To clarify, I meant that if you have a origin/destination data of travel costs you would have to conflate that data to a polygonal dataset.
As an example, I just grabbed the roads file from Geocommons as a proxy for the travel cost with the very simplistic assumption that more roads results in lower travel costs. I agree that weighted graphs would be a better visualization and more commonly used to display OD types of data. sophia On Fri, Nov 7, 2008 at 3:29 PM, Christian Willmes <[EMAIL PROTECTED]> wrote: > sophia parafina schrieb: >> http://iaddemo.erdas.com/Map.png >> > very cool! :-) > > But I don't get this: >> If you want to do travel cost between cities you will need to conflate >> you values to a polygon layer of administrative boundaries, since the >> algorithm and scapetoad only works on polygons. So its definitely >> most doable. >> > How can I derive travel cost from roads per area? > Am I right, that you want to derive cost from the density distribution > of roads per area? > If the density between two points is low the cost is high and if the > density between two points is high the cost is low? So far so good. > But what if you have for example a highway through a very large area. > The points along this highway would be accessible far better than the > points in this area not connected directly to this one highway, but this > other points would get the same cost values to access them, because they > are in the same area unit. > So in the moment I think you cant make good assumptions about travel > cost from density distributions. Wheighted graphs are the better model > for that question I think, also for the visualization of it... ;-) > > regards > Christian > > _______________________________________________ > Geowanking mailing list > [email protected] > http://geowanking.org/mailman/listinfo/geowanking_geowanking.org > _______________________________________________ Geowanking mailing list [email protected] http://geowanking.org/mailman/listinfo/geowanking_geowanking.org
