DMCA
A Service of zbw Leibniz-Informationszentrum Wirtschaft Leibniz Information Centre for Economics Exploring city social interaction ties in the big data era: Evidence based on location-based social media data from China
BibTeX
@MISC{Wu_aservice,
author = {Wenjie ; Wu and Jianghao Wang},
title = {A Service of zbw Leibniz-Informationszentrum Wirtschaft Leibniz Information Centre for Economics Exploring city social interaction ties in the big data era: Evidence based on location-based social media data from China},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu 1 Exploring city social interaction ties in the big data era: Evidence based on location-based social media data from China Terms of use: Documents in Abstract: Mountains of public expenditures have been invested on cyberspace infrastructure investments for mapping digital business, social networking and cities with big data. Despite intense public and policy interests, location-based social media data is largely unexplored in conventional urban computational modeling studies due to fundamental challenges in dealing with dynamics of unstructured texts, videos, pictures and web-based big data. This paper explores the geographical ties of urban network patterns by using social media users" mobility flows at a detailed spatial degree (the city-pair level) based on location-based social media (Weibo, a Chinese version of Twitter) data from China. At its heart a geo-tagged computational framework is designed to extract and integrate billions of social media users" space-time footprints for spatiotemporal topology network mining analysis at different geographical scales. A real-time intercity human mobility flow mapping matrix is developed to track spatial patterns of migration flows across regions before and after traditional Chinese Festivals" periods. Our visual exploration results also suggest the significantly heterogeneity in core-periphery urban systems in China, an issue that is highly sensitive in the regional cohesion policy decision making.
