Disaggregation of National Level Population Projections to Municipality Level Using a Neural Network Approach
Orlando Olaya Bucaro1, Andrea Tamburini
1International Institute for Applied Systems Analysis
Population projections for small geographical areas are challenging even when data availability is good. Despite the presence of register data in Norway the current municipality level population projections by Statistics Norway are not satisfactory and are in the process of being replaced from a cohort-component framework to microsimulation. We propose a simpler and generalizable approach for downscaling national level population projections into municipality level projections, leveraging Norwegian register data and other data sources using an innovative neural network-based machine learning model. An additional advantage of this downscaling approach is that additional dimensions can easily be added to sub-national projections. We show this by disaggregating the Wittgenstein Centre population projections. The machine learning model is also trained by categorizing municipalities by special economic activities that might affect the population structure in that area. Such activities are the presence of fish farming, oil production, universities, or a high concentration of agricultural production.