Abstract
AbstractThis 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.
Highlights
Spatial interpolation is a widely applied method in geographical research
Some recent research has used some of many new sources of data as ancillary information from websites, portals, social media, check-ins, point of interest data, volunteered geographic information, and geo-tagged microblogs
This paper reviews and summarises the main approaches used in spatial interpolation of areal features
Summary
Spatial interpolation is a widely applied method in geographical research. It is a technique which uses sample values of known geographical points (or area units) to estimate (or predict) values at other unknown points (or area units). Some recent research has used some of many new sources of data as ancillary information from websites, portals, social media, check-ins, point of interest data, volunteered geographic information, and geo-tagged microblogs These have been driven by the increased use (and even ubiquity) of web, mobile, and GPS-enabled technologies. This paper reviews the major developments in spatial interpolation using areal features and considers future directions afforded by such new data sources It groups these into methods that do not use any ancillary information (e.g., simple areal weighting, pycnophylactic interpolation, and area-to-point interpolation), those that do (dasymetric, street-weighting methods, statistical and geostatistical approaches, and point-based informed approaches) and approaches using new data sources. Point interpolation constructs a surface that covers the study area using data recorded at a sample of locations In geostatistics this is commonly done with kriging.
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