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Profiling the spatial structure of London: From individual tweets to aggregated functional zones

Zhong, C; Zeng, S; Tu, W; Yoshida, M; (2018) Profiling the spatial structure of London: From individual tweets to aggregated functional zones. ISPRS International Journal of Geo-Information , 7 (10) , Article 386. 10.3390/ijgi7100386. Green open access

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Abstract

Knowledge discovery about people and cities from emerging location data has been an active research field but is still relatively unexplored. In recent years, a considerable amount of work has been developed around the use of social media data, most of which focusses on mining the content, with comparatively less attention given to the location information. Furthermore, what aggregated scale spatial patterns show still needs extensive discussion. This paper proposes a tweet-topic-function-structure framework to reveal spatial patterns from individual tweets at aggregated spatial levels, combining an unsupervised learning algorithm with spatial measures. Two-year geo-tweets collected in Greater London were analyzed as a demonstrator of the framework and as a case study. The results indicate, at a disaggregated level, that the distribution of topics possess a fair degree of spatial randomness related to tweeting behavior. When aggregating tweets by zones, the areas with the same topics form spatial clusters but of entangled urban functions. Furthermore, hierarchical clustering generates a clear spatial structure with orders of centers. Our work demonstrates that although uncertainties exist, geo-tweets should still be a useful resource for informing spatial planning, especially for the strategic planning of economic clusters.

Type: Article
Title: Profiling the spatial structure of London: From individual tweets to aggregated functional zones
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/ijgi7100386
Publisher version: https://doi.org/10.3390/ijgi7100386
Language: English
Additional information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited https://creativecommons.org/licenses/by/4.0/
Keywords: geo-tweets; spatial structure; urban functions; clustering; topic modelling
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10118712
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