Thirapongphaiboon, T; Hanna, S; (2019) Spatial distribution of building use: recognition and prediction of use with machine learning. In: The 12th International Space Syntax Symposium. International Space Syntax Symposium: Beijing, China.

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Abstract

Spatial measures that can be applied at different scales, such as angular choice at specific radii, have been shown to effectively identify distinct and important spatial patterns at each scale. Foreground movement routes, local commercial centres, and identifiable urban regions have each be associated with different scales of such measures. This paper investigates and quantifies the degree to which the likelihood of use types of individual buildings can be determined based on spatial measures of the street segment graph alone, using supervised machine learning on a detailed dataset of buildings in London. A vector of 66 dimensions representing the spatial profile of each street segment is taken from analysis in DepthmapX and includes graph centrality measures such as choice at various radii, along with immediate features such as segment length. The proportion of building use is taken from OpenStreetMap and presented as a proportion of buildings in each category of Residential, Commercial or Business. A multilayer perceptron with between one and three hidden layers is trained to output the expected proportions of building use category given the various-dimensional spatial input vector for each segment. Various configurations of the training set and the network configuration are tested and discussed. Results of training indicate a best accuracy of approximately 85% with correlation coefficient of 0.696 for training set and 0.517 (moderately strong) for test set with 37-dimensional spatial input vector, suggesting that the spatial predictability of building use is comparable to that of other human factors such as movement. The spatial factors that appear most significant to the distribution of use types are variations in metric total length and metric mean depth SLW at medium metric radii. An analysis of the most significant dimensions of the input is given, to suggest a spectral spatial profile of each building use type, and the effectiveness of these in prediction or planning is discussed.

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