research-article

Discovering New Socio-demographic Regional Patterns in Cities

Authors:

Declan O'SullivanAuthors Info & Claims

LBSN16: Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-based Social Networks

Article No.: 1, Pages 1 - 9

https://doi.org/10.1145/3021304.3021305

Published: 31 October 2016 Publication History

Abstract

During the past few years, the analysis of data generated from Location-Based Social Networks (LBSNs) have aided in the identification of urban patterns, understanding activity behaviours in urban areas, as well as producing novel recommender systems that facilitate users' choices. However, the recent advancement in machine learning techniques promises new deeper insights with the possibility of finding new spatio-temporal patterns in cities. In this paper, we show that one of the recent advancements in machine learning, Deep Belief Networks (DBNs), can discover a new type of pattern, which we refer to in the paper as the Socio-demographic Regional Pattern. This pattern illustrates the ability of predicting the district of a city given a set of weekly activities captured from LBSNs. Specifically, we have found instances of this embedded pattern for the boroughs in New York City by training a DBN model that can classify with nearly 70% accuracy the location of weekly region-footprints. We further validated the existence and complexity of this type of pattern by applying a probabilistic topic model, namely Latent Dirichlet Allocation (LDA). We believe that this research can yield to a deeper understanding about social commonalities and the geographical evolution of different regions and areas, between cities across the globe.

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Cited By

Kulkarni DVenkatesan MKulkarni A(2023)Traffic Prediction with Network Slicing in 5G: A Survey2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)10.1109/ICAIS56108.2023.10073876(1360-1365)Online publication date: 2-Feb-2023
https://doi.org/10.1109/ICAIS56108.2023.10073876
Abbasi MShahraki ATaherkordi A(2021)Deep learning for Network Traffic Monitoring and Analysis (NTMA): A surveyComputer Communications10.1016/j.comcom.2021.01.021Online publication date: Feb-2021
https://doi.org/10.1016/j.comcom.2021.01.021
Anwar TLiao KGoyal ASellis TKayes AShen H(2020)Inferring Location Types With Geo-Social-Temporal Pattern MiningIEEE Access10.1109/ACCESS.2020.30189978(154789-154799)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3018997
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cover image ACM Conferences

LBSN16: Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-based Social Networks

October 2016

42 pages

ISBN:9781450345866

DOI:10.1145/3021304

Conference Chair:
Mohamed Sarwat
Arizona State University

Copyright © 2016 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 ACM 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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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Published: 31 October 2016

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SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 31 - November 3, 2016

CA, Burlingame, USA

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Cited By

Kulkarni DVenkatesan MKulkarni A(2023)Traffic Prediction with Network Slicing in 5G: A Survey2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)10.1109/ICAIS56108.2023.10073876(1360-1365)Online publication date: 2-Feb-2023
https://doi.org/10.1109/ICAIS56108.2023.10073876
Abbasi MShahraki ATaherkordi A(2021)Deep learning for Network Traffic Monitoring and Analysis (NTMA): A surveyComputer Communications10.1016/j.comcom.2021.01.021Online publication date: Feb-2021
https://doi.org/10.1016/j.comcom.2021.01.021
Anwar TLiao KGoyal ASellis TKayes AShen H(2020)Inferring Location Types With Geo-Social-Temporal Pattern MiningIEEE Access10.1109/ACCESS.2020.30189978(154789-154799)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3018997
Lima STeran L(2019)Cognitive Smart Cities and Deep Learning: A Classification Framework2019 Sixth International Conference on eDemocracy & eGovernment (ICEDEG)10.1109/ICEDEG.2019.8734346(180-187)Online publication date: Apr-2019
https://doi.org/10.1109/ICEDEG.2019.8734346
Assem HCaglayan BBuda TO’Sullivan D(2019)ST-DenNetFus: A New Deep Learning Approach for Network Demand PredictionMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-10997-4_14(222-237)Online publication date: 18-Jan-2019
https://doi.org/10.1007/978-3-030-10997-4_14
Doytsher YGalon BKanza Y(2017)Emotion Maps based on Geotagged Posts in the Social MediaProceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities10.1145/3149858.3149862(39-46)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3149858.3149862
Assem HBuda TO’sullivan D(2017)RCMCACM Transactions on Intelligent Systems and Technology10.1145/30866368:5(1-30)Online publication date: 12-Aug-2017
https://dl.acm.org/doi/10.1145/3086636

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