No abstract

No abstract

Abstract. Many aggregate distributions of urban activities such as city sizes reveal scaling but hardly any work exists on the properties of spatial distributions within individual cities, notwithstanding considerable knowledge about their fractal structure. We redress this here by examining scaling relationships in a world city using data on the geometric properties of individual buildings. We first summaries how power laws can be used to approximate the size distributions of buildings, in analogy to city-size distributions which have been widely studied as rank-size and lognormal distributions following Zipf [1] and Gibrat [2]. We then extend this analysis to allometric relationships between buildings in terms of their different geometric size properties. We present some preliminary analysis of building heights from the Emporis database which suggests very strong scaling in world cities. The data base for Greater London is then introduced from which we extract 3.6 million buildings whose scaling properties we explore. We examine key allometric relationships between these different properties illustrating how building shape changes according to size, and we extend this analysis to the classification of buildings according to land use types. We conclude with an analysis of two-point correlation functions of building geometries which supports our non-spatial analysis of scaling.

The method behind the UK Display Energy Certificate (DEC) improves the comparability of benchmarking by accounting for variations in weather and occupancy. To improve the comparability further, the incorporation of other features that are intrinsic to buildings (e.g. built form and building services) deserve exploration. This study investigates the impact of these features and explores ways to improve further comparability in benchmarking the energy performance of schools. Statistical analyses of approximately 7700 schools were performed, followed by analyses of causal factors in 465 schools in greater detail using artificial neural networks (ANNs), each designed to understand and identify the factors that have significant impact on the pattern of energy use of schools. Changes in the pattern of energy use of schools have occurred over the past four years. This fact highlights issues associated with static benchmarks. A significant difference in energy performance between primary and secondary schools meant that it was necessary to re-examine the way non-domestic buildings are classified. Factors were identified as having significant impact on the pattern of energy use. The characteristics raise new possibilities for developing sectorspecific methods and improving comparability.