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r.connectivity.distance bugfix #1091

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r.connectivity.distance bugfix #1091

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93e87c1
r.connectivity import ogr
ecodiv May 26, 2024
40ce90a
r.connectivity.distance bugfix
ecodiv May 31, 2024
33b5abe
Merge branch 'OSGeo:grass8' into r_connectivity
ecodiv May 31, 2024
18b7595
remove commented code
ecodiv May 31, 2024
e1c3cab
Merge branch 'r_connectivity' of github.com:ecodiv/grass-addons into …
ecodiv May 31, 2024
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91 changes: 56 additions & 35 deletions src/raster/r.connectivity/r.connectivity.distance/r.connectivity.distance.html
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Original file line number Diff line number Diff line change
@@ -1,32 +1,43 @@
<h2>DESCRIPTION:</h2>
<em>r.connectivity.distance</em> computes cost-distance between all

<em>r.connectivity.distance</em> computes cost-distances between all
areas (patches) of an input vector map within a user defined Euclidean
distance threshold.

<p>Recently, graph-theory has been characterised as an efficient and
useful tool for conservation planning (e.g. Bunn et al. 2000,
Calabrese &amp; Fagan 2004, Minor &amp; Urban 2008, Zetterberg et. al. 2010).</p>
<p>
Recently, graph-theory has been characterised as an efficient and
useful tool for conservation planning (e.g. Bunn et al. 2000, Calabrese
&amp; Fagan 2004, Minor &amp; Urban 2008, Zetterberg et. al. 2010).

<p>As a part of the r.connectivity.* tool-chain, <em>r.connectivity.distance</em> is
intended to make graph-theory more easily available to conservation
planning.</p>
<p>
As a part of the r.connectivity.* tool-chain,
<em>r.connectivity.distance</em> is intended to make graph-theory more
easily available to conservation planning.</p>

<p><em>r.connectivity.distance</em> is the first tool of the
r.connectivity.*-toolchain (followed by <em>r.connectivity.network</em>
<p>
<em>r.connectivity.distance</em> is the first tool of the
r.connectivity.*-toolchain (followed by <em>r.connectivity.network</em>
and <em>r.connectivity.corridors</em>).</p>

<p><em>r.connectivity.distance</em> loops through all polygons in the
<p>
<em>r.connectivity.distance</em> loops through all polygons in the
input vector map and calculates the cost-distance to all the other
polygons within a user-defined Euclidean distance threshold.</p>

<p>It produces two vector maps that hold the network:</p>
<p>
It produces two vector maps that hold the network:

<p>
<ul>
<li>an edge-map (connections between patches) and a</li>
<li>vertex-map (centroid representations of the patches).</li>
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<li>an edge-map (connections between patches) and a</li>
<li>vertex-map (centroid representations of the patches).</li>
<li>an edge-map (connections between patches) and,</li>
<li>a vertex-map (centroid representations of the patches).</li>

</ul>

<p>Attributes of the edge-map are:</p>
<table>
<p>
Attributes of the edge-map are:

<p>
<table border='1'>
<tr>
<td>cat</td>
<td>line category</td>
Expand All @@ -47,52 +58,62 @@ <h2>DESCRIPTION:</h2>
<td>cost-distance from from_patch to to_patch</td>
<td>double precision</td>
</tr>

</table>
<p>Attributes of the vertex-map are:</p>
<table>

<p>
Attributes of the vertex-map are:

<p>
<table border='1'>
<tr>
<td>cat</td>
<td>category of the input patches</td>
<td>integer</td>
</tr>
<tr>
<td>pop_proxy</td>
<td>the user defined population proxy to be used in further analysis,
representing a proxy for the amount of organisms potentially dispersing
from a patch (e.g. habitat area)</td>
<td>the user defined population proxy to be used<br>
in further analysis, representing a proxy for<br>
the amount of organisms potentially dispersing<br>
from a patch (e.g. habitat area)</td>
<td>double precision</td>
</tr>
</table>

<p>On user request (<b>p-flag</b>) the shortest paths between the possible
<p>
On user request (<b>p-flag</b>) the shortest paths between the possible
combination of patches can be extracted (using <em>r.drain</em>), along
with start and stop points.</p>
with start and stop points.

<p>In addition, <em>r.connectivity.distance</em> outputs a cost distance
raster map for every input area which later on are used in
<em>r.connectivity.corridors</em> (together with output from
<em>r.connectivity.network</em>) for corridor identification.</p>
<p>
In addition, <em>r.connectivity.distance</em> outputs a cost distance
raster map for every input area which later on are used in
<em>r.connectivity.corridors</em> (together with output from
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In addition, <em>r.connectivity.distance</em> outputs a cost distance
raster map for every input area which later on are used in
<em>r.connectivity.corridors</em> (together with output from
In addition, <em>r.connectivity.distance</em> outputs a cost distance
raster map for every input area. These are used later on in
<em>r.connectivity.corridors</em> (together with the output of

<em>r.connectivity.network</em>) for corridor identification.

<p>Distance between patches is measured as border to border distance. With
<p>
Distance between patches is measured as border to border distance. With
the <b>border_dist</b> option, the user can define the number of cells
(n) along the border to be used for distance measuring.<br>
(n) along the border to be used for distance measuring.

