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Spatially Balanced Sampling: A Review and A Reappraisal
Author(s) -
Benedetti Roberto,
Piersimoni Federica,
Postiglione Paolo
Publication year - 2017
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12216
Subject(s) - sampling (signal processing) , sampling design , simple random sample , computer science , benchmark (surveying) , spatial analysis , data mining , statistics , spatial design , econometrics , mathematics , geography , cartography , population , space (punctuation) , demography , filter (signal processing) , sociology , computer vision , operating system
Summary Spatially distributed data exhibit particular characteristics that should be considered when designing a survey of spatial units. Unfortunately, traditional sampling designs generally do not allow for spatial features, even though it is usually desirable to use information concerning spatial dependence in a sampling design. This paper reviews and compares some recently developed randomised spatial sampling procedures, using simple random sampling without replacement as a benchmark for comparison. The approach taken is design‐based and serves to corroborate intuitive arguments about the need to explicitly integrate spatial dependence into sampling survey theory. Some guidance for choosing an appropriate spatial sampling design is provided, and some empirical evidence for the gains from using these designs with spatial populations is presented, using two datasets as illustrations.