Cited By
View all- Ahlers DWilde ESchifanella RAlowibdi J(2021)Report on the tenth international workshop on location and the web (LocWeb 2020)ACM SIGIR Forum10.1145/3451964.345197254:1(1-8)Online publication date: 19-Feb-2021
Toward Characterizing Cities with Social Media Images Using Activity Recognition, Topic Modeling and Visualization
Pages 688 - 693
Abstract
Images are at the heart of the digital life, registering what, where, and when people do activities. This rich visual information may allow to characterize cities at spatial granularities unseen with traditional methods. To advance in that direction, here we propose a methodology to explore the activities held in cities, by means of activity recognition (what happens in images), topic modeling (what activities are relevant), and visualization (how to choose the number of topics, interpret them, and where do they appear in the city). We apply this methodology to Santiago, Chile, using images from Flickr, and find promising results for planners and urbanites.
References
[1]
Azam R Bahrehdar and Ross S Purves. 2018. Description and characterization of place properties using topic modeling on georeferenced tags. Geo-spatial Information Science 21, 3 (2018), 173–184.
[2]
Rita Borgo, Johannes Kehrer, David HS Chung, Eamonn Maguire, Robert S Laramee, Helwig Hauser, Matthew Ward, and Min Chen. 2013. Glyph-based Visualization: Foundations, Design Guidelines, Techniques and Applications. In Eurographics (STARs). 39–63.
[3]
John D Boy and Justus Uitermark. 2016. How to study the city on Instagram. PloS one 11, 6 (2016), e0158161.
[4]
Jean-Philippe Brunet, Pablo Tamayo, Todd R Golub, and Jill P Mesirov. 2004. Metagenes and molecular pattern discovery using matrix factorization. Proceedings of the national academy of sciences 101, 12(2004), 4164–4169.
[5]
Justin Cranshaw, Raz Schwartz, Jason Hong, and Norman Sadeh. 2012. The livehoods project: Utilizing social media to understand the dynamics of a city. In Sixth International AAAI Conference on Weblogs and Social Media.
[6]
Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Erez Lieberman Aiden, and Li Fei-Fei. 2017. Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. Proceedings of the National Academy of Sciences 114, 50(2017), 13108–13113.
[7]
Eduardo Graells-Garrido, Diego Caro, Omar Miranda, Rossano Schifanella, and Oscar F Peredo. 2018. The WWW (and an H) of Mobile Application Usage in the City: The What, Where, When, and How. In Companion of the The Web Conference 2018 on The Web Conference 2018. International World Wide Web Conferences Steering Committee, 1221–1229.
[8]
Thomas Huang. 1996. Computer vision: Evolution and promise. (1996).
[9]
Neal Jean, Marshall Burke, Michael Xie, W Matthew Davis, David B Lobell, and Stefano Ermon. 2016. Combining satellite imagery and machine learning to predict poverty. Science 353, 6301 (2016), 790–794.
[10]
Bálint Kádár. 2014. Measuring tourist activities in cities using geotagged photography. Tourism Geographies 16, 1 (2014), 88–104.
[11]
Carsten Keßler, Patrick Maué, Jan Torben Heuer, and Thomas Bartoschek. 2009. Bottom-up gazetteers: Learning from the implicit semantics of geotags. In International Conference on GeoSpatial Sematics. Springer, 83–102.
[12]
Kye-Hyeon Kim, Sanghoon Hong, Byungseok Roh, Yeongjae Cheon, and Minje Park. 2016. Pvanet: Deep but lightweight neural networks for real-time object detection. arXiv preprint arXiv:1608.08021(2016).
[13]
Da Kuang, Chris Ding, and Haesun Park. 2012. Symmetric nonnegative matrix factorization for graph clustering. In Proceedings of the 2012 SIAM international conference on data mining. SIAM, 106–117.
[14]
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Tom Duerig, 2018. The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale. arXiv preprint arXiv:1811.00982(2018).
[15]
Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, and Sanja Fidler. 2017. Situation Recognition With Graph Neural Networks. In The IEEE International Conference on Computer Vision (ICCV).
[16]
Arun Mallya and Svetlana Lazebnik. 2017. Recurrent models for situation recognition. In Proceedings of the IEEE International Conference on Computer Vision. 455–463.
[17]
Ignacio Pérez-Messina and Eduardo Graells-Garrido. 2019. Visualizing Transportation Flows with Mode Split using Glyphs. In EuroVis 2019 - Short Papers. The Eurographics Association.
[18]
Daniele Quercia, Luca Maria Aiello, and Rossano Schifanella. [n.d.]. Diversity of indoor activities and economic development of neighborhoods. 13, 6([n. d.]), e0198441. https://doi.org/10.1371/journal.pone.0198441 00000.
[19]
Luca Rossi, Eric Boscaro, and Andrea Torsello. 2018. Venice through the Lens of Instagram: A Visual Narrative of Tourism in Venice. In Companion of the The Web Conference 2018 on The Web Conference 2018. International World Wide Web Conferences Steering Committee, 1190–1197.
[20]
Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. 2016. YFCC100M: The New Data in Multimedia Research. Commun. ACM 59, 2 (2016), 64–73. http://cacm.acm.org/magazines/2016/2/197425-yfcc100m/fulltext
[21]
Carmen Vaca, Daniele Quercia, Bonchi Francesco, Piero Fraternali, 2015. Taxonomy-based discovery and annotation of functional areas in the city. In International Conference on Web and Social Media. 445–453.
[22]
Spencer A Wood, Anne D Guerry, Jessica M Silver, and Martin Lacayo. 2013. Using social media to quantify nature-based tourism and recreation. Scientific reports 3(2013), 2976.
[23]
Mark Yatskar, Luke Zettlemoyer, and Ali Farhadi. 2016. Situation recognition: Visual semantic role labeling for image understanding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5534–5542.
Index Terms
- Toward Characterizing Cities with Social Media Images Using Activity Recognition, Topic Modeling and Visualization
Index terms have been assigned to the content through auto-classification.
Recommendations
Topic Modeling Using Latent Dirichlet allocation: A Survey
We are not able to deal with a mammoth text corpus without summarizing them into a relatively small subset. A computational tool is extremely needed to understand such a gigantic pool of text. Probabilistic Topic Modeling discovers and explains the ...
Application of Interactive Computer-Assisted Argument Extraction to Opinionated Social Media Texts
VINCI '18: Proceedings of the 11th International Symposium on Visual Information Communication and InteractionThe analysis of various opinions and arguments in textual data can be facilitated by automatic topic modeling methods; however, the exploration and interpretation of the resulting topics and terms may prove to be difficult to the analysts. Opinions, ...
The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective
WWW '16: Proceedings of the 25th International Conference on World Wide WebThe Death and Life of Great American Cities was written in 1961 and is now one of the most influential book in city planning. In it, Jane Jacobs proposed four conditions that promote life in a city. However, these conditions have not been empirically ...
Comments
Information & Contributors
Information
Published In
April 2020
854 pages
ISBN:9781450370240
DOI:10.1145/3366424
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 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]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 April 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
WWW '20
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 190Total Downloads
- Downloads (Last 12 months)7
- Downloads (Last 6 weeks)0
Reflects downloads up to 02 Mar 2025
Other Metrics
Citations
Cited By
View all- Ahlers DWilde ESchifanella RAlowibdi J(2021)Report on the tenth international workshop on location and the web (LocWeb 2020)ACM SIGIR Forum10.1145/3451964.345197254:1(1-8)Online publication date: 19-Feb-2021
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format