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  • 1.
    Jiang, Bin
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad, GIS.
    Spatial heterogeneity, scale, data character and sustainable transport in the big data era2018Inngår i: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 7, nr 5 (SI), artikkel-id 167Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    I have advocated and argued for a paradigm shift from Tobler’s law to scaling law, from Euclidean geometry to fractal geometry, from Gaussian statistics to Paretian statistics, and – more importantly – from Descartes’ mechanistic thinking to Alexander’s organic thinking. Fractal geometry falls under the third definition of fractal given by Bin Jiang – that is, a set or pattern is fractal if the scaling of far more small things than large ones recurs multiple times – rather than under the second definition of fractal by Benoit Mandelbrot, which requires a power law between scales and details. The new fractal geometry is more towards Christopher Alexander’s living geometry, not only for understanding complexity, but also for creating complex or living structure. This short paper attempts to clarify why the paradigm shift is essential and to elaborate on several concepts, including spatial heterogeneity (scaling law), scale (or the fourth meaning of scale), data character (in contrast to data quality), and sustainable transport in the big data era.

  • 2.
    Ma, Ding
    et al.
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad, GIS.
    Sandberg, Mats
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för bygg- energi- och miljöteknik, Energisystem.
    Jiang, Bin
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad, GIS.
    Characterizing the Heterogeneity of the OpenStreetMap Data and Community2015Inngår i: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 4, nr 2, s. 535-550Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    OpenStreetMap (OSM) constitutes an unprecedented, free, geographical information source contributed by millions of individuals, resulting in a database of great volume and heterogeneity. In this study, we characterize the heterogeneity of the entire OSM database and historical archive in the context of big data. We consider all users, geographic elements and user contributions from an eight-year data archive, at a size of 692 GB. We rely on some nonlinear methods such as power law statistics and head/tail breaks to uncover and illustrate the underlying scaling properties. All three aspects (users, elements, and contributions) demonstrate striking power laws or heavy-tailed distributions. The heavy-tailed distributions imply that there are far more small elements than large ones, far more inactive users than active ones, and far more lightly edited elements than heavy-edited ones. Furthermore, about 500 users in the core group of the OSM are highly networked in terms of collaboration.

  • 3.
    Ren, Zheng
    et al.
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för datavetenskap och samhällsbyggnad, Samhällsbyggnad.
    Jiang, Bin
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för datavetenskap och samhällsbyggnad, Samhällsbyggnad.
    Seipel, Stefan
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för datavetenskap och samhällsbyggnad, Datavetenskap.
    Capturing and characterizing human activities using building locations in America2019Inngår i: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 8, nr 5, artikkel-id 200Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Capturing and characterizing collective human activities in a geographic space have become much easier than ever before in the big era. In the past few decades it has been difficult to acquire the spatiotemporal information of human beings. Thanks to the boom in the use of mobile devices integrated with positioning systems and location-based social media data, we can easily acquire the spatial and temporal information of social media users. Previous studies have successfully used street nodes and geo-tagged social media such as Twitter to predict users’ activities. However, whether human activities can be well represented by social media data remains uncertain. On the other hand, buildings or architectures are permanent and reliable representations of human activities collectively through historical footprints. This study aims to use the big data of US building footprints to investigate the reliability of social media users for human activity prediction. We created spatial clusters from 125 million buildings and 1.48 million Twitter points in the US. We further examined and compared the spatial and statistical distribution of clusters at both country and city levels. The result of this study shows that both building and Twitter data spatial clusters show the scaling pattern measured by the scale of spatial clusters, respectively, characterized by the number points inside clusters and the area of clusters. More specifically, at the country level, the statistical distribution of the building spatial clusters fits power law distribution. Inside the four largest cities, the hotspots are power-law-distributed with the power law exponent around 2.0, meaning that they also follow the Zipf’s law. The correlations between the number of buildings and the number of tweets are very plausible, with the r square ranging from 0.53 to 0.74. The high correlation and the similarity of two datasets in terms of spatial and statistical distribution suggest that, although social media users are only a proportion of the entire population, the spatial clusters from geographical big data is a good and accurate representation of overall human activities. This study also indicates that using an improved method for spatial clustering is more suitable for big data analysis than the conventional clustering methods based on Euclidean geometry.

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