Skip to main content
SpringerLink
Log in
Book cover

Seeing Cities Through Big Data pp 49–64Cite as

Using User-Generated Content to Understand Cities

  • Chapter
  • First Online:

Part of the book series: Springer Geography ((SPRINGERGEOGR))

Abstract

Understanding urban dynamics is crucial for a number of domains, but it can be expensive and time consuming to gather necessary data. The rapid rise of social media has given us a new and massive source of geotagged data that can be transformative in terms of how we understand our cities. In this position paper, we describe three opportunities in using geotagged social media data: to help city planners, to help small businesses, and to help individuals adapt to their city better. We also sketch some possible research projects to help map out the design space, as well as discuss some limitations and challenges in using this kind of data.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Alexander C et al (1977) A pattern language: towns, buildings, construction. Oxford University Press, New York

    Google Scholar 

  • Andrienko G, Andrienko N, Hurter C, Rinzivillo S, Wrobel S (2011) From movement tracks through events to places: extracting and characterizing significant places from mobility data. IEEE VAST, pp 159–168

    Google Scholar 

  • Becker RA, Cáceres R, Hanson K, Varshavsky A (2011) A tale of one city: using cellular network data for urban planning. IEEE Pervasive Comput 10(4):18–26

    Article  Google Scholar 

  • Becker R, Volinsky C, Cáceres R et al (2013) Human mobility characterization from cellular network data. Commun ACM 56(1):74–82

    Article  Google Scholar 

  • Brockmann D, Hufnagel L, Geisel T (2006) The scaling laws of human travel. Nature 439(7075):462–465

    Article  Google Scholar 

  • Cheng Z, Caverlee J, Lee K, Sui D (2011) Exploring millions of footprints in location sharing services. In: Proceedings of ICWSM

    Google Scholar 

  • Choudhury MD, Monroy-Hernández A, Mark G (2014) Narco emotions: affect and desensitization in social media during the Mexican drug war. In: Proceedings of CHI

    Google Scholar 

  • Cramer H, Rost M, Holmquist LE (2011) Performing a check-in: emerging practices, norms and ‘conflicts’ in location-sharing using foursquare. In: Proceedings of MobileHCI

    Google Scholar 

  • Cranshaw J, Schwartz R, Hong JI, Sadeh N (2012) The livehoods project: utilizing social media to understand the dynamics of a city. In: Proceedings of ICWSM

    Google Scholar 

  • Cranshaw J, Luther K, Kelley PG, Sadeh N (2014) Curated city: capturing individual city guides through social curation. In: Proceedings of CHI

    Google Scholar 

  • (2011) Costing the count. The Economist, June 2, 2011

    Google Scholar 

  • Cuomo C, Shaylor J, Mcguirt M, Francescani C (2009) ‘GMA’ gets answers: some credit card companies financially profiling customers. ABC News

    Google Scholar 

  • Dearman D, Sohn T, Truong KN (2011) Opportunities exist: continuous discovery of places to perform activities. In: Proceedings of CHI, pp 2429–2438

    Google Scholar 

  • Duhigg C (2009) What does your credit-card company know about you? The New York Times. http://www.nytimes.com/2009/05/17/magazine/17credit-t.html

  • Duhigg C (2012) How companies learn your secrets. New York Times, Feb 16, 2012. Retrieved from http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html

  • Eagle N, Macy M, Claxton R (2010) Network diversity and economic development. Science 328:1029–1031

    Article  Google Scholar 

  • Ferris B, Watkins K, Borning A (2010) OneBusAway: results from providing real-time arrival information for public transit. In: Proceedings of CHI, pp 1–10

    Google Scholar 

  • González MC, Hidalgo CA, Barabási A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779–782

    Article  Google Scholar 

  • Griffin D, Hughes T (2013) Projected 2013 costs of a voluntary American Community Survey. United States Census Bureau

    Google Scholar 

  • Harrison S, Dourish P (1996) Re-place-ing space: the roles of place and space in collaborative systems. In: Proceedings of CSCW, pp 67–76

    Google Scholar 

  • Hecht B, Stephens M (2014) A tale of cities: urban biases in volunteered geographic information. ICWSM

    Google Scholar 

  • Huff DL (1963) A probabilistic analysis of shopping center trade areas. Land Econ 39(1):81–90

    Article  Google Scholar 

  • (2014) Instagram: our story. http://www.instagram.com/press. Retrieved 24 May 2014

  • Isaacman S, Becker R, Cáceres R et al (2012) Human mobility modeling at metropolitan scales. In: Proceedings of MobiSys

    Google Scholar 

  • Jernigan C, Mistree BFT (2009) Gaydar: Facebook friendships expose sexual orientation. First Monday 14:10

