Introduction
Someone once asked me, “When someone asks you to look at their model and make suggestions, what do you look for?” I thought this might be a good topic for a series of blog posts on modeling best practices to make QA easier.
The first thing I ask a client is what prompted the request? Is the model running too slow? Does it take a long time to solve? Does the solver struggle with feasibility? In most cases, the usual answer is, “it takes a long time to generate and solve.” That starts me thinking about efficiency in terms of development types and decision variables. Cardinal rule #1: avoid code that is difficult to understand or hides meaning.
Flawed Characterization
Here is a recent example that I came across. The primary data set is a shapefile where stand boundaries, harvest system, management intensity, and road buffers have been intersected. The resulting polygons had Woodstock theme fields populated according to the LANDSCAPE section attributes.
Stands
Harvest System
Management Intensity
Roads
4-Layer GIS Intersection
Let’s say that Theme 1 corresponds to management intensity (1 or 2), Theme 2 corresponds to stands (Y or N), Theme 3 corresponds to harvest system (C or G), Theme 4 is rotation (NA or RG), Theme 5 is thinning (NA or thin age) and Theme 6 is StandID. Here are the resulting AREAS section records:
;*A Th1 Th2 Th3 Th4 Th5 Th6 Age Area*A 1 Y G NA NA 00000 1 51.238 ; 2 polygons*A 1 Y C NA NA 00000 1 8.006 ; 1 polygons*A 1 Y G NA NA 00000 1 46.339 ; 1 polygons*A 1 Y G NA NA 83901 17 11.346 ; 1 polygons*A 1 Y C NA NA 83901 17 4.766 ; 1 polygons*A 1 N G NA NA 83901 17 4.977 ; 19 polygons*A 1 Y C NA NA 83902 14 53.185 ; 2 polygons*A 1 Y G NA NA 83902 14 13.172 ; 1 polygons*A 1 Y C NA NA 83904 18 48.816 ; 1 polygons*A 1 Y G NA NA 83904 18 6.009 ; 2 polygons*A 1 N G NA NA 83904 18 7.428 ; 19 polygons*A 2 Y G NA NA 83905 17 3.222 ; 2 polygons*A 1 Y C NA NA 83906 17 5.124 ; 1 polygons*A 1 Y G NA NA 83906 17 10.972 ; 1 polygons*A 1 Y C NA NA 83907 9 8.334 ; 2 polygons*A 1 Y G NA NA 83907 9 20.993 ; 1 polygons*A 1 Y C NA NA 83908 13 47.249 ; 1 polygons*A 1 Y C NA NA 83909 18 18.29 ; 1 polygons*A 1 Y G NA NA 83909 18 18.805 ; 2 polygons*A 1 Y C NA NA 83910 18 7.695 ; 1 polygons*A 1 Y C NA NA 83914 14 6.881 ; 2 polygons*A 1 Y G NA NA 83914 14 38.483 ; 1 polygons*A 1 Y G NA NA 83914 14 5.538 ; 1 polygons*A 1 Y C NA NA 83914 14 17.36 ; 2 polygons*A 1 Y G NA NA 83914 14 24.605 ; 2 polygons*A 2 N G NA NA 83914 14 1.087 ; 1 polygons*A 1 Y G NA NA 83916 16 7.076 ; 1 polygons*A 2 N G NA NA 83916 16 1.86 ; 1 polygons*A 1 Y G NA NA 83917 22 31.772 ; 1 polygons*A 2 Y C NA NA 83918 16 5.214 ; 1 polygons*A 2 Y C NA NA 83919 18 6.393 ; 1 polygons*A 2 Y G NA NA 83919 18 17.887 ; 1 polygons*A 1 N G NA NA 83919 18 4.168 ; 8 polygons*A 1 Y G NA NA 84001 16 10.713 ; 2 polygons
Did you spot the problem yet? Look at the records with the most polygons. What do you notice about them? They are the ones with Theme2 = N, and they are a minority of the area. Based on the map examples, one can only conclude that Theme2 = N corresponds to roads. So, why is this a problem?
