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Emphasized Opportunity, Risk, and Value: Updated Normalization and Symbology

The Vibrant Planet Platform has adopted a new, density-dependent formula for normalization of the relative scores of Opportunity (used for project selection), Risk, and Value. That is, we scale the scores based on the maximum value/risk/opportunity per acre within the planning area or landscape rather than scaling based on the raw values attributed to management units. The difference is subtle, as the ForSys algorithm does consider acreage 

and/or price constraint when building projects; it is an order of operations change.

The application has also adopted a new symbology for these scores, moving away from a coloring method that evenly divides the landscape-specific values into ten or twenty buckets, and instead uses a method that is reflective of relative magnitude across the western United States.

These changes allow Vibrant Planet to more accurately represent the geographic distribution of user-weighted scores (Opportunity, Risk, and Value). Sometimes in the old scheme, units with similar scores could have much darker or lighter colors if all unit values were similar. Now users can be assured that units within the same band of value/acre will receive the same coloration.

Users may notice a smaller visual “pop” for high value areas in the map or higher saturation when many objectives are given a high weight.

Before and After: Score Normalization

Background: Opportunity, Risk, and Value are unitless, relative scores that the Vibrant Planet Platform uses to measure relative value. Each objective within the system (Assets, Safety, Water, etc) is independently normalized to prevent objectives with more SARAs or more geographically prevalent SARAs from overwhelming other objectives. For example, a collaborative might focus Biodiversity SARAs on rare species, which are by definition sparsely populated across the landscape, but the Wildland Health objective contains Aboveground Biomass, which is widely distributed across landscapes. Were the objectives to remain in a raw format, Wildland Health would overwhelm Biodiversity in this case, as there is much more occurrence data. Thus each objective is given the same ceiling and same floor.

Previously: Objectives were either normalized across a collaborative’s landscape or at the planning area scale, based on the user’s selection. Regardless of the normalization scope, raw RO, Risk, and Value scores were normalized on a per management unit basis, even though management units can vary dramatically in size. When sent to the ForSys project prioritization algorithm, which takes acres per project or $ per project as a parameter, RO per unit acre could effectively play a role in management unit selection in projects, directly when acres per project was used as the constraint and somewhat indirectly when $ per project was used as the constraint (because cost of treatment per unit is tied to management unit acreage). 

Now: Score normalization has a new added element, which is density. Rather than normalizing on management unit raw scores, we now normalize based on per-acre scores. We believe that this meets an inherent intuition about the data and planning process, which is that areas with the highest density of value/risk/opportunity are the most efficient areas to work in. In practice, density-based normalization changes have resulted in very minor changes in relative scoring, although the unitless score figure does change significantly. This change is chiefly useful to us because it allows us to visualize the data in a standardized way.

Before and After: Score Visualization

Previously: The previous methodology for symbolizing Opportunity, Risk, and Value scores in the Vibrant Planet Platform map was a rank-based decile scheme, meaning that all management units would be equally divided into 10 different color categories based on rank. One benefit of this approach is that there are always darker areas in the map choropleth, and it emphasizes patterns. A disadvantage however, is that if the distribution of values has a small range the choropleth creates a false sense of divergence when values are actually quite close. In this old method, if one were to select a single objective and set it to a weight of “1” or to a weight of “5”, there would be no difference in the color pattern.

Now: The new symbology uses a standardized bucketing scheme (each bucket has the same range of normalized values) to assign colors across the map. This is possible due to the new density-dependent normalization scheme; the highest value- density management unit visualized should be in the “darkest” bucket from the start. In practice, this means that weighting a single objective “1” versus “5” will result in a darkening color pattern as the weight increases. Typically, we see areas of greater homogeneity in this new scheme, which more accurately represents that the value and opportunity of those areas is close.