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Jiang, Bin, ProfessorORCID iD iconorcid.org/0000-0002-2337-2486
Publications (10 of 103) Show all publications
Jiang, B. & Ren, Z. (2019). Geographic space as a living structure for predicting human activities using big data. International Journal of Geographical Information Science, 33(4), 764-779
Open this publication in new window or tab >>Geographic space as a living structure for predicting human activities using big data
2019 (English)In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 33, no 4, p. 764-779Article in journal (Refereed) Published
Abstract [en]

Inspired by Christopher Alexander's conception of the world - space is not lifeless or neutral, but a living structure involving far more small things than large ones - a topological representation has been previously developed to characterize the living structure or the wholeness of geographic space. This paper further develops the topological representation and living structure for predicting human activities in geographic space. Based on millions of street nodes of the United Kingdom extracted from OpenStreetMap, we established living structures at different levels of scale in a nested manner. We found that tweet locations at different levels of scale, such as country and city, can be well predicted by the underlying living structure. The high predictability demonstrates that the living structure and the topological representation are efficient and effective for better understanding geographic forms. Based on this major finding, we argue that the topological representation is a truly multiscale representation, and point out that existing geographic representations are essentially single scale, so they bear many scale problems such as modifiable areal unit problem, the conundrum of length and the ecological fallacy. We further discuss on why the living structure is an efficient and effective instrument for structuring geospatial big data, and why Alexander's organic worldview constitutes the third view of space.

Place, publisher, year, edition, pages
Taylor & Francis, 2019
Keywords
Organic worldview, topological representation, tweet locations, natural cities, scaling of geographic space
National Category
Social and Economic Geography
Identifiers
urn:nbn:se:hig:diva-26177 (URN)10.1080/13658816.2018.1427754 (DOI)000459561600007 ()2-s2.0-85041331898 (Scopus ID)
Funder
Swedish Research Council Formas, FR-2017/0009
Available from: 2018-02-22 Created: 2018-02-22 Last updated: 2019-03-12Bibliographically approved
Yao, X. A., Huang, H., Jiang, B. & Krisp, J. M. (2019). Representation and analytical models for location-based big data. International Journal of Geographical Information Science, 33(4), 707-713
Open this publication in new window or tab >>Representation and analytical models for location-based big data
2019 (English)In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 33, no 4, p. 707-713Article in journal, Editorial material (Refereed) Published
Place, publisher, year, edition, pages
Taylor & Francis, 2019
National Category
Physical Geography Civil Engineering
Identifiers
urn:nbn:se:hig:diva-29190 (URN)10.1080/13658816.2018.1562068 (DOI)000459561600004 ()2-s2.0-85059679355 (Scopus ID)
Available from: 2019-01-28 Created: 2019-01-28 Last updated: 2019-03-12Bibliographically approved
Jiang, B. (2019). Spatial heterogeneity, scale, data character, and sustainable transport in the big data era. In: Nathanail E.G., and Karakikes I.D. (Ed.), CSUM 2018: Data Analytics: Paving the Way to Sustainable Urban Mobility: Proceedings of 4th Conference on Sustainable Urban Mobility (CSUM2018), 24 - 25 May, Skiathos Island, Greece. Paper presented at 4th Conference on Sustainable Urban Mobility, CSUM 2018, 24-25 May 2018, Skiathos Island, Greece (pp. 730-736). Springer Verlag, 879
Open this publication in new window or tab >>Spatial heterogeneity, scale, data character, and sustainable transport in the big data era
2019 (English)In: CSUM 2018: Data Analytics: Paving the Way to Sustainable Urban Mobility: Proceedings of 4th Conference on Sustainable Urban Mobility (CSUM2018), 24 - 25 May, Skiathos Island, Greece / [ed] Nathanail E.G., and Karakikes I.D., Springer Verlag , 2019, Vol. 879, p. 730-736Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Springer Verlag, 2019
Series
Advances in Intelligent Systems and Computing, ISSN 21945357
Keywords
Data character, Living structure, Scaling law, Big data, Scaling laws, Christopher Alexander, Euclidean Geometry, Fractal geometry, Gaussian statistics, Paradigm shifts, Spatial heterogeneity, Sustainable transport, Fractals
National Category
Civil Engineering
Identifiers
urn:nbn:se:hig:diva-29069 (URN)10.1007/978-3-030-02305-8_88 (DOI)2-s2.0-85059025602 (Scopus ID)9783030023041 (ISBN)
Conference
4th Conference on Sustainable Urban Mobility, CSUM 2018, 24-25 May 2018, Skiathos Island, Greece
Available from: 2019-01-07 Created: 2019-01-07 Last updated: 2019-01-07Bibliographically approved
Ma, D. & Jiang, B. (2018). A smooth curve as a fractal under the third definition. Cartographica, 53(3), 203-210
Open this publication in new window or tab >>A smooth curve as a fractal under the third definition
2018 (English)In: Cartographica, ISSN 0317-7173, E-ISSN 1911-9925, Vol. 53, no 3, p. 203-210Article in journal (Refereed) Published
Abstract [en]

