Cartographic model:

Create friction surface for the time of travel:

Create buffers around roads and highways, and combine both images into one.  Buffer around roads is 100 m, and around highways 200 m (this is necessary because smaller buffers did not produce appropriate results in later analysis).  Specify that highways and buffer have cell value of 1, and roads also have cell value of 1 (these will be modified later). Maui_Roads.rst ------ (BUFFER) ------> Maui_RoadBuf.rst   IMAGE Maui_Highways.rst ----- (BUFFER) ------> Maui_HwyBuf.rst These two images can be combined in map composition to show road transportation network of Maui.   IMAGE
Create traffic friction areas from landuse image so that urban areas have the greatest friction to travel because of city traffic congestion, and all other areas get much less traffic.  The landuse codes were:  (1) agricultural, (2) conservation, (3) rural, (4) urban agricultural    = little traffic and flat, friction value 3 conservation = windy mountain roads, friction value 6 rural              = moderate traffic, friction value 8 urban            = heaviest traffic, friction value 10 Maui_Landuse.rst -------- (ASSIGN) ---------> Maui_Traffic.rst                                    ( traffic_friction.avl )	traffic_friction.avl 1 3 2 6 3 8 4 10
Overlay the road image with landuse image and multiply all cells.  The road cells take the values of traffic friction (landuse) cells because road image has values 1 for roads and 0 for other areas.  The roads will have values from 5 to 10 corresponding to different traffic frictions (landuses). Maui_RoadBuf.rst (multiply) Maui_Traffic.rst ------ (OVERLAY) -----> Maui_RoadBuf_tr.rst The areas between roads can be considered as barriers to travel, and they need to have very large friction values.  Simply reclass the image so that values 0 to less than 1 (which means 0 values only in this integer value image) take value of 1000. Maui_RoadBuf_tr.rst ------ (RECLASS) ------> Maui_RoadTraffic.rst
Now, overlay the highways and roads into one image:  Maui_HwyBuf.rst (cover) Maui_RoadTraffic.rst ------ (OVERLAY) ------> Maui_TravelFriction.rst  IMAGE

Digitize airport location:

Digitize Kahului Airport location and create 500 m buffer around it to represent its size (ensures that it connects with roads) use Maui_RoadBuf.rst as basemap: digitize point feature and save as new point vector layer (e.g. Airport.vct) rasterize point vector layer (copy spatial parameters from existing raster image, e.g. MauiCoast.rst) (DIGITIZE) -----> Airport.vct ------ (POINTRAS) -----> Airport.rst ----- (BUFFER) ------> AirportBuf.rst

Calculate cost of travel image:

Calculate cost of travel from Kahului Airport along the road network (use cost push algorithm): surface feature image:  AirportBuf.rst friction surface image:  Maui_TravelFriction.rst output image:            Maui_CostOfTravel.rst

To show ocean and land as different colours, overlay two images where ocean pixels have value of 1 in covering image, and land pixels have value of 0 so the other image (cost of travel) shows through: MauiCoasts_inverted.rst (cover) Maui_CostOfTravel.rst ----- (OVERLAY) -------> Maui_CostOfTravel2.rst Create map composition for cost of travel from Kahului Airport    IMAGE using "cost-push" and "cost-grow" algorithms, and one for Makena city, to compare with the other map.

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