Spatial interpolation using areal features: a review of methods and opportunities using new forms of data with coded illustrations

This paper provides a high‐level review of different approaches for spatial interpolation using areal features. It groups these into those that use ancillary data to constrain or guide the interpolation (dasymetric, statistical, street‐weighted, and point‐based), and those do not but instead develop and refine allocation procedures (area to point, pycnophylactic, and areal weighting). Each approach is illustrated by being applied to the same case study. The analysis is extended to examine the opportunities arising from the many new forms of spatial data that are generated by everyday activities such as social media, check‐ins, websites offering services, microblogging sites, and social sensing, as well as intentional VGI activities, both supported by ubiquitous web‐ and GPS‐enabled technologies. Here, data of residential properties from a commercial website was used as ancillary data. Overall, the interpolations using many of the new forms of data perform as well as traditional, formal data, highlighting the analytical opportunities as ancillary information for spatial interpolation, and for supporting spatial analysis more generally. However, the case study also highlighted the need to consider the completeness and representativeness of such data. The R code used to generate the data, to develop the analysis and to create the tables and figures is provided.