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Challenges of working with a large building energy database. Combining data sets from different scales

Abstract:

One of the important issues in geographical analysis is the scale at which the analysis is performed, as different processes may operate at different levels of geographical aggregation in the data set. In this paper, we have used the lowest level of areal aggregation obtainable as the smallest unit of analysis, the single building. Even so, the case study used in this paper demonstrates that including higher levels of geographical aggregation simultaneously in a model of smaller units is essential to draw useful energy consumption conclusions from the data analysis. We use the top-down approach and the bottom-up approach to give a better description of the smallest unit of analysis. The top-down approach uses object representation learned from examples to detect an object in each input building and provide an approximation to its figure-attribute-ground representation from national surveys. The bottom-up approach uses an archetype-based criterion from local surveys to define coherent groups of buildings that are likely to belong together to an urban texture. The combination provides a final urban image that draws on the relative merits of both approaches. The result is as close as possible to the top-down approximation but is also constrained by the bottom-up process to be consistent with significant urban texture discontinuities. Our experiments seem to suggest that individual building extended archetype records derived from an augmentation algorithm are superior to results given by a pure top-down or pure bottom-up approaches.