A Review of current methods to generate synthetic spatial microdata using reweighting and future directions

摘要: Abstract Synthetic spatial microdata enable analyses of artificial populations in the form individual unit record files at a small area level. They allow estimates variables that are otherwise not available this level, while preserving confidentiality personal data. This type data has mainly been used to provide more detailed census and for microsimulation modelling: example analyse social policy population changes, transportation, marketing strategies or health outcomes. We argue many potential applications synthetic remain be developed. One reason is lack information about confidence Introductory literature creating discussions on decisions need taken during generation process rare. In paper, we therefore review currently existing methods generate manner which will support most readers who considering approach, address main issues with regards neighbourhood level discuss further possible these importance microdata.