Functional visibility graph analysis: Quantifying visuofunctional space with social media check-in data
Yao Shen, Zhìqiáng Wú
Abstract
Public space is characterised by visible spaces and featured by observable activities, forming the atmosphere that humans can perceive, interpret, and experience. This article conceptualises urban space as a set of viewsheds connecting functions visually which can be represented as functional visibility graphs – the graphs with mutual-visibility edges between space and function nodes. It begins with an introduction of three basic measures: visual function size, entropy, and mean angular depth step, then proposes advanced measures: namely visual function connectivity, function visibility, and visible function closeness, showing spatial narratives along paths, visual centrality in place, and functional regions for continuous areas, respectively. This framework is enabled by social media check-in data that records people’s engagements across function nodes tagged by them. An application in a real example in Tianjin City is shown. The novelty of using social media check-ins as a delegation of actual function usage is demonstrated for modelling urban movement with improved precision. By tracing the shifting performance of the functional visibility graphs for the same spatial layout, this study outlines how such analysis can be conducted for uncovering short-term transformation of the visual landscape, contributing to the fine-scale, high-resolution implementations of land use policies from a real human-focused perspective, with the socially sensed data.