<p>
The distance from a (start) patch to another (end) is measured as the
n-th closest cell on the border of the other (end) patch. An increased
number of border cells used for distance measuring also increases the
width of possible corridors computed with
<em>r.connectivity.corridors</em> later on.</p>
<em>r.connectivity.corridors</em> later on.

<p>If an output directory is given for the <b>conefor_dir</b> option is
specified, also output suitable for further processing in
<a href="http://www.conefor.org">CONEFOR</a> will be produced, namely:</p>
<a href="http://www.conefor.org">CONEFOR</a> will be produced, namely:
<ul>
<li>a node file</li>
<li>a directed connection file, and</li>
<li>an undirected connection file</li>
</ul>

<h2>EXAMPLES</h2>

The following example is based on the North Carolina dataset!
<p><em>Please be aware that all input parameters of the following example are
purely hypothetical (though they intend to imitate a real life
Expand All @@ -107,9 +128,9 @@ <h2>EXAMPLES</h2>
the borders are no suitable habitats.<br>
It is not the most mobile of species and can cover (under optimal
conditions) maximal 1.5 km.
</p>

<h3>Prepare input data</h3>

Before we can run the connectivity analysis with the r.connectivity.*-tools
we need to prepare the example input data. Because we want to use
cost distance as a distance measure we have to provide a cost raster map
Expand Down Expand Up @@ -137,10 +158,7 @@ <h3>Create input patch vector map</h3>
landuse96_28m==11,1,null())"

# Vectorize patches
r.to.vect input=patches output=patches feature=area

# Add a column for the population proxy (in this case area in hectares)
v.db.addcolumn map=patches layer=1 columns="area_ha double precision"
r.to.vect input=patches output=patches type=area

# Upload area to attribute table (later used as population proxy)
v.to.db map=patches type=point,line,boundary,centroid layer=1 qlayer=1 \
Expand Down Expand Up @@ -190,9 +208,10 @@ <h3>Create a cost raster:</h3>
18 = 28 #Mixed Hardwoods/Conifers (1*resolution (28m))
20 = 42 #Water Bodies (1,5*resolution (28m))
21 = 84 #Unconsolidated Sediment (3*resolution (28m))' | r.reclass \
input=landuse96_28m output=costs rules=- --overwrite
input=landuse96_28m output=costs rules=-
</pre></div>

<p>
<div style="margin: 10px">
<a href="r_connectivity_distance_costs.png">
<img src="r_connectivity_distance_costs.png" width="600" height="600"
Expand Down Expand Up @@ -221,6 +240,7 @@ <h3>Create the network</h3>
conefor_dir=./conefor
</pre></div>

<p>
<div style="margin: 10px">
<a href="r_connectivity_distance_shortest_paths.png">
<img src="r_connectivity_distance_shortest_paths.png" width="600"
Expand All @@ -230,6 +250,7 @@ <h3>Create the network</h3>
represented by shortest paths and patch areas, produced in the example above.</i>
</div>

<p>
<div style="margin: 10px">
<a href="r_connectivity_distance_network.png">
<img src="r_connectivity_distance_network.png" width="600" height="600"
Expand Down
39 changes: 20 additions & 19 deletions src/raster/r.connectivity/r.connectivity.distance/r.connectivity.distance.py
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Expand Up @@ -550,12 +550,14 @@ def main():
"v.db.addtable",
quiet=True,
map=shortest_paths,
columns="cat integer,\
from_p integer,\
to_p integer,\
dist_min double precision,\
dist double precision,\
dist_max double precision",
columns=[
"cat integer",
"from_p integer",
"to_p integer",
"dist_min double precision",
"dist double precision",
"dist_max double precision",
],
)

start_region_bbox = Bbox(
Expand Down Expand Up @@ -790,7 +792,9 @@ def main():
to_patch_ids = vpatch_ids[vpatch_ids["cat"] == int(to_cat)]["vid"]

if len(to_patch_ids) == 1:
to_centroid = Centroid(v_id=to_patch_ids, c_mapinfo=vpatches.c_mapinfo)
to_centroid = Centroid(
v_id=to_patch_ids[0], c_mapinfo=vpatches.c_mapinfo
)
to_x = to_centroid.x
to_y = to_centroid.y
elif len(to_patch_ids) >= 1:
Expand Down Expand Up @@ -868,12 +872,14 @@ def main():
stderr=subprocess.PIPE,
output="{}_{}_cp".format(TMP_PREFIX, cat),
separator=",",
columns="x double precision,\
y double precision,\
to_p integer,\
dist_min double precision,\
dist double precision,\
dist_max double precision",
columns=[
"x double precision",
"y double precision",
"to_p integer",
"dist_min double precision",
"dist double precision",
"dist_max double precision",
],
)
sp.stdin.write(grass.encode("\n".join(to_coords)))
sp.stdin.close()
Expand All @@ -897,12 +903,7 @@ def main():
"v.db.addtable",
map=cost_paths,
quiet=True,
columns="cat integer,\
from_p integer,\
to_p integer,\
dist_min double precision,\
dist double precision,\
dist_max double precision",
columns=["cat integer", "from_p integer", "to_p integer"],
)
grass.run_command(
"v.db.update",
Expand Down