Learning functional compositions of urban spaces with crowd-augmented travel survey data

Regions in urban environments often afford a mixture of different utilities. Their identification allows urban planners to leverage important insights on the emerging functional dynamics of cities. With the increasing availability of human mobility data and other forms of online digital breadcrumbs, we can now characterize urban regions with multi-source features. In this work, we form a comprehensive view of urban regions by fusing features depicting their temporal, spatial, and demographic aspects. Aggregating 47K explicitly stated trip purposes into their respective destination regions, we obtain multi-dimensional ground-truths on the functionalities of urban spaces. Given fused features and training labels, we can perform supervised learning, via multi-output regression, to estimate the functional composition of urban spaces. With 14 functional dimensions, our approach using crowd-augmented travel survey predictors delivers a mean absolute error of 3.9, approximately half of the error resulting from a mean-based straw man approach (mean absolute error of 7.9). Clustering estimated regional functionalities, we find highly coherent cluster assignments (adjusted Rand Index of 0.81) compared to clustering directly on regional functionality labels. Finally, we provide an illustrative case-study where clustering of estimated region functionalities can be used to intuitively differentiate prototypical spatial neighbourhoods of a large metropolitan.