Using network analyst to calculate effort of walking uphill along set path network
Hello,
I am working of similar workflows and I wonder if anyone could help me with my analysis.
I have been asked to calculate distance (along the path network) from the water source to each household and the effort of walking from the water source to each household for a small community in Uganda. Effort is related to the additional difficulty of walking from the water source, at the bottom of a valley, to each household, all of them in the mountain slopes, carrying cans full of water.
To do the analysis I have the following datasets:
*location of water source and households (as point feature classes)
*path network which have been digitised as line feature class. Before creating the network dataset I segmented the lines at 30 m intervals and extract the elevation value (from SRTM 30m DEM) at both the start and end nodes of each line segment. Each line segment in my network has the following fields: start and end elevation, elevation change and slope. The path network represent the routes followed from the water source to each household.
Using the OD Cost Matrix I have calculated the lenght (distance) from the water source to each hosehold however, I am not able to figure out how to use use other attributes of my network. For examples, being able to aggregate along each route the elevation change of perhaps the elevation (e.g. of the start node) to use it to calculate the effort.
(1) I would be very grateful if someone could advice on a good way to estimate effort from the data I have or whether I would need additional data or look into other type of analysis.
(2) I wonder if someone could point me to good help pages or resources where I can see how to create my network and/or how to set up the OD Cost Matrix layer parameters. I also would like to be able to use parameters such as elevation or time to 'quantify' proximity of household to water source. I am including a screenshot of my OD Cost Matrix Layer where the Mode is grey out.
Thank you in advance.