It is commonly believed in the literature that smooth curves, such as circles, are not fractal, and only non-smooth curves, such as coastlines, are fractal. However, this paper demonstrates that a smooth curve can be fractal, under the new, relaxed, third definition of fractal – a set or pattern is fractal if the scaling of far more small things than large ones recurs at least twice. The scaling can be rephrased as a hierarchy, consisting of numerous smallest, a very few largest, and some in between the smallest and the largest. The logarithmic spiral, as a smooth curve, is apparently fractal because it bears the self-similar property, or the scaling of far more small squares than large ones recurs multiple times, or the scaling of far more small bends than large ones recurs multiple times. A half-circle or half-ellipse and the UK coastline (before or after smooth processing) are fractal, if the scaling of far more small bends than large ones recurs at least twice.

Abstract [fr]

Il est généralement convenu dans les écrits que les courbes douces, comme les cercles, ne sont pas fractales, et que seules les courbes qui ne sont pas douces, comme les littoraux, sont fractales. Les auteurs montrent toutefois qu'une courbe douce peut être fractale, en vertu d'une troisième définition, nouvelle et élargie, du terme fractal — un ensemble ou un motif est fractal si l'échelle d'un nombre beaucoup plus grand de petits éléments que de grands se répète au moins deux fois. L'échelle peut être interprétée comme étant la hiérarchie, soit un grand nombre d'éléments très petits, très peu d'éléments très grands, et des éléments se situant entre les plus petits et les plus grands. La spirale équangulaire, à titre de courbe douce, est en apparence fractale du fait qu'elle affiche la propriété d'autosimilitude, ou du fait que l'échelle d'un nombre beaucoup plus grand de petits carrés que de grands se répète plusieurs fois, ou l'échelle d'un nombre beaucoup plus grand de petite courbures que de grandes se répète plusieurs fois. Un demi-cercle ou une demi-ellipse et le littoral du Royaume-Uni (avant ou après lissage) sont fractals si l'échelle d'un nombre beaucoup plus grand de petites courbures que de grandes se répète au moins deux fois.

Keywords
Third definition of fractal, head/tail breaks, bends, ht-index, scaling hierarchy, courbures, échelle, hiérarchie, indice h-t, ruptures de tête ou de queue, troisième définition de fractal
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:hig:diva-26164 (URN)10.3138/cart.53.3.2017-0032 (DOI)000449098400004 ()2-s2.0-85055157486 (Scopus ID)
Available from: 2018-02-18 Created: 2018-02-18 Last updated: 2018-11-27Bibliographically approved
Jiang, B. (2018). A Topological Representation for Taking Cities as a Coherent Whole. Geographical Analysis, 50(3), 298-313
Open this publication in new window or tab >>A Topological Representation for Taking Cities as a Coherent Whole
2018 (English)In: Geographical Analysis, ISSN 0016-7363, E-ISSN 1538-4632, Vol. 50, no 3, p. 298-313Article in journal (Refereed) Published
Abstract [en]

A city is a whole, as are all cities in a country. Within a whole, individual cities possess different degrees of wholeness, defined by Christopher Alexander as a life-giving order or simply a living structure. To characterize the wholeness and in particular to advocate for wholeness as an effective design principle, this article develops a geographic representation that views cities as a whole. This geographic representation is topology-oriented, so fundamentally differs from existing geometry-based geographic representations. With the topological representation, all cities are abstracted as individual points and put into different hierarchical levels, according to their sizes and based on head/tail breaks-a classification and visualization tool for data with a heavy tailed distribution. These points of different hierarchical levels are respectively used to create Thiessen polygons. Based on polygon-polygon relationships, we set up a complex network. In this network, small polygons point to adjacent large polygons at the same hierarchical level and contained polygons point to containing polygons across two consecutive hierarchical levels. We computed the degrees of wholeness for individual cities, and subsequently found that the degrees of wholeness possess both properties of differentiation and adaptation. To demonstrate, we developed four case studies of all China and U.K. natural cities, as well as Beijing and London natural cities, using massive amounts of street nodes and Tweet locations. The topological representation and the kind of topological analysis in general can be applied to any design or pattern, such as carpets, Baroque architecture and artifacts, and fractals in order to assess their beauty, echoing the introductory quote from Christopher Alexander.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hig:diva-27644 (URN)10.1111/gean.12145 (DOI)000440288900005 ()2-s2.0-85051267873 (Scopus ID)
Available from: 2018-08-15 Created: 2018-08-15 Last updated: 2018-09-05Bibliographically approved
Chen, Y. & Jiang, B. (2018). Hierarchical scaling in systems of natural cities. Entropy, 20(6), Article ID 432.
Open this publication in new window or tab >>Hierarchical scaling in systems of natural cities
2018 (English)In: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 20, no 6, article id 432Article in journal (Refereed) Published
Abstract [en]