    Article  Google Scholar 

  • Joseph K, Tan CH, Carley KM (2012) Beyond “Local”, “Categories” and “Friends”: clustering foursquare users with latent “Topics.” In: Proceedings of Ubicomp

    Google Scholar 

  • Kitamura R, Chen C, Pendyala RAMM, Narayanan R (2000) Micro-simulation of daily activity-travel patterns for travel. Transportation 27:25–51

    Article  Google Scholar 

  • Komninos A, Stefanis V, Plessas A, Besharat J (2013) Capturing urban dynamics with scarce check-in data. IEEE Pervasive Comput 12(4):20–28

    Article  Google Scholar 

  • Krikorian R (2013) New tweets per second record, and how! Twitter Engineering Blog. 16 Aug 2013. Retrieved from https://blog.twitter.com/2013/new- tweets-per-second-record-and-how

  • Lindqvist J, Cranshaw J, Wiese J, Hong J, Zimmerman J (2011) I’m the Mayor of my house: examining why people use foursquare - a social-driven location sharing application. In: Proceedings of CHI

    Google Scholar 

  • Lowenthal TA (2006) American Community Survey: evaluating accuracy

    Google Scholar 

  • Mahmud J, Nichols J, Drews C (2013) Home location identification of Twitter users. ACM Trans Intell Syst Technol. doi:10.1145/2528548

    Google Scholar 

  • Martine G et al (2007) The state of world population 2007: unleashing the potential of urban growth. United Nations Population Fund, New York, Retrieved from https://www.unfpa.org/public/home/publications/pid/408

    Google Scholar 

  • Mccullagh D (2011) Carrier IQ: more privacy alarms, more confusion | Privacy Inc. - CNET News. C|Net. http://www.cnet.com/news/carrier-iq-more-privacy-alarms-more-confusion/

  • Mccullagh D (2012) Verizon draws fire for monitoring app usage, browsing habits. C|Net. http://www.cnet.com/news/verizon-draws-fire-for-monitoring-app-usage-browsing-habits/

  • Palchykov V, Kaski K, Kertész J, Barabási A-L, Dunbar RIM (2012) Sex differences in intimate relationships. Sci Rep 2:370

    Article  Google Scholar 

  • Pew Internet (2014) Mobile technology fact sheet. http://www.pewinternet.org/fact-sheets/mobile-technology-fact-sheet/. January 2014

  • Priedhorsky R, Culotta A, Del Valle SY (2014) Inferring the origin locations of tweets with quantitative confidence. In: Proceedings of CSCW, pp 1523–1536

    Google Scholar 

  • Quercia D, Ellis J, Capra L et al (2011) Tracking “Gross Community Happiness” from Tweets. CSCW

    Google Scholar 

  • Rost M, Barkhuus L, Cramer H, Brown B (2013) Representation and communication: challenges in interpreting large social media datasets. In: Proceedings of CSCW

    Google Scholar 

  • Smith-Clarke C, Mashhadi A, Capra L (2014) Poverty on the cheap: estimating poverty maps using aggregated mobile communication networks. In: Proceedings of CHI

    Google Scholar 

  • Toch E, Cranshaw J, Drielsma PH et al (2010) Empirical models of privacy in location sharing. In: Proceedings of Ubicomp, pp 129–138

    Google Scholar 

  • Williams S, Currid-Halkett E (2014) Industry in motion: using smart phones to explore the spatial network of the garment industry in New York City. PLoS One 9(2):1–11

    Google Scholar 

  • Yelp Word Maps (2013) http://www.yelp.com/wordmap/sf/

  • Zhang AX, Noulas A, Scellato S, Mascolo C (2013) Hoodsquare: modeling and recommending neighborhoods in location-based social networks. In: Proceedings of SocialCom, pp 1–15

    Google Scholar 

  • Zimmerman J et al (2011) Field trial of tiramisu: crowd-sourcing bus arrival times to spur co-design. In: Proceedings of CHI

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Dan Tasse & Jason I. Hong

Authors
  1. Dan Tasse
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jason I. Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dan Tasse .

Editor information

Editors and Affiliations

  1. Urban Studies and Urban Big Data Centre, University of Glasgow, Glasgow, United Kingdom

    Piyushimita (Vonu) Thakuriah

  2. Department of Urban Planning and Policy, University of Illinois at Chicago, Chicago, Illinois, USA

    Nebiyou Tilahun

  3. Department of Urban Planning and Policy, University of Illinois at Chicago, Chicago, Illinois, USA

    Moira Zellner

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Tasse, D., Hong, J.I. (2017). Using User-Generated Content to Understand Cities. In: Thakuriah, P., Tilahun, N., Zellner, M. (eds) Seeing Cities Through Big Data. Springer Geography. Springer, Cham. https://doi.org/10.1007/978-3-319-40902-3_3

Download citation

Publish with us

Policies and ethics

Search

Navigation