Data from: On the use of random graphs in analysing resource utilization in urban systems
Cite this dataset
Arbabi, Hadi et al. (2020). Data from: On the use of random graphs in analysing resource utilization in urban systems [Dataset]. Dryad. https://doi.org/10.5061/dryad.kwh70rz0n
Urban resource models increasingly rely on implicit network formulations. Resource consumption behaviours documented in the existing empirical studies are ultimately by-products of the network abstractions underlying these models. Here we present an analytical formulation and examination of a generic demand-driven network model that accounts for the effectiveness of resource utilisation and its implications for policy levers in addressing resource management in cities. We establish simple limiting boundaries to systems' resource effectiveness. These limits are found not to be a function of system size and to be simply determined by the system's average ability to maintain resource quality through its transformation processes. We also show that resource utilisation in itself does not enjoy considerable size efficiencies with larger and more diverse systems only offering increased chances of finding matching demand and supply between existing sectors in the system.
Data from Monte Carlo simulations. Source code for regenarating the dataset can be found on https://github.com/cip15ha/randomgraph-resource-utilisation
Dataset is provided as a hdf5 encoded table. Jupyter notebooks for processing of the dataset can be found on https://github.com/cip15ha/randomgraph-resource-utilisation
Engineering and Physical Sciences Research Council, Award: EP/N010019/1
Engineering and Physical Sciences Research Council, Award: EP/R013411/1