Hierarchies can be modeled by a set of exponential functions, from which we can derive a set of power laws indicative of scaling. The solution to a scaling relation equation is always a power law. The scaling laws are followed by many natural and social phenomena such as cities, earthquakes, and rivers. This paper reveals the power law behaviors in systems of natural cities by reconstructing the urban hierarchy with cascade structure. Cities of the U.S.A., Britain, France, and Germany are taken as examples to perform empirical analyses. The hierarchical scaling relations can be well fitted to the data points within the scaling ranges of the number, size and area of the natural cities. The size-number and area-number scaling exponents are close to 1, and the size-area allometric scaling exponent is slightly less than 1. The results show that natural cities follow hierarchical scaling laws very well. The principle of entropy maximization of urban evolution is then employed to explain the hierarchical scaling laws, and differences entropy maximizing processes are used to interpret the scaling exponents. This study is helpful for scientists to understand the power law behavior in the development of cities and systems of cities. © 2018 by the authors.

Place, publisher, year, edition, pages
MDPI AG, 2018
Keywords
Allometry, Entropy, Fractals, Hierarchy, Natural cities, Scaling
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:hig:diva-27519 (URN)10.3390/e20060432 (DOI)000436275400041 ()2-s2.0-85048716908 (Scopus ID)
Available from: 2018-07-04 Created: 2018-07-04 Last updated: 2018-08-15Bibliographically approved
Jiang, B. & Ma, D. (2018). How complex is a fractal?: Head/tail breaks and fractional hierarchy. Journal of Geovisualization and Spatial Analysis, 2(6)
Open this publication in new window or tab >>How complex is a fractal?: Head/tail breaks and fractional hierarchy
2018 (English)In: Journal of Geovisualization and Spatial Analysis, ISSN 2509-8810, Vol. 2, no 6Article in journal (Refereed) Published
Abstract [en]

A fractal bears a complex structure that is reflected in a scaling hierarchy, indicating that there are far more small things than large ones. This scaling hierarchy can be effectively derived using head/tail breaks—a clustering and visualization tool for data with a heavy-tailed distribution—and quantified by a head/tail breaks-induced integer, called ht-index, indicating the number of clusters or hierarchical levels. However, this integral ht-index has been found to be less precise for many fractals at their different phrases of development. This paper refines the ht-index as a fraction to measure the scaling hierarchy of a fractal more precisely within a coherent whole and further assigns a fractional ht-index—the fht-index—to an individual data value of a data series that represents the fractal. We developed two case studies to demonstrate the advantages of the fht-index, in comparison with the ht-index. We found that the fractional ht-index or fractional hierarchy in general can help characterize a fractal set or pattern in a much more precise manner. The index may help create intermediate map scales between two consecutive map scales.

Keywords
Ht-index;Fractal;Scaling;Complexity;Fht-index
National Category
Natural Sciences
Identifiers
urn:nbn:se:hig:diva-26168 (URN)10.1007/s41651-017-0009-z (DOI)
Available from: 2018-02-18 Created: 2018-02-18 Last updated: 2019-01-08Bibliographically approved
Jiang, B. (2018). Spatial heterogeneity, scale, data character and sustainable transport in the big data era. ISPRS International Journal of Geo-Information, 7(5 (SI)), Article ID 167.
Open this publication in new window or tab >>Spatial heterogeneity, scale, data character and sustainable transport in the big data era
2018 (English)In: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 7, no 5 (SI), article id 167Article in journal, Editorial material (Refereed) Published
Place, publisher, year, edition, pages
MDPI AG, 2018
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:hig:diva-26791 (URN)10.3390/ijgi7050167 (DOI)000435194700007 ()2-s2.0-85047143960 (Scopus ID)
Available from: 2018-06-05 Created: 2018-06-05 Last updated: 2018-07-05Bibliographically approved
Ma, D., Omer, I., Osaragi, T., Sandberg, M. & Jiang, B. (2018). Why Topology Matters in Predicting Human Activities. Environment and Planning B: Urban Analytics and City Science
Open this publication in new window or tab >>Why Topology Matters in Predicting Human Activities
Show others...
2018 (English)In: Environment and Planning B: Urban Analytics and City Science, ISSN 2399-8083Article in journal (Refereed) Epub ahead of print
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.

Keywords
Topological analysis, space syntax, segment analysis, natural streets, scaling of geographic space
National Category
Other Engineering and Technologies Social and Economic Geography
Identifiers
urn:nbn:se:hig:diva-26166 (URN)10.1177/2399808318792268 (DOI)2-s2.0-85052568351 (Scopus ID)
Available from: 2018-02-18 Created: 2018-02-18 Last updated: 2018-09-24Bibliographically approved
Ma, D., Sandberg, M. & Jiang, B. (2017). A Socio-Geographic Perspective on Human Activities in Social Media. Geographical Analysis, 49(3), 328-342
Open this publication in new window or tab >>A Socio-Geographic Perspective on Human Activities in Social Media
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.

National Category
Other Social Sciences Other Civil Engineering
Identifiers
urn:nbn:se:hig:diva-24868 (URN)10.1111/gean.12122 (DOI)000405108800004 ()2-s2.0-85011710648 (Scopus ID)
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
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2337-2486

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