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作者

来源

JOURNAL OF URBAN TECHNOLOGY,Vol.24,Issue4,P.115-135

语言

英文

关键字

Big data; social media; data mining; social science methods; twitter; data frameworks; TWITTER DATA; SYSTEM

作者单位

[Poorthuis, Ate] Singapore Univ Technol & Design, Humanities Arts & Social Sci, Singapore, Singapore. [Zook, Matthew] Univ Kentucky, Informat & Econ Geog, Lexington, KY USA. Poorthuis, A (reprint author), Singapore Univ Technol & Design, 8 Somapah Rd,Bldg 1,Level 4, Singapore 487372, Singapore. E-Mail: ate_poorthuis@sutd.edu.sg

摘要

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 approachesin which analysis is done on the entire datasetmuch productive social science work can use smaller datasetsaround the same size as census or survey datawithin standard methodological frameworks. The approach we outline in this paperincluding the example of a fully operating systemoffers a solution for urban researchers interested in these types of data but reluctant to personally build data science skills.