Using street based metrics to characterize urban typologies

Abstract

The urban spatial structure reflexes the local particularities produced during the historical 25 development of a city. Currently high spatial resolution imagery and LiDAR data are used to 26 derive numerical attributes to characterize the intra-urban structure and morphology. The urban-27 block boundaries have been frequently used to define the units to extract metrics from the 28 remotely sensed data. In this paper, we propose to complement those metrics with a set of 29 descriptors of the streets surrounding the urban blocks that numerically characterize the 30 geometry, presence of vegetation, and relationship with buildings. To carry out this purpose we 31 also introduce a methodology to define the street area related with an urban block from which 32 derive the urban metrics referred to the street. The assessment of these metrics is fulfilled using 33 one-way ANOVA procedure and decision trees classifier. These results reveal that street 34 metrics, and particularly those describing the street geometry, are suitable to enhance the 35 2 discrimination of complex urban typologies. Thus, the overall classification accuracy increases 36 from 72.7% to 81.1% when adding the street descriptors. The results of this study demonstrate 37 the usefulness of the metrics describing the street properties to complement the information 38 derived from the urban blocks and to improve the characterization of urban areas. 39 40 Highlights 41We propose a set of urban metrics to describe the streets with remotely sensed data 42 A methodology to relate the street space to urban blocks is defined 43Results show that street metrics are useful to improve the characterization of cities 44 45

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