Vector length of common boundaries: Difference between revisions
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We want only one category per boundary, that's why the sides check is needed (a boundary may consist of several pieces) | We want only one category per boundary, that's why the sides check is needed (a boundary may consist of several pieces) | ||
So we create a new attribute table and link it to the new layer 2 of the vector map: | So we create a new attribute table and link it to the new layer 2 of the vector map: | ||
v.db.addtable sids_nc2 layer=2 col="left integer,right integer,length integer" | {{cmd|v.db.addtable}} sids_nc2 layer=2 col="left integer,right integer,length integer" | ||
Now we query the polygon/boundary relationships and store it into the attribute table linked to layer 2: | Now we query the polygon/boundary relationships and store it into the attribute table linked to layer 2: | ||
v.to.db map=sids_nc2 option=sides col=left,right layer=2 | {{cmd|v.to.db}} map=sids_nc2 option=sides col=left,right layer=2 | ||
Now we have unique categories for the boundaries and can calculate the lengths: | Now we have unique categories for the boundaries and can calculate the lengths: | ||
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See the new attribute table containing the boundary lengths: | See the new attribute table containing the boundary lengths: | ||
v.db.select sids_nc2 layer=2 | {{cmd|v.db.select}} sids_nc2 layer=2 | ||
Verification (let's check boundary #193): | Verification (let's check boundary #193): | ||
d.vect sids_nc2 cat=193 layer=2 col=red type=boundary | {{cmd|d.vect}} sids_nc2 cat=193 layer=2 col=red type=boundary | ||
d.zoom | {{cmd|d.zoom}} | ||
d.measure | {{cmd|d.measure}} | ||
# LEN: 12756.00 meters | # LEN: 12756.00 meters | ||
What does the attribute table say: | What does the attribute table say: | ||
v.db.select sids_nc2 layer=2 | grep '^193' | {{cmd|v.db.select}} sids_nc2 layer=2 | grep '^193' | ||
190|65|68|12814 | 190|65|68|12814 | ||
Revision as of 10:13, 14 December 2010
Q: Any way to get the length of common boundaries shared by neighbouring areas in a map?
A:'
Sample data set: sudden infant deaths data from North Carolina, data imported from SHAPE file with v.in.ogr.
Let's have a look at the data:
d.mon x0 d.vect sids_nc
We add a second layer to the map which references the boundaries of polygons. In the vector geometry we generate an ID (category) for each boundary:
v.category sids_nc out=sids_nc2 layer=2 type=boundary option=add
Underlying idea: we'll fetch the IDs (categories) of the polygons left and right from each boundary and store it into the attribute table linked to layer 2. In general:
# cat_of_boundary | cat_of_left_polygon | cat_of_right_polygon | length_of_boundary
We want only one category per boundary, that's why the sides check is needed (a boundary may consist of several pieces) So we create a new attribute table and link it to the new layer 2 of the vector map:
v.db.addtable sids_nc2 layer=2 col="left integer,right integer,length integer"
Now we query the polygon/boundary relationships and store it into the attribute table linked to layer 2:
v.to.db map=sids_nc2 option=sides col=left,right layer=2
Now we have unique categories for the boundaries and can calculate the lengths:
v.to.db map=sids_nc2 option=length col=length layer=2
Done.
See the new attribute table containing the boundary lengths:
v.db.select sids_nc2 layer=2
Verification (let's check boundary #193):
d.vect sids_nc2 cat=193 layer=2 col=red type=boundary d.zoom d.measure # LEN: 12756.00 meters
What does the attribute table say:
v.db.select sids_nc2 layer=2 | grep '^193'
190|65|68|12814
This is reasonably close since on screen digitization in d.measure isn't always that precise ...