Abstract
The rapid development of big data analytics provides tremendous possibilities to investigate large-scale patterns in both the spatial and temporal dimensions. In this research, we utilize a unique open dataset, the Global Database on Events, Location, and Tone (GDELT), and a geotagged social media dataset (Weibo) to analyze connections between Chinese provinces. Specifically, this study constructs a gravity model to compare the distance decay effect between the GDELT data (i.e., mass media data) and the Weibo data (i.e., location-based social media [LBSM] data). The results demonstrate that mass media data possess a weaker distance decay effect than LBSM data for Chinese provinces. This study generates valuable input to interpret regional relations in a fast-growing, developing country—China. It also provides methodological references to explore urban relations in other countries and regions in the big data era.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
Conflict and Mediation Event Observations (CAMEO) is a framework for coding event data.
- 3.
From the GDELT codebook (http://data.gdeltproject.org/documentation/GDELT-Global_Knowledge_Graph_Codebook.pdf).
- 4.
Here user IDs are long integers generated by Weibo.com and are not directly connected to any personally identifiable information (PII), unless the users volunteer to make such information publicly accessible.
References
Austin LC (1963) Review of shaping the world-economy, Tinbergen. J Int Aff 17(2):221
Box GEP, Draper NR (1987) Empirical model-building and response surfaces. Wiley series in probability and mathematical statistics. Applied probability and statistics. Wiley, New York
Brockmann D, Theis F (2008) Money circulation, trackable items, and the emergence of universal human mobility patterns. IEEE Pervas Comput 7(4):28–35
Chun Y, Griffith DA (2011) Modeling network autocorrelation in space–time migration flow data: an eigenvector spatial filtering approach. Ann Assoc Am Geogr 101(3):523–536. doi:10.1080/00045608.2011.561070
Eagle N, Pentland A, Lazer D (2009) Inferring friendship network structure by using mobile phone data. Proc Natl Acad Sci USA 106(36):15274–15278. doi:10.1073/pnas.0900282106
Elwood S, Goodchild MF, Sui DZ (2012) Researching volunteered geographic information: spatial data, geographic research, and new social practice. Ann Assoc Am Geogr 102(3):571–590. doi:10.1080/00045608.2011.595657
Fischer M, Reismann M, Scherngell T (2010) Spatial interaction and spatial autocorrelation. In: Anselin L, Rey SJ (eds) Perspectives on spatial data analysis: advances in spatial science. Springer, Berlin, pp 61–79. doi:10.1007/978-3-642-01976-0_5
Gao H, Tang J, Liu H (2012) Exploring social-historical ties on location-based social networks. Paper presented at the 6th international AAAI conference on weblogs and social media, Dublin, Ireland, June 4–7, 2012
Getis A (1991) Spatial interaction and spatial autocorrelation: a cross-product approach. Environ Plan A 23:1269–1277
Gonzalez MC, Hidalgo CA, Barabasi AL (2008) Understanding individual human mobility patterns. Nature 453(7196):779–782. doi:10.1038/Nature06958
Hardy D, Frew J, Goodchild MF (2012) Volunteered geographic information production as a spatial process. Int J Geogr Inf Sci 26(7):1191–1212. doi:10.1080/13658816.2011.629618
Hasan S, Zhan X, Ukkusuri SV (2013) Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. In: UrbComp 13, Chicago, 2013, Chicago, August 11, 2013
Jiang L, Mai F (2014) Discovering bilateral and multilateral causal events in GDELT. Paper presented at the international conference on social computing, behavioral-cultural modeling, and prediction, Washington, DC, April 2–4, 2014
Klapper JT (1968) Effects of mass-media-depicted violence—a review of research findings. Am J Orthopsychiat 38(2):310
Leetaru K, Schrodt P (2013) GDELT: global data on events, language, and tone, 1979–2012. Paper presented at the international studies association annual conference, San Diego, CA
Lewer JJ, Van den Berg H (2008) A gravity model of immigration. Econ Lett 99(1):164–167. doi:10.1016/j.econlet.2007.06.019
Li X, Liu B (2003) Learning to classify texts using positive and unlabeled data. Paper presented at the 18th international joint conference on artificial intelligence, Acapulco, Mexico, August 3–9, 2003
Liben-Nowell D, Novak J, Kumar R, Raghavan P, Tomkins A (2005) Geographic routing in social networks. Proc Natl Acad Sci USA 102(33):11623–11628. doi:10.1073/pnas.0503018102
Liebert RM, Schwartzberg NS (1977) Effects of mass-media. Annu Rev Psychol 28:141–173
Liu Y, Wang FH, Kang CG, Gao Y, Lu YM (2014) Analyzing relatedness by toponym co-occurrences on web pages. Trans GIS 18(1):89–107. doi:10.1111/Tgis.12023
Malleson N, Birkin M (2014) New insights into individual activity spaces using crowd-sourced big data. Paper presented at the ASE bigdata/socialcom/cybersecurity conference, Stanford, CA, May 27–31, 2014
Masand B, Linoff G, Waltz D (1992) Classifying news stories using memory based reasoning. Paper presented at the 15th annual international ACM SIGIR conference on research and development in information retrieval, Copenhagen, Denmark, June 21–24, 1992
Mazzitello KI, Candia J, Dossetti V (2007) Effects of mass media and cultural drift in a model for social influence. Int J Mod Phys C 18(9):1475–1482
Rodrigue J-P, Comtois C, Slack B (2013) The geography of transport systems, 3rd edn. Routledge, Abingdon, Oxon
Roick O, Heuser S (2013) Location based social networks—definition, current state of the art and research agenda. Trans GIS 17:763–784
Sen AK, Smith TE (1995) Gravity models of spatial interaction behavior. Advances in spatial and network economics. Springer, Berlin
Schrodt P (2012) Conflict and Mediation Event Observations event and actor codebook V.1.1b3. [http://eventdata.psu.edu/cameo.dir/CAMEO.Manual.1.1b3.pdf]
Shook E, Leetaru K, Cao G, Padmanabhan A, Wang S (2012) Happy or not: generating topic-based emotional heatmaps for culturomics using CyberGIS. Paper presented at the 8th IEEE international conference on eScience, Chicago, October 8–12, 2012
Song CM, Qu ZH, Blumm N, Barabasi AL (2010) Limits of predictability in human mobility. Science 327(5968):1018–1021. doi:10.1126/science.1177170
Wu L, Zhi Y, Sui ZW, Liu Y (2014) Intra-urban human mobility and activity transition: evidence from social media check-in data. PLoS One 9(5):e97010. doi:10.1371/journal.pone.0097010
Yonamine JE (2013) Predicting future levels of violence in Afghanistan district using GDELT. UT Dallas, Technical Report. http://data.gdeltproject.org/documentation/Predicting-Future-Levels-of-Violence-in-Afghanistan-Districts-using-GDELT.pdf
Yuan Y, Liu Y (2015) Exploring inter-country connections in mass media: a case study of China. In: International conference on location-based social media, Athens, GA, 2015
Yuan Y, Raubal M, Liu Y (2012) Correlating mobile phone usage and travel behavior—a case study of Harbin, China. Comput Environ Urban Syst 36(2):118–130
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this paper
Cite this paper
Yuan, Y. (2017). Exploring the Spatial Decay Effect in Mass Media and Location-Based Social Media: A Case Study of China. In: Griffith, D., Chun, Y., Dean, D. (eds) Advances in Geocomputation. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-22786-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-22786-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22785-6
Online ISBN: 978-3-319-22786-3
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)