hig.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Jiang, Bin, ProfessorORCID iD iconorcid.org/0000-0002-2337-2486
Alternative names
Publications (10 of 107) Show all publications
Jiang, B. (2019). A recursive definition of goodness of space for bridging the concepts of space and place for sustainability. Sustainability, 11(15), Article ID 4091.
Open this publication in new window or tab >>A recursive definition of goodness of space for bridging the concepts of space and place for sustainability
2019 (English)In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 11, no 15, article id 4091Article in journal (Refereed) Published
Abstract [en]

Conceived and developed by Christopher Alexander through his life's work, The Nature of Order, wholeness is defined as a mathematical structure of physical space in our surroundings. Yet, there was no mathematics, as Alexander admitted then, that was powerful enough to capture his notion of wholeness. Recently, a mathematical model of wholeness, together with its topological representation, has been developed that is capable of addressing not only why a space is good, but also how much goodness the space has. This paper develops a structural perspective on goodness of space (both large- and small-scale) in order to bridge two basic concepts of space and place through the very concept of wholeness. The wholeness provides a de facto recursive definition of goodness of space from a holistic and organic point of view. A space is good, genuinely and objectively, if its adjacent spaces are good, the larger space to which it belongs is good, and what is contained in the space is also good. Eventually, goodness of space, or sustainability of space, is considered a matter of fact rather than of opinion under the new view of space: space is neither lifeless nor neutral, but a living structure capable of being more living or less living, or more sustainable or less sustainable. Under the new view of space, geography or architecture will become part of complexity science, not only for understanding complexity, but also for making and remaking complex or living structures. 

Place, publisher, year, edition, pages
MDPI AG, 2019
Keywords
Beauty, Cities, Head/tail breaks, Living structure, Scaling law, Streets
National Category
Civil Engineering
Identifiers
urn:nbn:se:hig:diva-30574 (URN)10.3390/su11154091 (DOI)000485230200096 ()2-s2.0-85070450249 (Scopus ID)
Funder
Swedish Research Council Formas, FR-2017/0009
Available from: 2019-08-23 Created: 2019-08-23 Last updated: 2019-10-11Bibliographically approved
Ren, Z., Jiang, B. & Seipel, S. (2019). Capturing and characterizing human activities using building locations in America. ISPRS International Journal of Geo-Information, 8(5), Article ID 200.
Open this publication in new window or tab >>Capturing and characterizing human activities using building locations in America
2019 (English)In: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 8, no 5, article id 200Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
MDPI AG, 2019
Keywords
Big data, City-size distribution, Human activities, Scaling, Twitter, US building footprints
National Category
Civil Engineering Other Natural Sciences
Identifiers
urn:nbn:se:hig:diva-30544 (URN)10.3390/ijgi8050200 (DOI)00470965400001 ()2-s2.0-85066441533 (Scopus ID)
Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22Bibliographically approved
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: 2019-08-12Bibliographically approved
Jiang, B. (2019). New Paradigm in Mapping: A Critique on Cartography and GIS. Cartographica, 54(3), 193-205
Open this publication in new window or tab >>New Paradigm in Mapping: A Critique on Cartography and GIS
2019 (English)In: Cartographica, ISSN 0317-7173, E-ISSN 1911-9925, Vol. 54, no 3, p. 193-205Article in journal (Refereed) Published
Abstract [en]

As noted in the epigraph, a map was long ago seen as the map of the map, the map of the map, of the map, and so on endlessly. This recursive perspective on maps, however, has received little attention in cartography. Cartography, as a scientific discipline, is essentially founded on Euclidean geometry and Gaussian statistics, which deal respectively with regular shapes and more or less similar things. It is commonly accepted that geographic features are not regular and that the Earth's surface is full of fractal or scaling or living phenomena: far more small things than large ones are found at different scales. This article argues for a new paradigm in mapping, based on fractal or living geometry and Paretian statistics, and – more critically – on the new conception of space, conceived and developed by Christopher Alexander, as neither lifeless nor neutral, but a living structure capable of being more living or less living. The fractal geometry is not limited to Benoit Mandelbrot's framework, but tends towards Christopher Alexander's living geometry and is based upon the third definition of fractal: A set or pattern is fractal if the scaling of far more small things than large ones recurs multiple times. Paretian statistics deals with far more small things than large ones, so it differs fundamentally from Gaussian statistics, which deals with more or less similar things. Under the new paradigm, I make several claims about maps and mapping: (1) the topology of geometrically coherent things – in addition to that of geometric primitives – enables us to see a scaling or fractal or living structure; (2) under the third definition, all geographic features are fractal or living, given the right perspective and scope; (3) exactitude is not truth – to paraphrase Henri Matisse – but the living structure is; and (4) Töpfer's law is not universal, but the scaling law is. All these assertions are supported by evidence, drawn from a series of previous studies. This article demands a monumental shift in perspective and thinking from what we are used to in the legacy of cartography and GIS. 

Place, publisher, year, edition, pages
University of Toronto Press, 2019
Keywords
fractal or living geometry, head/tail breaks (ht-index), scaling law, third definition of fractal wholeness
National Category
Earth and Related Environmental Sciences
Identifiers
urn:nbn:se:hig:diva-30757 (URN)10.3138/cart.54.3.2018-0019 (DOI)
Available from: 2019-10-09 Created: 2019-10-09 Last updated: 2019-10-09Bibliographically 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: 2019-08-12Bibliographically 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
Behnisch, M., Hecht, R., Herold, H. & Jiang, B. (2019). Urban big data analytics and morphology. Environment and Planning B: Urban Analytics and City Science, 46(7 (SI)), 1203-1205
Open this publication in new window or tab >>Urban big data analytics and morphology
2019 (English)In: Environment and Planning B: Urban Analytics and City Science, ISSN 2399-8083, Vol. 46, no 7 (SI), p. 1203-1205Article in journal, Editorial material (Refereed) Published
Place, publisher, year, edition, pages
Sage Publications, 2019
National Category
Other Social Sciences Other Engineering and Technologies
Identifiers
urn:nbn:se:hig:diva-30631 (URN)10.1177/2399808319870016 (DOI)000482057700001 ()2-s2.0-85071724449 (Scopus ID)
Available from: 2019-09-05 Created: 2019-09-05 Last updated: 2019-10-04Bibliographically approved
Ma, D., Omer, I., Osaragi, T., Sandberg, M. & Jiang, B. (2019). Why Topology Matters in Predicting Human Activities. Environment and Planning B: Urban Analytics and City Science, 46(7), 1297-1313
Open this publication in new window or tab >>Why Topology Matters in Predicting Human Activities
Show others...
2019 (English)In: Environment and Planning B: Urban Analytics and City Science, ISSN 2399-8083, Vol. 46, no 7, p. 1297-1313Article in journal (Refereed) Published
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
Sage Publications, 2019
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)000482057700007 ()2-s2.0-85052568351 (Scopus ID)
Available from: 2018-02-18 Created: 2018-02-18 Last updated: 2019-09-05Bibliographically 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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2337-2486

Search in DiVA

Show all publications