Estimating Rooftop Solar Potential in Urban Environments: A Generalized Approach and Assessment of the Galápagos Islands

Presently, many island communities are largely dependent on fossil fuel resources for energy, leaving the abundance of renewable energy resources largely untapped. Although various solar energy potential modeling tools have been developed, most require high-resolution data, which do not presently exist for many developing countries or remote areas. Here, we calculate the potential of rooftop solar systems using low-cost, readily obtainable data and methods. This approach can be replicated by local communities and decision-makers to obtain an estimate of solar potential before investing in more detailed analysis. We illustrate the use of these methods on the two major urban centers on the Galápagos Islands (Ecuador), Puerto Baquerizo Moreno and Puerto Ayora. Our results show that a minimum of 21% and 27% of the total rooftop area must be covered with today's solar energy production technology to meet the current electricity demand of Puerto Baquerizo Moreno and Puerto Ayora, respectively. Additionally, the results demonstrate that Puerto Baquerizo Moreno has a higher production potential than Puerto Ayora, making it an attractive option for solar development that does not compete with the scarce land resources, most of which must be preserved as natural protected areas

The Bayesian Origins of Growth Rates in Stochastic Environments

Stochastic multiplicative dynamics characterize many complex natural phenomena such as selection and mutation in evolving populations, and the generation and distribution of wealth within social systems. Population heterogeneity in stochastic growth rates has been shown to be the critical driver of diversity dynamics and of the emergence of wealth inequality over long time scales. However, we still lack a general statistical framework that systematically explains the origins of these heterogeneities from the adaptation of agents to their environment. In this paper, we derive population growth parameters resulting from the interaction between agents and their knowable environment, conditional on subjective signals each agent receives. We show that average growth rates converge, under specific conditions, to their maximal value as the mutual information between the agent's signal and the environment, and that sequential Bayesian learning is the optimal strategy for reaching this maximum. It follows that when all agents access the same environment using the same inference model, the learning process dynamically attenuates growth rate disparities, reversing the long-term effects of heterogeneity on inequality. Our approach lays the foundation for a unified general quantitative modeling of social and biological phenomena such as the dynamical effects of cooperation, and the effects of education on life history choices.Comment: 12 pages, 4 figure

Identification of functional information subgraphs in complex networks

We present a general information theoretic approach for identifying functional subgraphs in complex networks where the dynamics of each node are observable. We show that the uncertainty in the state of each node can be expressed as a sum of information quantities involving a growing number of correlated variables at other nodes. We demonstrate that each term in this sum is generated by successively conditioning mutual informations on new measured variables, in a way analogous to a discrete differential calculus. The analogy to a Taylor series suggests efficient search algorithms for determining the state of a target variable in terms of functional groups of other degrees of freedom. We apply this methodology to electrophysiological recordings of networks of cortical neurons grown it in vitro. Despite strong stochasticity, we show that each cell's patterns of firing are generally explained by the activity of a small number of other neurons. We identify these neuronal subgraphs in terms of their mutually redundant or synergetic character and reconstruct neuronal circuits that account for the state of each target cell.Comment: 4 pages, 4 figure

Urban Scaling and Its Deviations: Revealing the Structure of Wealth, Innovation and Crime across Cities

With urban population increasing dramatically worldwide, cities are playing an increasingly critical role in human societies and the sustainability of the planet. An obstacle to effective policy is the lack of meaningful urban metrics based on a quantitative understanding of cities. Typically, linear per capita indicators are used to characterize and rank cities. However, these implicitly ignore the fundamental role of nonlinear agglomeration integral to the life history of cities. As such, per capita indicators conflate general nonlinear effects, common to all cities, with local dynamics, specific to each city, failing to provide direct measures of the impact of local events and policy. Agglomeration nonlinearities are explicitly manifested by the superlinear power law scaling of most urban socioeconomic indicators with population size, all with similar exponents (1.15). As a result larger cities are disproportionally the centers of innovation, wealth and crime, all to approximately the same degree. We use these general urban laws to develop new urban metrics that disentangle dynamics at different scales and provide true measures of local urban performance. New rankings of cities and a novel and simpler perspective on urban systems emerge. We find that local urban dynamics display long-term memory, so cities under or outperforming their size expectation maintain such (dis)advantage for decades. Spatiotemporal correlation analyses reveal a novel functional taxonomy of U.S. metropolitan areas that is generally not organized geographically but based instead on common local economic models, innovation strategies and patterns of crime

Multiple-Scale Analysis of the Quantum Anharmonic Oscillator

Conventional weak-coupling perturbation theory suffers from problems that arise from resonant coupling of successive orders in the perturbation series. Multiple-scale perturbation theory avoids such problems by implicitly performing an infinite reordering and resummation of the conventional perturbation series. Multiple-scale analysis provides a good description of the classical anharmonic oscillator. Here, it is extended to study the Heisenberg operator equations of motion for the quantum anharmonic oscillator. The analysis yields a system of nonlinear operator differential equations, which is solved exactly. The solution provides an operator mass renormalization of the theory.Comment: 12 pages, Revtex, no figures, available through anonymous ftp from ftp://euclid.tp.ph.ic.ac.uk/papers/ or on WWW at http://euclid.tp.ph.ic.ac.uk/Papers/papers_95-6_.htm