“Solar power is contagious. These maps show how it spreads.” (from Vox)

Brad Plumer at Vox writes on take-up patterns of rooftop solar based upon a large dataset from SolarCity. The full article is available at the SolarCity site.

Mr Plumer combines it with a report on other studies of solar adoption patterns. Quoting the Abstract of one, by Graziano and Gillingham:

The diffusion of new technologies is often mediated by spatial and socioeconomic factors. This article empirically examines the diffusion of an important renewable energy technology: residential solar photovoltaic (PV) systems. Using detailed data on PV installations in Connecticut, we identify the spatial patterns of diffusion, which indicate considerable clustering of adoptions. This clustering does not simply follow the spatial distribution of income or population. We find that smaller centers contribute to adoption more than larger urban areas, in a wave-like centrifugal pattern. Our empirical estimation demonstrates a strong relationship between adoption and the number of nearby previously installed systems as well as built environment and policy variables. The effect of nearby systems diminishes with distance and time, suggesting a spatial neighbor effect conveyed through social interaction and visibility. These results disentangle the process of diffusion of PV systems and provide guidance to stakeholders in the solar market.

These are examples of spatio-temporal point patterns and they exhibit a kind of diffusion process. These are subjects of very interesting scholarly and computational work which I have studied but have not, as yet, had the opportunity to apply professionally or in my environmental (pro bono) consulting. I look forward to the day when I can.

Two references:

• P. J. Diggle, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, 3^rd edition, CRC Press, Chapman & Hall, 2014. Point pattern analysis is well supported by packages written for the programming language, R, which I use heavily for statistical analysis and data science, including `big data’ work.
• D. Stauffer, Am Aharony, Introduction to Percolation Theory, revised 2^nd edition, CRC Press, 1994.