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Extracting urban patterns from location-based social networks

Authors:

Franco ZambonelliAuthors Info & Claims

LBSN '11: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks

Pages 9 - 16

https://doi.org/10.1145/2063212.2063226

Published: 01 November 2011 Publication History

Abstract

Social networks attract lots of new users every day and absorb from them information about events and facts happening in the real world. The exploitation of this information can help identifying mobility patterns that occur in an urban environment as well as produce services to take advantage of social commonalities between people. In this paper we set out to address the problem of extracting urban patterns from fragments of multiple and sparse people life traces, as they emerge from the participation to social network. To investigate this challenging task, we analyzed 13 millions Twitter posts (3 GB) of data in New York. Then we test upon this data a probabilistic topic models approach to automatically extract urban patterns from location-based social network data. We find that the extracted patterns can identify hotspots in the city, and recognize a number of major crowd behaviors that recur over time and space in the urban scenario.

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Cited By

Xie YQiu TXiong YHuang XGao XChen CWang QLi HBaeza-Yates RBonchi F(2024)Weather Knows What Will Occur: Urban Public Nuisance Events Prediction and Control with Meteorological AssistanceProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671639(6037-6048)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671639
Tian ZYang FQin D(2023)An Improved New YOLOv7 Algorithm for Detecting Building Air Conditioner External Units from Street View ImagesSensors10.3390/s2322911823:22(9118)Online publication date: 11-Nov-2023
https://doi.org/10.3390/s23229118
Katsikouli PMadariaga DViana ATarable AFiore M(2023)DuctiLoc: Energy-Efficient Location Sampling With Configurable AccuracyIEEE Access10.1109/ACCESS.2023.324373111(15375-15389)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3243731
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Extracting urban patterns from location-based social networks

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Published In

cover image ACM Conferences

LBSN '11: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks

November 2011

103 pages

ISBN:9781450310338

DOI:10.1145/2063212

General Chairs:
Christian S. Jensen
Aarhus University, Denmark
,
Wang-Chien Lee
Pennsylvania State University
,
Program Chairs:
Yu Zheng
Microsoft Research Asia, China
,
Mohamed F. Mokbel
University of Minnesota

Copyright © 2011 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2011

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Research-article

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SAPERE (Self-Aware Pervasive Service Ecosystems)

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GIS '11

Sponsor:

SIGSPATIAL

GIS '11: 19th SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 1, 2011

Illinois, Chicago

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Overall Acceptance Rate 8 of 15 submissions, 53%

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Cited By

Xie YQiu TXiong YHuang XGao XChen CWang QLi HBaeza-Yates RBonchi F(2024)Weather Knows What Will Occur: Urban Public Nuisance Events Prediction and Control with Meteorological AssistanceProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671639(6037-6048)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671639
Tian ZYang FQin D(2023)An Improved New YOLOv7 Algorithm for Detecting Building Air Conditioner External Units from Street View ImagesSensors10.3390/s2322911823:22(9118)Online publication date: 11-Nov-2023
https://doi.org/10.3390/s23229118
Katsikouli PMadariaga DViana ATarable AFiore M(2023)DuctiLoc: Energy-Efficient Location Sampling With Configurable AccuracyIEEE Access10.1109/ACCESS.2023.324373111(15375-15389)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3243731
Huang LXia FChen HHu BZhou XLi CJin YXu Y(2023)Reconstructing human activities via coupling mobile phone data with location-based social networksTravel Behaviour and Society10.1016/j.tbs.2023.10060633(100606)Online publication date: Oct-2023
https://doi.org/10.1016/j.tbs.2023.100606
Majeed AKhan SHwang S(2022)A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social NetworksElectronics10.3390/electronics1113193111:13(1931)Online publication date: 21-Jun-2022
https://doi.org/10.3390/electronics11131931
Hasenburg JBermbach D(2022)GeoBrokerComputer Communications10.1016/j.comcom.2020.01.015151:C(473-484)Online publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.comcom.2020.01.015
Kravi EKanza YKimelfeld BReichart R(2021)Location Classification Based on TweetsProceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery10.1145/3486635.3491075(51-60)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3486635.3491075
Banerjee SKrebs CBisgin NBisgin HMani M(2021)Predicting customer poachability from locomotion intelligenceProceedings of the 5th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising10.1145/3486183.3490998(1-4)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3486183.3490998
Belcastro LMarozzo FPerrella E(2021)Automatic detection of user trajectories from social media postsExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115733186:COnline publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.115733
Iranmanesh ACömert NHoşkara Ş(2021)Reading urban land use through spatio-temporal and content analysis of geotagged Twitter dataGeoJournal10.1007/s10708-021-10391-987:4(2593-2610)Online publication date: 18-Feb-2021
https://doi.org/10.1007/s10708-021-10391-9
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