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Contemporary integrated land-use transport models (ILUTMs) explicitly consider interactions between floorspace demand/supply and rent at fine spatial scales, which requires a good understanding between floorspace use pattern and competition of locations among socioeconomic activities. Floorspace use patterns are usually represented by space use coefficients (SUCs) by activity type by zone, which are then used to develop theoretical space-use-rent curves (SURCs), in order to reflect the elasticity between rent and floorspace consumption rates. Literature review indicates that existing studies mostly use borrowed SUCs or subjective judgement methods for synthesising base-year floorspace and developing SURCs. In general, their accuracy is largely unknown and synthesised floorspace could be highly inaccurate. In this study, a linear programming method is proposed to estimate localised SUCs by assuming that zonal population, employment and floorspace total data are available. Study results show that the method can provide localised SUCs and better SURCs than traditional methods. It is found that, as the size of the homogeneous optimisation areas (HOAs) decreases, the accuracy of zonal space totals estimated increases considerably. For example, the estimation error between the observed and estimated zonal space totals reduces from 76.2% under the most aggregate case to 24.7% under the most disaggregate case. The sum of square errors (SSEs) between the optimised SUCs and the SURCs also reduces to about one-quarter of their original values. The method proposed contributes to a procedural process to estimate localised SUCs with known accuracy, which is proved to be a better alternative to traditional synthesis methods.
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