How do c-squares compare with bounding rectangles (minimum bounding rectangles, MBRs) for representing spatial extents?
Minimum bounding rectangles (MBRs (also known as bounding rectangles or bounding coordinates) are frequently used to represent dataset spatial extents because they are relatively easy to construct, store, exchange, and query. However they suffer from being a “good fit” to only a small subset of potential dataset footprints, since in the real world these are frequently irregular in shape, or regular but not aligned with parallels of latitude and longitude, or fragmented, or include holes (for example marine data around an island or continent) which the MBR method of representation does not cater for. C-squares has been designed specifically as an improvement over the MBR method of representation, to greatly eliminate the “false positives” encountered when a “search” rectangle intersects a portion of a “data” rectangle which does not, in fact, contain any data.