Redundant Development Types
Let us assume Theme2 = N in a polygon represents a road segment. Presumably, all harvest and silvicultural actions require Theme2 = Y, so there is no possibility of harvesting a road. However, Woodstock still has to maintain these road records as distinct development types. If you have a lot of these, it can create significant overhead because Woodstock must track all these different flavors of road. Moreover, if you are not careful, they can contribute to your inventory outputs because they might be linked to active yield tables (if yields are indexed just on the last 3 themes).
Chances are, roads are only included in the model to contribute to road maintenance costs, or fixed costs like property taxes or overhead. Since they don’t grow or have volumes, they don’t need to be differentiated by age or stand ID. A better approach would be to assign a dummy stand ID and a common age of one. This results in the following AREAS section, with all of the road segments collapsed into a single development type:
This improved formulation avoids the possibility of _DEATH, and a default NULL yield table where every yield component equals 0 explicitly forces roads to contribute nothing to harvest volumes, etc. If your characterization has forest type or species, include ‘RD’ or ‘NF” to the list of attributes. To make it even more obvious, you could add the ‘0’ attribute to Theme 1 (no management).
;*A Th1 Th2 Th3 Th4 Th5 Th6 Age Area*A 1 Y G NA NA 00000 1 51.238 ; 2 polygons*A 1 Y C NA NA 00000 1 8.006 ; 1 polygons*A 1 Y G NA NA 00000 1 46.339 ; 1 polygons*A 1 Y G NA NA 83901 17 11.346 ; 1 polygons*A 1 Y C NA NA 83901 17 4.766 ; 1 polygons*A 1 Y C NA NA 83902 14 53.185 ; 2 polygons*A 1 Y G NA NA 83902 14 13.172 ; 1 polygons*A 1 Y C NA NA 83904 18 48.816 ; 1 polygons*A 1 Y G NA NA 83904 18 6.009 ; 2 polygons*A 2 Y G NA NA 83905 17 3.222 ; 2 polygons*A 1 Y C NA NA 83906 17 5.124 ; 1 polygons*A 1 Y G NA NA 83906 17 10.972 ; 1 polygons*A 1 Y C NA NA 83907 9 8.334 ; 2 polygons*A 1 Y G NA NA 83907 9 20.993 ; 1 polygons*A 1 Y C NA NA 83908 13 47.249 ; 1 polygons*A 1 Y C NA NA 83909 18 18.29 ; 1 polygons*A 1 Y G NA NA 83909 18 18.805 ; 2 polygons*A 1 Y C NA NA 83910 18 7.695 ; 1 polygons*A 1 Y C NA NA 83914 14 6.881 ; 2 polygons*A 1 Y G NA NA 83914 14 38.483 ; 1 polygons*A 1 Y G NA NA 83914 14 5.538 ; 1 polygons*A 1 Y C NA NA 83914 14 17.36 ; 2 polygons*A 1 Y G NA NA 83914 14 24.605 ; 2 polygons*A 1 Y G NA NA 83916 16 7.076 ; 1 polygons*A 1 Y G NA NA 83917 22 31.772 ; 1 polygons*A 2 Y C NA NA 83918 16 5.214 ; 1 polygons*A 2 Y C NA NA 83919 18 6.393 ; 1 polygons*A 2 Y G NA NA 83919 18 17.887 ; 1 polygons*A 1 Y G NA NA 84001 16 10.713 ; 2 polygons*A 0 N G NA NA NULL 1 19.52 ; 48 polygons
Modeling best practices demand that your models be as lightweight and transparent as possible. Even if your actions and outputs do not include them, having a multitude of development type classes representing a non-forest type creates model overhead and slows performance. Don’t trip yourself up with non-forest areas in disguise!
Bloated Models Weighing You Down? Contact Me!
If you’d like a new set of eyes to look over your model(s), give me a shout to schedule a model audit or an on-site training review where we will use your own models rather than canned examples.
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