LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Lovelace, R; Birkin, M; Malleson, N (2014)
Publisher: University of Glasgow
Languages: English
Types: Other
Subjects:
This paper explores the potential of volunteered geographical information from social media to inform geographical models of behavior. Based on a case study of museums in Yorkshire, we created a spatial interaction model of visitors to 15 museums from 179 administrative zones to test this potential. Instead of relying on limited official data on the magnitude of flows from different attractions we used volunteered geographic information’ (VGI) to calibrate the model. The method represents the potential of VGI for applications beyond descriptive statistics and visuals and highlights potential uses of georeferenced social media data for geographic models. The main input dataset comprised geo-tagged messages harvested using the Twitter Streaming Application Programming Interface (API). We successfully calibrated the distance decay parameter of the model and conclude that social media data have great potential for aiding models of spatial behavior. However, we also caution that there are dangers associated with the use of social media data. Researchers should weigh up the wider costs and benefits of harnessing such ‘big data’ before blindly harnessing this low quality, high volume resource. Our case study also serves as the basis for discussion of the ethics surrounding the use of privately harvested VGI by publicly funded academics.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • EUGSTER, M. J. A., & SCHLESINGER, T. (2013). osmar: OpenStreetMap and R. R Journal, 5(1), 53-63.
    • FLANAGIN, A. J., & METZGER, M. J. (2008). The credibility of volunteered geographic information. GeoJournal, 72(3-4), 137- 148.
    • GOODCHILD, M. F. (2007). Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4), 211-221. doi: 10.1007/s10708-007-9111-y
    • RUSSELL, M. A. (2011). Mining the Social Web: Analyzing Data from SNOWDEN, E. (2013). Interview with Glen Grweenwald - Full Transcript. http://www.policymic.com/articles/47355/edwardsnowden-interview-transcript-full-text-read-the-guardian-sentire-interview-with-the-man-who-leaked-prism
    • SUNDAY, E. M., & AWARA, N. F. (2014). Customer Satisfaction and Social Media Driven Micromarketing: An Empirical Evidence. International Business and management, 8(1), 32-36.
    • WILSON, A. G. (2000). Complex spatial systems: the modelling foundations of urban and regional analysis: Pearson Education.
  • Inferred research data

    The results below are discovered through our pilot algorithms. Let us know how we are doing!

    Title Trust
    42
    42%
  • No similar publications.

Share - Bookmark

Cite this article