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.
Highlights
Spatial planning and the allocation of urban resources need to be supported by accurate and dynamic urban information
In recent years, emerging automatically generated location data typified by smart card data, mobile phone data, and social media data has been widely explored for applications such as, for instance, detecting events [1,2], extracting population groups and their associated patterns [3,4], understanding human activity and mobility behaviors [5], redrawing communities and boundaries [6,7,8], inferring activity types and land uses [9], evaluating urban functionalities [10,11], and understanding the regularities of cities [12]
The results generated in our multilevel analysis, including unexplained patterns, can be aligned with the insights given in these works since we position our research in the field of urban analytics using emerging mobility data
Summary
Spatial planning and the allocation of urban resources (e.g., goods, infrastructure, services) need to be supported by accurate and dynamic urban information. A growing number of discussions have pointed out the drawbacks and consequences rooted in the nature of automatic data—the lack of demographic and contextual information [16,17], and the bias in sampling [18]. This leads to the salient research question behind our work: with inferred information, at what aggregated level can clear and meaningful spatial patterns be detected using geo-tweets in the context of urban analysis?
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
