On 1/26/2012 10:47 AM, Nicolas Ribot wrote:
Hi All,
I have an interesting problem I am trying to solve and would love some
feedback on how to best go about it.
I have road data from two different vendor data sets. But this could also be
one road network and a GPS track, so I think this is a pretty common use
case.
Assumptions:
o the networks are similar, ie: they have similar roadway coverage
o the two sets might be slightly misaligned, ie: shifted by some amount
o the segments in the two data sets do not have to be broken into equivalent
segments, ie: one segment in A might be represented my multiple segments in
B
o segments are not aligned end point wise, ie: a segment in A might go from
mid-point one segment in B to the midpoint of a connected segment in B
o in many cases I will be working with a set of lines in one set that I need
to match to the other to select a matching set of lines.
So strategies for matching these:
1. take a segment from A and buffer it, then intersect the data in B and
select the longest intersected object. I can probably throw out any pieces
smaller than the buffer distance.
2. Do the same but buffer and union the set of lines into a single
multipolygon, and intersect that with the other set.
3. ??? Other ideas?
Thoughts on performance?
Typically I will have a small set (1-20) of segments to compare against a
larger (100K-2M) set. Obvious a spatial index will will be used. But I'm
wondering what is the fast way to do this matching computationally. I think
I will want to be able to compare 1-200 sets like this every 5 mins as data
comes in from a feed, while supporting other queries.
With this small input set, maybe st_hausdorffDistance() could be
useful to select good candidates in the larger set.
Hi Nicolas,
Thank you for your insights and suggestions. I have not used
st_hausdorffDistance() and just read up on it. I agree this might be a
good option for initial segment selection.
-Steve
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