Abstract
Despite being a small country-state, electricity consumption in Singapore is said to be non-homogeneous, as exploratory data analysis showed that the distributions of electricity consumption differ across and within administrative boundaries and dwelling types. Local indicators of spatial association (LISA) were calculated for public housing postal codes using June 2016 data to discover local clusters of households based on electricity consumption patterns. A detailed walkthrough of the analytical process is outlined to describe the R packages and framework used in the R environment. The LISA results are visualized on three levels: country level, regional level and planning subzone level. At all levels we observe that households do cluster together based on their electricity consumption. By faceting the visualizations by dwelling type, electricity consumption of planning subzones can be said to fall under one of these three profiles: low-consumption subzone, high-consumption subzone and mixed-consumption subzone. These categories describe how consumption differs across different dwelling types in the same postal code (HDB block). LISA visualizations can guide electricity retailers to make informed business decisions, such as the geographical zones to enter, and the variety and pricing of plans to offer to consumers.
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Tan, Y.Y.J., Kam, T.S. (2019). Exploring and Visualizing Household Electricity Consumption Patterns in Singapore: A Geospatial Analytics Approach. In: Taylor, N., Christian-Lamb, C., Martin, M., Nardi, B. (eds) Information in Contemporary Society. iConference 2019. Lecture Notes in Computer Science(), vol 11420. Springer, Cham. https://doi.org/10.1007/978-3-030-15742-5_74
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DOI: https://doi.org/10.1007/978-3-030-15742-5_74
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