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Ren, Zheng
Publications (2 of 2) Show all publications
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, 2019
Big data, City-size distribution, Human activities, Scaling, Twitter, US building footprints
National Category
Civil Engineering Other Natural Sciences
urn:nbn:se:hig:diva-30544 (URN)10.3390/ijgi8050200 (DOI)000470965400001 ()2-s2.0-85066441533 (Scopus ID)
Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2020-01-29Bibliographically 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
Organic worldview, topological representation, tweet locations, natural cities, scaling of geographic space
National Category
Social and Economic Geography
urn:nbn:se:hig:diva-26177 (URN)10.1080/13658816.2018.1427754 (DOI)000459561600007 ()2-s2.0-85041331898 (Scopus ID)
Swedish Research Council Formas, FR-2017/0009
Available from: 2018-02-22 Created: 2018-02-22 Last updated: 2019-08-12Bibliographically approved

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