TY - GEN
T1 - Measuring inter-city network using digital footprints from twitter users
AU - Jiang, Yuqin
AU - Li, Zhenlong
AU - Ye, Xinyue
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2018/11/6
Y1 - 2018/11/6
N2 - City connectivity is an important measurement in characterizing human dynamics from regional to international scales. World City Network has been built based on companies' communication. The interactions between spatial and social dimensions of cities have both conceptual and practical significance. To further expand the studies of inter-city network in the big social data context, this research builds a network at the county level using digital footprints from Twitter users. Retrieving geotags from Twitter users, we identify the connection strength of each pair of counties based on the amounts of shared users who leave digital footprints on both counties. Using the shared user amount as the weighted link and each county as the node, we build a county-to-county user flow network. Various network structures have been detected at the state level. In addition, by creating a direct flow chain, we can identify influential counties and its hinterland. This network demonstrates how human mobility operate across various spatial settings and distances. Results of this study can be used in transportation planning, regional planning and metropolitan management.
AB - City connectivity is an important measurement in characterizing human dynamics from regional to international scales. World City Network has been built based on companies' communication. The interactions between spatial and social dimensions of cities have both conceptual and practical significance. To further expand the studies of inter-city network in the big social data context, this research builds a network at the county level using digital footprints from Twitter users. Retrieving geotags from Twitter users, we identify the connection strength of each pair of counties based on the amounts of shared users who leave digital footprints on both counties. Using the shared user amount as the weighted link and each county as the node, we build a county-to-county user flow network. Various network structures have been detected at the state level. In addition, by creating a direct flow chain, we can identify influential counties and its hinterland. This network demonstrates how human mobility operate across various spatial settings and distances. Results of this study can be used in transportation planning, regional planning and metropolitan management.
UR - http://www.scopus.com/inward/record.url?scp=85066066423&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066066423&partnerID=8YFLogxK
U2 - 10.1145/3283590.3283594
DO - 10.1145/3283590.3283594
M3 - Conference contribution
AN - SCOPUS:85066066423
T3 - Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018
SP - 25
EP - 31
BT - Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018
A2 - Sudo, Akihito
A2 - Chin, Lau Hoong
A2 - Yabe, Takahiro
A2 - Song, Xuan
A2 - Sekimoto, Yoshihide
PB - Association for Computing Machinery, Inc
T2 - 2nd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, PredictGIS 2018
Y2 - 6 November 2018
ER -
