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Added basic description and images of spatial and knn queries

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[SVN r81540]
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Adam Wulkiewicz
2012-11-25 21:26:00 +00:00
parent 7babc43f75
commit 73e7974408
7 changed files with 144 additions and 5 deletions
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2
doc/html/geometry_index/r_tree/introduction.html
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@@ -201,7 +201,7 @@
<br><hr style="width:100; align:left;">
<div id="ftn.geometry_index.r_tree.introduction.f0" class="footnote"><p><a href="#geometry_index.r_tree.introduction.f0" class="para"><sup class="para">[1] </sup></a>
Guttman, A. (1984). <span class="emphasis"><em>R-Trees: A Dynamic Index Structure for Spatial
Searching</em></span> footnote
Searching</em></span>
</p></div>
<div id="ftn.geometry_index.r_tree.introduction.f1" class="footnote"><p><a href="#geometry_index.r_tree.introduction.f1" class="para"><sup class="para">[2] </sup></a>
Greene, D. (1989). <span class="emphasis"><em>An implementation and performance analysis

49
doc/html/geometry_index/r_tree/nearest_neighbours_queries.html
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@@ -37,6 +37,55 @@
<dt><span class="section"><a href="nearest_neighbours_queries.html#geometry_index.r_tree.nearest_neighbours_queries.using_spatial_predicates">Using
spatial predicates</a></span></dt>
</dl></div>
<p>
Nearest neighbours queries returns <code class="computeroutput"><span class="identifier">Value</span></code>s
which are closest to some point in space. Additionally it is possible to
pass distance predicates in order to define how the distance to the <code class="computeroutput"><span class="identifier">Value</span></code> should be calculated or minimal and
maximal distances. The examples of some knn queries may be found in the table
below. All queries returns 5 closest <code class="computeroutput"><span class="identifier">Values</span></code>.
The query point, region and result Values are orange.
</p>
<div class="informaltable"><table class="table">
<colgroup>
<col>
<col>
<col>
</colgroup>
<thead><tr>
<th>
<p>
Basic knn query
</p>
</th>
<th>
<p>
knn in a ring (Value's furthest point)
</p>
</th>
<th>
<p>
knn in a ring (Value's closest point)
</p>
</th>
</tr></thead>
<tbody><tr>
<td>
<p>
<span class="inlinemediaobject"><img src="../../../images/knn.png" alt="knn"></span>
</p>
</td>
<td>
<p>
<span class="inlinemediaobject"><img src="../../../images/knn_inters.png" alt="knn_inters"></span>
</p>
</td>
<td>
<p>
<span class="inlinemediaobject"><img src="../../../images/knn_cover.png" alt="knn_cover"></span>
</p>
</td>
</tr></tbody>
</table></div>
<div class="section">
<div class="titlepage"><div><div><h4 class="title">
<a name="geometry_index.r_tree.nearest_neighbours_queries.k_nearest_neighbours"></a><a class="link" href="nearest_neighbours_queries.html#geometry_index.r_tree.nearest_neighbours_queries.k_nearest_neighbours" title="k nearest neighbours">k

70
doc/html/geometry_index/r_tree/spatial_queries.html
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@@ -32,6 +32,76 @@
<dt><span class="section"><a href="spatial_queries.html#geometry_index.r_tree.spatial_queries.spatial_predicates">Spatial
predicates</a></span></dt>
</dl></div>
<p>
Spatial queries returns <code class="computeroutput"><span class="identifier">Value</span></code>s
which meets some predicates. For instance it may be used to retrieve Values
intersecting some area or are within some other area. The examples of some
basic queries may be found in the table below. The query region and result
<code class="computeroutput"><span class="identifier">Value</span></code>s are orange.
</p>
<div class="informaltable"><table class="table">
<colgroup>
<col>
<col>
<col>
<col>
<col>
</colgroup>
<thead><tr>
<th>
<p>
intersects (default)
</p>
</th>
<th>
<p>
covered_by
</p>
</th>
<th>
<p>
disjoint
</p>
</th>
<th>
<p>
overlaps
</p>
</th>
<th>
<p>
within
</p>
</th>
</tr></thead>
<tbody><tr>
<td>
<p>
<span class="inlinemediaobject"><img src="../../../images/intersects.png" alt="intersects"></span>
</p>
</td>
<td>
<p>
<span class="inlinemediaobject"><img src="../../../images/within.png" alt="within"></span>
</p>
</td>
<td>
<p>
<span class="inlinemediaobject"><img src="../../../images/disjoint.png" alt="disjoint"></span>
</p>
</td>
<td>
<p>
<span class="inlinemediaobject"><img src="../../../images/overlaps.png" alt="overlaps"></span>
</p>
</td>
<td>
<p>
<span class="inlinemediaobject"><img src="../../../images/within.png" alt="within"></span>
</p>
</td>
</tr></tbody>
</table></div>
<div class="section">
<div class="titlepage"><div><div><h4 class="title">
<a name="geometry_index.r_tree.spatial_queries.basic_queries"></a><a class="link" href="spatial_queries.html#geometry_index.r_tree.spatial_queries.basic_queries" title="Basic queries">Basic

