Urban spatial structure analysis: quantitative identification of urban social functions using building footprints

Abstract

Analysis of urban spatial structures is an effective way to explain and solve increasingly serious urban problems. However, many of the existing methods are limited because of data quality and availability, and usually yield inaccurate results due to the unclear description of urban social functions. In this paper, we present an investigation on urban social function based spatial structure analysis using building footprint data. An improved turning function (TF) algorithm and a self-organizing clustering method are presented to generate the variable area units (VAUs) of high-homogeneity from building footprints as the basic research units. Based on the generated VAUs, five spatial metrics are then developed for measuring the morphological characteristics and the spatial distribution patterns of buildings in an urban block. Within these spatial metrics, three models are formulated for calculating the social function likelihoods of each urban block to describe mixed social functions in an urban block, quantitatively. Consequently, the urban structures can be clearly observed by an analysis of the spatial distribution patterns, the development trends, and the hierarchy of different social functions. The results of a case study conducted for Munich validate the effectiveness of the proposed method.

Graphical abstract

Keywords

urban spatial structure / variable area unit (VAU) / spatial metric / social function likelihood / OpenStreetMap

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Data and codes availability statement

The data and codes that support the findings of this study are available in repository “Github” with the identifier at the public link. The authors would like to thank Mr. Karimbahgat and his team for providing the Pyshp (shapefile.py) for reading and writing shapefile. They would also like to thank the OpenStreetMap (OSM) for providing the Munich building footprint data.

Acknowledgments

This research was funded by the National Key Research and Development Program of China (No. 2018YFB0505400), and the National Natural Science Foundation of China Project (Grant Nos. 42071370 and 41771484).

RIGHTS & PERMISSIONS

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