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Why Topology Matters in Predicting Human Activities
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
Tel-Aviv University.
Tokyo Institute of Technology.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Building, Energy and Environmental Engineering, Energy system.
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2018 (English)In: Article in journal (Refereed) Submitted
Abstract [en]

Geographic space is best understood through the topological relationship of the underlying streets (note: entire streets rather than street segments), which enabales us to see scaling or fractal or living structure of far more less-connected streets than well-connected ones. It is this underlying scaling structure that makes human activities or urban traffic predictable, albeit in the sense of collective rather than individual human moving behavior. This power of topological analysis has not yet received its deserved attention in the literature, as many researchers continue to rely on segment analysis for predicting urban traffic. The segment-analysis-based methods are essentially geometric, with a focus on geometric details such as locations, lengths, and directions, and are unable to reveal the scaling property, which means they cannot be used for human activities prediction. We conducted a series of case studies using London streets and tweet location data, based on related concepts such as natural streets, and natural street segments (or street segments for short), axial lines, and axial line segments (or line segments for short). We found that natural streets are the best representation in terms of traffic prediction, followed by axial lines, and that neither street segments nor line segments bear a good correlation between network parameters and tweet locations. These findings point to the fact that the reason why axial lines-based space syntax, or the kind of topological analysis in general, works has little to do with individual human travel behavior or ways that human conceptualize distances or spaces. Instead, it is the underlying scaling hierarchy of streets – numerous least-connected, a very few most-connected, and some in between the least- and most-connected – that makes human activities or urban traffic predictable.

Place, publisher, year, edition, pages
2018.
Keywords [en]
Topological analysis, space syntax, segment analysis, natural streets, scaling of geographic space
National Category
Other Engineering and Technologies Social and Economic Geography
Identifiers
URN: urn:nbn:se:hig:diva-26166OAI: oai:DiVA.org:hig-26166DiVA, id: diva2:1183547
Available from: 2018-02-18 Created: 2018-02-18 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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