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Spatial Distribution of City Tweets and Their Densities
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS. 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
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS. University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
Department of Geography and Geographic Information Science, University of Illinois at Urbana and Champaign, Illinois, USA.
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 Building, Energy and Environmental Engineering, Energy system.
2016 (English)In: Geographical Analysis, ISSN 0016-7363, E-ISSN 1538-4632, Vol. 48, no 3, p. 337-351Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

Social media outlets such as Twitter constitute valuable data sources for understanding human activities in the virtual world from a geographic perspective. This article examines spatial distribution of tweets and densities within cities. The cities refer to natural cities that are automatically aggregated from a country’s small street blocks, so called city blocks. We adopted street blocks (rather than census tracts) as the basic geographic units and topological center (rather than geometric center) to assess how tweets and densities vary from the center to the peripheral border. We found that, within a city from the center to the periphery, the tweets first increase and then decrease, while the densities decrease in general. These increases and decreases fluctuate dramatically, and differ significantly from those if census tracts are used as the basic geographic units. We also found that the decrease of densities from the center to the periphery is less significant, and even disappears, if an arbitrarily defined city border is adopted. These findings prove that natural cities and their topological centers are better than their counterparts (conventionally defined cities and city centers) for geographic research. Based on this study, we believe that tweet densities can be a good surrogate of population densities. If this belief is proved to be true, social media data could help solve the dispute surrounding exponential or power function of urban population density.

Place, publisher, year, edition, pages
2016. Vol. 48, no 3, p. 337-351
Keywords [en]
urban-population densities, head/tail breaks
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:hig:diva-22228DOI: 10.1111/gean.12096ISI: 000380333200006Scopus ID: 2-s2.0-84959080817OAI: oai:DiVA.org:hig-22228DiVA, id: diva2:953095
Available from: 2016-08-16 Created: 2016-08-16 Last updated: 2018-03-13Bibliographically 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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Jiang, BinMa, DingSandberg, Mats

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