Outline of this video

• Downscaling is the way to get data if you don’t have data on your own local scale and you calculate data from a higher spatial scale to approximate it for your city.
• From the data gap analysis, you probably have identified a number of gaps, in a number of places, lifecycle stages or years. If there is no local data for stocks and flows, then we need to look to higher or larger spatial scales, like region, NUTS, country etc. and downscale, which means to scale it down to the city level with proxies;
• The approach of this module is a bit different: It is not very sensible that we present a number of good downscaling means, if you won’t actually use or need them. Therefore, we ask you that you let us know which data downscaling you need help with and we will support you in that.
• In this video we are giving you some broad tips and tricks. We’ve also summarised them in the downscaling part of the support document.
• M3:30, Process for downscaling, steps
• M6:14, Overview of proxies that can be used by lifecycle stage and in order of preference.
• M7:27, the general formula to downscale data.
• M11:00, examples and data sources