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<big>Steve,<br>
<br>
Adding viewsheds to the package would certainly up the computing
costs; I was wondering if you had a limit to what sort of
processing power you've got there. ;-)<br>
<br>
I also think what you're proposing might be interesting, but you
have to be care</big><big>ful about what conclusions you can draw
from it. At what point does the cost due to gradient variations
become insignificant to the overall cost of a route for a
particular type of vehicle? For a trucker on an interstate highway
it doesn't signify because the statistical noise of factors such
high speeds and short driving time balanced against the higher
price of fuel, services and road freight taxes completely
overwhelms the cost factor contributed by the change in gradients.
So in those cases you'd be computing numbers but not saying
anything.<br>
<br>
A different scenario, where gradient /is/ a significant factor,
would be a three-day 100 mile bike ride event through the
mountains (like the 'Ride the Rockies' event they hold around here
every year.) The power that bicyclists can produce is so low that
speeds and endurance are strongly affected by grades. But a
bicyclist doesn't typically operate on the scale of the nation so
applying the calculations to the entire TIGER file is overkill.
Also, the bicyclist operates on such a large scale that the source
data you're using to calculate gradient (30m DEM) may be too
coarse to be reliable on the bicyclist's scale.<br>
<br>
I'm not saying it isn't worth doing, I'm just saying you'll need
to qualify the precision of your results before you can say much
about applying this to any real-world problems.<br>
<br>
- Bill Thoen<br>
<br>
</big><br>
On 9/13/2010 5:28 PM, Stephen Woodbridge wrote:
<blockquote cite="mid:4C8EB386.2060706@swoodbridge.com" type="cite">Bill,
<br>
<br>
Thanks for the ideas. I might try to do something with the
viewshed idea in the future. It would need a LOT of computing to
process all the road segments in a National dataset like Tiger.
<br>
<br>
But for now I would like to figure out the routing costs.
<br>
<br>
One idea I had was to compute the grade for a segment and then
compute cost as:
<br>
<br>
cost = (time or distance) * scalefactor * max(abs(grade), 1.0)
<br>
<br>
This would have the effect of causing segments with a lot of grade
to have a higher cost of traversal.
<br>
<br>
Or similarly, if you want to pick roads with a lot of elevation
changes then use cost factor like:
<br>
<br>
cost = (time or distance) * scalefactor /
<br>
abs(sum_elevation_changes_over_the_segment)
<br>
<br>
This would have the effect of decreasing the traversal cost for
segments that have a lot of elevation changes.
<br>
<br>
These are pretty crude estimates and probably would need some fine
tuning to get reasonable results.
<br>
<br>
Thanks,
<br>
-Steve W
<br>
<br>
On 9/13/2010 4:24 PM, Bill Thoen wrote:
<br>
<blockquote type="cite">Stephen Woodbridge wrote:
<br>
<blockquote type="cite">Hi all,
<br>
<br>
(This is cross posting from the pgrouting list, sorry for the
dups.)
<br>
<br>
I have preprocessed some shapefile data and added elevation
<br>
information in the Z value of the coordinates. I'm wondering
how to
<br>
best utilize that in routes and would like any thoughts or
ideas you
<br>
might be willing to share.
<br>
<br>
The obvious answer is to wrap the elevation data into the cost
values
<br>
as this is simple and straight forward and does not require
code
<br>
changes. This brings me to what have other people done or
thought
<br>
about doing in this regard?
<br>
</blockquote>
Since you seem to enjoy large database problems, have you
considered
<br>
loading the DEM data together with the roads and sample the
viewshed
<br>
every few km? You could then create an objective cost factor for
<br>
"scenic," proportional to the amount of land visible, with some
<br>
adjusting factor that distinguishes morphology, land cover, or
other
<br>
weighted factors from each sample point. Creating a scale of
"scenic"
<br>
and "picturesque" as it goes form "ho-hum flatland" to
"precipitous,
<br>
brake-burning, wheel-gripping adventurous" might be fun all by
itself.
<br>
<br>
If you're looking for 3D ideas, there's a GIS consulting company
across
<br>
the hall from me that specializes in 3D information,
visualization and
<br>
analysis, and I know they are working on web services to deliver
the
<br>
sort of data that an application like yours would consume. Their
website
<br>
is full of 3D imagery, articles and examples that you might want
to
<br>
check out for ideas or inspiration There's a particularly good
<br>
demonstration of using fog instead of shadow to create a visual
<br>
representation of ridge lines, if your 're using those to
determine a
<br>
topographic index (see <a class="moz-txt-link-freetext" href="http://ctmap.com/serendipity/index.php">http://ctmap.com/serendipity/index.php</a>).
<br>
<br>
*Bill Thoen*
<br>
GISnet - <a class="moz-txt-link-abbreviated" href="http://www.gisnet.com">www.gisnet.com</a> <a class="moz-txt-link-rfc2396E" href="http://www.gisnet.com/"><http://www.gisnet.com/></a>
<br>
1401 Walnut St., Suite C
<br>
Boulder, CO 80302
<br>
303-786-9961 tel
<br>
303-443-4856 fax
<br>
<a class="moz-txt-link-abbreviated" href="mailto:bthoen@gisnet.com">bthoen@gisnet.com</a>
<br>
<br>
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<div class="moz-signature">-- <br>
<b>Bill Thoen</b><br>
GISnet - <a class="moz-txt-link-abbreviated" href="http://www.gisnet.com">www.gisnet.com</a><br>
303-786-9961<br>
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