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doc/html/index.html
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@@ -56,7 +56,7 @@
</div>
</div>
<table xmlns:rev="http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" width="100%"><tr>
<td align="left"><p><small>Last revised: November 25, 2012 at 20:52:29 GMT</small></p></td>
<td align="left"><p><small>Last revised: November 25, 2012 at 21:23:52 GMT</small></p></td>
<td align="right"><div class="copyright-footer"></div></td>
</tr></table>
<hr>

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doc/rtree/introduction.qbk
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@@ -11,7 +11,7 @@
[section Introduction]
__rtree__ is a tree data structure used for spatial searching. It was proposed by
Antonin Guttman in 1984 [footnote Guttman, A. (1984). /R-Trees: A Dynamic Index Structure for Spatial Searching/ footnote]
Antonin Guttman in 1984 [footnote Guttman, A. (1984). /R-Trees: A Dynamic Index Structure for Spatial Searching/]
as an expansion of B-tree for multi-dimensional data. It may be used to store points or volumetric data in order to
perform a spatial query later. This query may return objects that are inside some area or are close to some point in space.
@@ -26,8 +26,8 @@ the new node is created and elements are split between nodes. If the number of e
and elements are reinserted into the tree.
The __rtree__ is a self-balanced data structure. The key part of balancing algorithm is node splitting algorithm mentioned before
[footnote Greene, D. (1989). /An implementation and performance analysis of spatial data access methods/ ]
[footnote Beckmann, N.; Kriegel, H. P.; Schneider, R.; Seeger, B. (1990). /The R*-tree: an efficient and robust access method for points and rectangles/ ].
[footnote Greene, D. (1989). /An implementation and performance analysis of spatial data access methods/]
[footnote Beckmann, N.; Kriegel, H. P.; Schneider, R.; Seeger, B. (1990). /The R*-tree: an efficient and robust access method for points and rectangles/].
Each algorithm would produce different splits so the internal structure of a tree may be different for each one of them.
In general more complex algorithms analyses elements better and produces less overlapping nodes. This is a "better" split because
later, in the searching process less nodes must be traversed in order to find desired obejcts. On the other hand more complex analysis

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doc/rtree/nearest_query.qbk
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@@ -10,6 +10,17 @@
[section Nearest neighbours queries]
Nearest neighbours queries returns `Value`s which are closest to some point in space.
Additionally it is possible to pass distance predicates in order to define how the distance
to the `Value` should be calculated or minimal and maximal distances. The examples of some knn
queries may be found in the table below. All queries returns 5 closest `Values`.
The query point, region and result Values are orange.
[table
[[Basic knn query] [knn in a ring (Value's furthest point)] [knn in a ring (Value's closest point)]]
[[[$../images/knn.png]] [[$../images/knn_inters.png]] [[$../images/knn_cover.png]]]
]
[section k nearest neighbours]
There are three ways of performing knn queries. Following queries returns

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doc/rtree/spatial_query.qbk
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@@ -10,6 +10,15 @@
[section Spatial queries]
Spatial queries returns `Value`s which meets some predicates. For instance it may be used to
retrieve Values intersecting some area or are within some other area. The examples of some
basic queries may be found in the table below. The query region and result `Value`s are orange.
[table
[[intersects (default)] [covered_by] [disjoint] [overlaps] [within]]
[[[$../images/intersects.png]] [[$../images/within.png]] [[$../images/disjoint.png]] [[$../images/overlaps.png]] [[$../images/within.png]]]
]
[section Basic queries]
There are three ways to perform a spatial query. Following queries returns