Now you are attempting to get a reasonable association between nesting areas and feeding zones based on minimal distance criteria.

Suppose a zoological/ecological study: you know where are located the nesting areas of some rare water bird species, also the location of all rivers, lakes, ponds and marshlands in the area you are interested in.

Suppose while using a smartphone app: your current geographic location is well known by the integrated GPS sensor, so you are simply expecting that the app will immediately tell where you are (and the exact location of) the nearest fuel stations (hotels, restaurants etc).

Now imagine that for whatever reason you are interested in quickly identifying all features within a close spatial proximity to an arbitrary location. Imagine a set of arbitrary geometries which may well be, a very huge dataset containing some millions of features. What is the KNN ( K-Nearest Neighbors) problem ?

Except for this any other reference to a KNN table is now just a no-op without any practical effect.

Allowing to successfully execute a DROP TABLE knn SQL statement.

Avoiding any errors when a bVirtualKNN table created by a previous version of SpatiaLite is inadvertently queried.The intended scopes for such minimal support are:

It critically depended on a SQLite API ( sqlite3_rtree_query_callback) causing many architectural troubles and instabilities on Windows when SpatiaLite was dynamically loaded as an extension ( mod_spatialite), most notably when some kind of language connector was involved (Java, Python, PHP, C# dot NET and alike).įor historical compatibility reasons a minimal skeletal implementation of KNN still continues to be implemented, but it does absolutely nothing and always returns NULL responses.

KNN was rather slow, especially when processing huge datasets with million rows.

WARNING: the KNN module is now deprecated and no longer supported. Regione Toscana - Settore Sistema Informativo Territoriale ed Ambientale. Tuscany Region - Territorial and Environmental Information System Development of VirtualKNN has been entirely funded by

Download monte mario motel & efficiency