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

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

► Potential applications of synthetic spatial microdata are not fully exploited. ► We review current methods to generate synthetic spatial microdata. ► We address issues of spatial microsimulation models at the neighbourhood level. ► We discuss the increasing importance and potential of this type of data.