Geographically Based Hydrogen Consumer Demand and Infrastructure Analysis: Final Report Page: 8 of 35
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Internal Dataset Ranking-There is no single best data classification method; each has advantages and
disadvantages depending on the nature of the data and the type of information and analysis desired. In
general, a classification method should maximize the between-class differences and minimize the within-
The natural breaks classification was chosen for this study. This method identifies break points by
looking for groupings and patterns inherent in the data. ArcGIS uses a complex statistical formula (Jenk's
Optimization) to identify break points by choosing the class breaks that best group similar value and
maximize the differences between classes. The features are divided into classes with boundaries set where
there are relatively big jumps in the data values. The major disadvantage is that the concept behind the
classification may not be easily understood by all map users, and the legend values for the class breaks
may not be intuitive. The advantage, however, is that it is one of the best ways to classify data that model
natural human behaviors and patterns. The natural break method best applies to hydrogen vehicle demand
because hydrogen vehicle demand patterns are not uniform by nature.
Using the natural break classification method, we created seven classes within each data layer. The
selection of seven groups was chosen because of the depth of analysis and the refinement of results it
would provide. Then, we employed a ranking system of 1 to 7 to rate the values within each class used in
the hydrogen demand model. A class was ranked 1 if its values had a "very low" influence on the chosen
strategy (e.g., people with the lowest income would generate the lowest hydrogen vehicle demand). A
class was ranked 7 if its values had a "very high" influence (e.g., people with the highest income would
generate the highest hydrogen vehicle demand).
Attribute Descriptions, Rankings, and Weightings-Based on the transportation experts' valuing of
the attributes (Table 1), attributes were weighted in relation to each other in ArcGIS in terms of low,
medium, or high impact on hydrogen vehicle adoption. These are normalized so the weightings of all the
attributes are equal to 100%. The following section describes each attribute as well as the attribute
classification, ranking, and weighting for the consumer strategy baseline scenario. The results of this
scenario are described below.
* Households with Two or More Vehicles
o Data origin: 2000 U.S. Census
o Data representation: number of households that have two or more vehicles
o Rationale: Initial customers for hydrogen vehicles will be those in households that have at
least two vehicles because of limited hydrogen range and refueling opportunities. The NREL
focus group considered this to be the most important factor in predicting hydrogen vehicle
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Melendez, M. & Milbrandt, A. Geographically Based Hydrogen Consumer Demand and Infrastructure Analysis: Final Report, report, October 1, 2006; Golden, Colorado. (https://digital.library.unt.edu/ark:/67531/metadc887690/m1/8/?rotate=270: accessed May 19, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.