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A Socio-Geographic Perspective on Human Activities in Social Media
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.ORCID iD: 0000-0001-9328-9584
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Building, Energy and Environmental Engineering, Energy system.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.ORCID iD: 0000-0002-2337-2486
2017 (English)In: Geographical Analysis, ISSN 0016-7363, E-ISSN 1538-4632, Vol. 49, no 3, p. 328-342Article in journal (Refereed) Published
Abstract [en]

Location-based social media make it possible to understand social and geographic aspects of human activities. However, previous studies have mostly examined these two aspects separately without looking at how they are linked. The study aims to connect two aspects by investigating whether there is any correlation between social connections and users' check-in locations from a socio-geographic perspective. We constructed three types of networks: a people–people network, a location–location network, and a city–city network from former location-based social media Brightkite and Gowalla in the U.S., based on users' check-in locations and their friendships. We adopted some complexity science methods such as power-law detection and head/tail breaks classification method for analysis and visualization. Head/tail breaks recursively partitions data into a few large things in the head and many small things in the tail. By analyzing check-in locations, we found that users' check-in patterns are heterogeneous at both the individual and collective levels. We also discovered that users' first or most frequent check-in locations can be the representatives of users' spatial information. The constructed networks based on these locations are very heterogeneous, as indicated by the high ht-index. Most importantly, the node degree of the networks correlates highly with the population at locations (mostly with R2 being 0.7) or cities (above 0.9). This correlation indicates that the geographic distributions of the social media users relate highly to their online social connections.

Place, publisher, year, edition, pages
2017. Vol. 49, no 3, p. 328-342
National Category
Other Social Sciences Other Civil Engineering
Identifiers
URN: urn:nbn:se:hig:diva-24868DOI: 10.1111/gean.12122ISI: 000405108800004Scopus ID: 2-s2.0-85011710648OAI: oai:DiVA.org:hig-24868DiVA, id: diva2:1134008
Note

Funding agency:

Key Laboratory of Eco Planning & Green Building, Ministry of Education (Tsinghua University), China

Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2018-03-22Bibliographically approved
In thesis
1. Topological and Scaling Analysis of Geospatial Big Data
Open this publication in new window or tab >>Topological and Scaling Analysis of Geospatial Big Data
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Geographic information science and systems face challenges related to understanding the instinctive heterogeneity of geographic space, since conventional geospatial analysis is mainly founded on Euclidean geometry and Gaussian statistics. This thesis adopts a new paradigm, based on fractal geometry and Paretian statistics for geospatial analysis. The thesis relies on the third definition of fractal geometry: A set or pattern is fractal if the scaling of far more small things than large ones recurs multiple times. Therefore, the terms fractal and scaling are used interchangeably in this thesis. The new definition of fractal is well-described by Paretian statistics, which is mathematically defined as heavy-tailed distributions. The topology of geographic features is the key prerequisite that enables us to see the fractal or scaling structure of the geographic space. In this thesis, topology refers to the relationship among meaningful geographic features (such as natural streets and natural cities).

The thesis conducts topological and scaling analyses of geographic space and its involved human activities in the context of geospatial big data. The thesis utilizes the massive, volunteered, geographic information coming from LBSM platforms, which are the global OpenStreetMap database and countrywide, geo-referenced tweets and check-in locations. The thesis develops geospatial big-data processing and modeling techniques, and employs complexity science methods, including heavy-tailed distribution detection and head/tail breaks, along with some complex network analysis. Head/tail breaks and the induced ht-index are a powerful tool for geospatial big-data analytics and visualization. The derived scaling hierarchies, power-law metrics, and network measures provide quantitative insights into the heterogeneity of geographic space and help us understand how it shapes human activities at city, country, and world scales. 

Place, publisher, year, edition, pages
Gävle: Gävle University Press, 2018. p. 73
Series
Studies in the Research Profile Built Environment. Doctoral thesis ; 7
Keywords
Third definition of fractal, scaling, topology, power law, head/tail breaks, ht-index, complex network, geospatial big data, natural cities, natural streets
National Category
Computer and Information Sciences Earth and Related Environmental Sciences
Identifiers
urn:nbn:se:hig:diva-26197 (URN)978-91-88145-24-6 (ISBN)978-91-88145-25-3 (ISBN)
Public defence
2018-05-16, Lilla Jadwiga-salen, Kungsbäcksvägen 47, Gävle, 10:00 (English)
Opponent
Supervisors
Available from: 2018-04-24 Created: 2018-03-04 Last updated: 2018-04-25

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Ma, DingSandberg, MatsJiang, Bin

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