Perez D(2023)Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UKProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581190(1-23)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581190
Show More Cited By

Index Terms

Understanding Heavy Drinking at Night through Smartphone Sensing and Active Human Engagement
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing design and evaluation methods

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PervasiveHealth '20: Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare

May 2020

446 pages

ISBN:9781450375320

DOI:10.1145/3421937

Editors:
Sean A. Munson
Human Centered Design & Engineering, University of Washington
,
Stephen M. Schueller
University of California, Irvine
,
General Chair:
Rosa Arriaga
Georgia Institute of Technology, USA

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 02 February 2021

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PervasiveHealth '20

PervasiveHealth '20: 14th EAI International Conference on Pervasive Computing Technologies for Healthcare

May 18 - 20, 2020

GA, Atlanta, USA

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Cited By

Lee HKim ABae SLee U(2024)S-ADL: Exploring Smartphone-based Activities of Daily Living to Detect Blood Alcohol Concentration in a Controlled EnvironmentProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642832(1-25)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642832
Jung YJung GJeong SKim CWoo WHong HLee U(2023)"Enjoy, but Moderately!": Designing a Social Companion Robot for Social Engagement and Behavior Moderation in Solitary Drinking ContextProceedings of the ACM on Human-Computer Interaction10.1145/36100287:CSCW2(1-24)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610028
Assi KMeegahapola LDroz WKun PDe Götzen ABidoglia MStares SGaskell GChagnaa AGanbold AZundui TCaprini CMiorandi DZarza JHume ACernuzzi LBison IRodas Britez MBusso MChenu-Abente RGiunchiglia FGatica-Perez D(2023)Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UKProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581190(1-23)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581190
Meegahapola LLabhart FPhan TGatica-Perez D(2021)Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34781265:3(1-26)Online publication date: 14-Sep-2021
https://dl.acm.org/doi/10.1145/3478126
Pozo APhan TGatica-Perez D(2020)Learning Urban Nightlife Routines from Mobile DataProceedings of the 19th International Conference on Mobile and Ubiquitous Multimedia10.1145/3428361.3428396(107-118)Online publication date: 22-Nov-2020
https://dl.acm.org/doi/10.1145/3428361.3428396

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