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 pattems 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 pattems,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.