Making Big Data Small: Strategies to Expand Urban and Geographical Research Using Social Media

Ate Poorthuis and Matthew Zook

Journal of Urban Technology, 2017, vol. 24, issue 4, 115-135

Abstract: While exciting, Big Data (particularly geotagged social media data) has proven difficult for many urbanists and social science researchers to use. As a partial solution, we propose a strategy that enables the fast extracting of only relevant data from large sets of geosocial data. While contrary to many Big Data approaches—in which analysis is done on the entire dataset—much productive social science work can use smaller datasets—around the same size as census or survey data—within standard methodological frameworks. The approach we outline in this paper—including the example of a fully operating system—offers a solution for urban researchers interested in these types of data but reluctant to personally build data science skills.

Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://hdl.handle.net/10.1080/10630732.2017.1335153 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:cjutxx:v:24:y:2017:i:4:p:115-135

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/cjut20

DOI: 10.1080/10630732.2017.1335153

Access Statistics for this article

Journal of Urban Technology is currently edited by Richard E. Hanley

More articles in Journal of Urban Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().