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Head/tail breaks for visualization of city structure and dynamics
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management.ORCID iD: 0000-0002-2337-2486
2015 (English)In: Cities, ISSN 0264-2751, E-ISSN 1873-6084, Vol. 43, 69-77 p.Article in journal (Refereed) Published
Abstract [en]

The things surrounding us vary dramatically, which implies that there are far more small things than large ones, e.g., far more small cities than large ones in the world. This dramatic variation is often referred to as fractal or scaling. To better reveal the fractal or scaling structure, a new classification scheme, namely head/tail breaks, has been developed to recursively derive different classes or hierarchical levels. The head/tail breaks works as such: divide things into a few large ones in the head (those above the average) and many small ones (those below the average) in the tail, and recursively continue the dividing process for the large ones (or the head) until the notion of far more small things than large ones has been violated. This paper attempts to argue that head/tail breaks can be a powerful visualization tool for illustrating structure and dynamics of natural cities. Natural cities refer to naturally or objectively defined human settlements based on a meaningful cutoff averaged from a massive amount of units extracted from geographic information. To illustrate the effectiveness of head/tail breaks in visualization, I have developed several case studies applied to natural cities derived from the points of interest, social media location data, and time series nighttime images. I further elaborate on head/tail breaks related to fractals, beauty, and big data.

Place, publisher, year, edition, pages
2015. Vol. 43, 69-77 p.
Keyword [en]
Big data; Fractals; Ht-index; Natural cities; Nighttime images; Social media
National Category
Computer and Information Science
URN: urn:nbn:se:hig:diva-19065DOI: 10.1016/j.cities.2014.11.013ISI: 000349427400008ScopusID: 2-s2.0-84917677363OAI: oai:DiVA.org:hig-19065DiVA: diva2:792071
Available from: 2015-03-03 Created: 2015-03-03 Last updated: 2016-06-30Bibliographically approved

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Jiang, Bin
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