By Sean Fox, Felix Agyemang & Rashid Memon
As part of our ESRC-funded project Quantifying Cities for Sustainable Development, we’ve been working on creative solutions to a widespread problem: the lack of basic demographic and socioeconomic data in cities in low- and middle-income countries. This data deficit impedes evidence-based planning and policy. How can we get a handle on living conditions and economic activity in cities without traditional administrative and survey data?
In this project we explore the possibility of using energy data as a proxy for various socioeconomic variables at relatively high spatial resolution. To do this we examine the potential of georeferenced residential electricity meter data and night-time lights (NTL) data in the megacity of Karachi, Pakistan.
First, we use nationally representative survey data to establish a strong association between electricity consumption and household living standards. Second, we compare gridded radiance values from NTL data with a unique dataset containing georeferenced median monthly electricity consumption values for over 2 million individual households in the city. As the figure shows, these maps are similar, but diverge in important ways. Finally, we develop a model to explain intra-urban variation in radiance values using proxy measures of economic activity from Open Street Map. Overall, we find that NTL data are a poor proxy for living standards but do capture spatial variation in population density and economic activity. By contrast, electricity data are an excellent proxy for living standards and could be used more widely to inform policy and support poverty research in cities in low- and middle-income countries.